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REVIEW 4 major objections 5 minor 39 references

Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A camera-only drone fleet can find animals, agree on their identities, and assign each drone a target without GPS or centralized control.

desk verdict A serious integration with a genuinely new Box-ICP registration, but the ring-scalability claim rests on a synthetic single-scene success rate and a two-drone qualitative demo. read the letter →

arxiv 2508.15038 v1 pith:WREAPVQK submitted 2025-08-20 cs.RO cs.AIcs.CVcs.MA

Makram Chahine , William Yang , Alaa Maalouf , Justin Siriska , Ninad Jadhav , Daniel Vogt , Stephanie Gil , Robert Wood
show 1 more author
Daniela Rus
This is my paper · ORCID
classification cs.ROcs.AIcs.CVcs.MA
keywords decentralizedroboticswildlifemonitoringmulti-quadrotorsystemsvisualdetectionbounding-boxregistrationgraphneuralnetworksgoalassignmentvision-basednavigation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to show that a fleet of small camera-equipped quadrotors can monitor wildlife at the level of individual animals without a central controller, GPS, or heavy communication. The proposed pipeline runs six stages—scouting, detection, rendezvous, registration, goal assignment, and execution—and the two technical pillars are Box-ICP, which aligns detected bounding boxes between neighboring drones to agree on which animal is which, and a graph neural network that assigns each drone a different animal to follow. The authors validate the pieces with offline detection and registration tests, GNN assignment experiments, real-time tests with three simulated viewpoints, and an end-to-end flight with two drones over six whale posters. If the system works as claimed, field scientists could deploy swarms that gather individual-level behavioral data in parallel over large areas with minimal equipment.

What carries the argument

The two load-bearing mechanisms are Box-ICP and the decentralized GNN goal assigner. Box-ICP aligns two agents' sets of bounding boxes by treating each box's edges as a small point cloud, solving a linear sum assignment to pair edges, then a second linear sum assignment to pair boxes, then a least-squares rigid transform, iterating until convergence—this is what turns independent detections into shared identities. The goal assigner is a graph neural network that runs on the ring graph, with agents as nodes and candidate goals as options; it was trained on randomly sampled layouts labeled by the Hungarian algorithm, and its loss treats duplicate assignments as errors, so it can allocate even

What would settle it

Fly three or more drones using the published ring protocol over six marked animal-sized targets; log every identity label at every drone and compare. The claim fails if the fraction of runs with full identity consensus is worse than the reported per-pair rate raised to the ring size, or if assignment optimality on real detections is substantially below the unit-square GNN numbers.

Watch

Extended reading notes

Core claim

The central claim is that individual-level wildlife monitoring can be fully decentralized and vision-only: each drone carries one downward RGB camera, communicates only with two ring neighbors, and still reaches consensus on animal identities and near-optimal goal assignments. In the authors' account, Box-ICP solves identity registration by matching bounding-box edges through two linear sum assignment steps and a least-squares transform, achieving perfect pairwise matches on 200 synthesized frame pairs from a 9-whale scene. The GNN-based assignment reaches 92.2% optimality with 5 agents and 10 goals and keeps assignments mostly non-overlapping even when goals outnumber agents. These modules,

Load-bearing premise

The load-bearing premise is that the goal-assignment network trained on randomly sampled layouts in a unit square will still make near-optimal choices on real, noisy bounding boxes, and that identity-registration errors compound only as the per-pair success rate raised to the number of drones in the ring.

Editorial extensions

If this is right

  • A future field deployment could send a swarm of camera-only quadrotors over a whale group and get back individual-level tracks, not just group counts, with no central base station deciding assignments.
  • Each drone only needs to talk to its two ring neighbors, so communication scales with fleet size instead of a central bottleneck.
  • The system works when there are more animals than drones, because the assignment loss penalizes duplicates rather than requiring every goal to be filled.
  • The full loop was run end-to-end on hardware, meaning scouting, detection, rendezvous, registration, assignment, and tracking are all integrated in one closed system.
  • Identity reliability in an n-drone ring is explicitly modeled as per-pair accuracy to the nth power, so the designers know exactly how much per-pair accuracy the deployment must achieve.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The end-to-end validation used printed whale posters on a soccer field, not live animals; the authors' field footage is used only to train and test the detector. So 'reliable deployment in diverse field conditions' is best read, for now, as a poster-scale proof of the coordination loop.
  • The reported identity-success formula s(n) = s_p^n means a small drop in per-pair registration accuracy becomes a large drop for fleets of four or more drones; a natural stress test is to run the ring with 4-6 real drones and measure whether consensus survives.
  • Because the GNN was evaluated at the cost-matrix abstraction level, feeding it raw detections with false positives and missed animals is the immediate next experiment; training it on detection-level inputs or fine-tuning on real layouts could change the numbers.
  • If the system does scale, the roughly 300-byte message format would let a swarm operate over long-range, low-bandwidth radio links, turning many cheap platforms into a distributed individual-level behavior sensor.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper presents a decentralized, vision-only multi-quadrotor pipeline for individual-level wildlife monitoring. The six-stage pipeline consists of scouting, YOLO-based detection, formation rendezvous, a newly proposed Box-ICP algorithm for pairwise identity registration over a ring communication topology, GNN-based decentralized goal assignment adapted from DGNN-GA, and execution via mask tracking. The authors report offline detection performance, synthetic pairwise registration accuracy, large-scale GNN assignment benchmarks on Euclidean cost matrices, and a two-drone real-world deployment over six whale posters. The central claim is that the integrated system achieves robust decentralized identity consensus and near-optimal task allocation without GPS or centralized communication, and is 'ready for real-world in situ testing.'

Significance. If the central claims were fully supported, the paper would contribute a useful systems integration: it combines low-bandwidth visual registration and learned decentralized assignment in a way that could scale to parallel monitoring of large species. The explicit communication budget (Section 5) and the large-scale GNN evaluation over 5000 random initializations are strengths, as is the held-out detection validation. However, the quantitative evidence for the load-bearing components is substantially weaker: registration is validated only on synthetic affine warps of one scene, the GNN is never connected to real visual cost matrices, and the end-to-end real-world test is qualitative and limited to two agents. These gaps currently limit the significance to a proof-of-concept rather than a validated field-ready system.

major comments (4)
  1. [Section 5, Detection and Registration] The pairwise success rate is reported as 100% using 200 pairs drawn from 39 synthetic frames generated by affine warps of a single 9-whale scene. Because the paper itself defines ring-wide identity consensus as s(n)=s_p^n with s_p=s_det^2*s_reg, the entire scalability claim rests on this synthetic s_reg=1.0. Field conditions (viewpoint change, occlusion, motion blur, detector misses/false positives) will lower s_p, and the exponential form amplifies any drop: even s_p=0.97 gives s(8)=0.78. The two-agent full-pipeline experiment (Section 4) cannot exercise this failure mode. Please provide a realistic multi-view estimate of s_p from real data, or substantially qualify the scalability and 'ready for in situ testing' claims.
  2. [Section 5, Table 2] For the balanced case ng=5, the reported optimality is 64.1% and diversity is 34.3%. The paper handles ng<10 by adding ghost goals, so the low diversity indicates that agents frequently select the same real or ghost goals in exactly the regime where agent and goal counts are similar — a central scenario for wildlife monitoring. The text says 'We achieve both highly non-overlapping and close-to-optimal assignments while the number of goals remains close to the training setup (until ng=7)', which is contradicted by the ng=5 row. Please report duplicate-assignment rates and/or coverage of real goals, and either retrain the GNN for balanced cases or restrict the claim.
  3. [Section 4/5, Full Pipeline] The end-to-end claim in the abstract is supported by a single qualitative two-drone experiment over six posters, with no reported success metrics, no number of repeated runs, and no identity-consensus or assignment-correctness numbers. The wording 'validated through real-world experiments' and 'reliable deployment in diverse field conditions' is not supported. At minimum, report quantitative metrics (e.g., detection F1, registration accuracy, assignment optimality) over multiple runs and, ideally, more than two agents.
  4. [Section 3/4, Goal Assignment] The GNN is trained on uniformly sampled Euclidean positions on a unit square and evaluated on the same abstraction with costs defined by Euclidean distance. The actual pipeline feeds Box-ICP-aligned bounding boxes, whose cost structure differs from the training distribution. The paper states that the GNN is 'a high-level decision based solely on the cost matrix (pixel distance)' and therefore evaluated independent of flight data, but this means the near-optimality shown in Table 2 is not evidence for the full pipeline. The optimality metric is also computed against the same Hungarian algorithm used to generate labels, which is a valid approximation benchmark but not a demonstration of generalization. Please add an experiment where GNN inputs are derived from real detections/registrations, or explicitly mark the goal-assignment results as an idealized abstraction.
minor comments (5)
  1. [Section 5, Figure 3] The detection success rate results are not reported numerically; specify the confidence thresholds and exact success rates for each data point.
  2. [Section 5, Communication Bandwidth] '1MHz' should be '1 Mbit/s' or define the bandwidth metric; the latency calculation omits packet overhead, hidden-state synchronization, and wireless contention.
  3. [Algorithm 1] The notation for the inner LSA on edge-distance matrix D and the outer LSA on C is hard to follow; a short example or dimension annotations would help.
  4. [Section 3, Loss Equations] The assignment validity loss equation appears garbled; the second term is missing indices and the expectation operator E[] in LCE is not defined.
  5. [Abstract and Section 5] The phrase 'reliable deployment in diverse field conditions' overstates the evidence, which comes from a soccer field with posters. Please temper the wording to match the experimental scope.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: core components are validated on held-out data; the GNN training/evaluation loop is a standard supervised benchmark rather than a self-referential reduction.

full rationale

The paper's central claims are supported by independent experiments. Detection is evaluated on a held-out 10% split and on real-world flights (Section 4, 'Scouting and Detection'; Table 1). Box-ICP registration is tested on 200 pairs of frames from a 39-frame synthetic set derived from aerial footage not in the training set (Section 4, 'Detection and Registration'; Section 5). The GNN goal-assignment module is trained on labels produced by the centralized Hungarian algorithm and then evaluated on 'large-scale runs with randomized agent and goal initializations' (Section 4, 'Goal Assignment'); this is a conventional supervised approximation benchmark, not a circular reduction, because the test graphs are freshly sampled and the metric—cost proximity to the Hungarian solution—is an external standard, not the training loss itself. The exponential consensus model s(n)=s_p^n with s_p=s_det^2 * s_reg (Section 5) is an explicitly stated independence assumption; it is unsupported and a scalability risk, but the pairwise quantities are measured independently of the ring-level outcome, so it is not circular. The full two-drone pipeline deployment (Section 4, 'Full Pipeline in the Real World') lacks reported quantitative success metrics; this is missing evidence, not circularity. No load-bearing self-citation chain is evident: the cited GNN framework [26] is a component that is re-evaluated here, and the paper does not rely on a self-citation to forbid alternatives or to define its success metric. Therefore the derivation chain is self-contained and no circular step can be exhibited.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical or conceptual entities. The free parameters are hyperparameters and thresholds, none fitted to force a desired outcome in a circular way, but several are unspecified. The key unproven assumptions are the transfer of the GNN from synthetic Euclidean cost matrices to real visual data and the multiplicative independence of registration errors across a ring, both load-bearing for the claimed scalability.

free parameters (6)
  • Loss weight alpha = 0.5
    Chosen by hand to balance structural and supervised losses in the GNN training (Section 3, Final Loss).
  • Scout detection threshold = 80% of frames with detections in a buffer
    Heuristic threshold for robust whale presence signal (Section 3, Scouting).
  • Ghost goal cost = high-cost, unspecified
    Introduced to handle ng < 10 in goal assignment evaluation; the actual value is not reported (Section 5, Goal Assignment).
  • Message passing rounds = 5
    Hyperparameter for the GNN, chosen by the authors (Section 5, Goal Assignment).
  • Hidden state dimension dh = 32
    Hyperparameter for the GNN hidden state, chosen by the authors (Section 5, Communication Bandwidth).
  • Detection confidence thresholds = Three thresholds, values not specified
    Used in Figure 3 detection success rate evaluation; thresholds are not listed.
assumptions (4)
  • domain assumption Ring communication topology with each drone communicating only with its two neighbors
    Assumed for scalability and decentralization; stated in Section 3: 'we restrict inter-agent communication to a ring topology'.
  • domain assumption Downward-facing cameras with all individuals contained in the images, ensured by formation and altitude control
    Stated in Section 3: 'We assume all drones have downward-facing cameras, with all individuals contained in the images, which can be ensured by control of the swarm's formation and altitude.'
  • ad hoc to paper GNN trained on Euclidean distance cost matrices generalizes to real visual cost functions
    The GNN is trained and evaluated on synthetic Euclidean costs (Section 4, Goal Assignment), yet is deployed with costs derived from bounding-box detections; no evidence of transfer is provided.
  • domain assumption Pairwise registration success rates multiply independently across the ring, s(n) = s_p^n
    Stated in Section 5 Results without derivation or empirical validation, used to argue scalability.

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Cite this review

Pith. "Pith review of Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring." pith.science (2026). https://pith.science/paper/WREAPVQK

@misc{pith2026250815038,
  author       = {Pith},
  title        = {Pith review of: Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WREAPVQK}},
  note         = {Machine review of arXiv:2508.15038}
}
read the original abstract

Wildlife field operations demand efficient parallel deployment methods to identify and interact with specific individuals, enabling simultaneous collective behavioral analysis, and health and safety interventions. Previous robotics solutions approach the problem from the herd perspective, or are manually operated and limited in scale. We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring that is scalable, low-bandwidth, and sensor-minimal (single onboard RGB camera). Our approach enables robust identification and tracking of large species in their natural habitat. We develop novel vision-based coordination and tracking algorithms designed for dynamic, unstructured environments without reliance on centralized communication or control. We validate our system through real-world experiments, demonstrating reliable deployment in diverse field conditions.

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Reference graph

Works this paper leans on

39 extracted references · 29 canonical work pages

  1. [1]

    S. Ren, K. He, R. Girshick, J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks (2016)

    work page 2016

  2. [2]

    W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu, A.C. Berg, SSD: Single Shot MultiBox Detector (Springer International Publishing, 2016), p. 21–37. doi:10.1007/978-3-319-46448-0_2

    doi:10.1007/978-3-319-46448-0_2 2016

  3. [3]

    Redmon, S

    J. Redmon, S. Divvala, R. Girshick, A. Farhadi. You only look once: Unified, real-time object detection (2016)

    work page 2016

  4. [4]

    Carion, F

    N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, S. Zagoruyko. End-to-end object detection with transformers (2020)

    work page 2020

  5. [5]

    D. Reis, J. Kupec, J. Hong, A. Daoudi. Real-time flying object detection with yolov8 (2024)

    work page 2024

  6. [6]

    Y. Tian, Q. Ye, D. Doermann. Yolov12: Attention-centric real-time object detectors (2025)

    work page 2025

  7. [7]

    Pomerleau, Alvinn: An autonomous land vehicle in a neural network, Advances in neural information processing systems 1 (1988)

    D.A. Pomerleau, Alvinn: An autonomous land vehicle in a neural network, Advances in neural information processing systems 1 (1988)

    work page 1988

  8. [8]

    Bojarski, D

    M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L.D. Jackel, M. Monfort, U. Muller, J. Zhang, et al., End to end learning for self-driving cars, arXiv preprint arXiv:1604.07316 (2016)

    arXiv 2016

Show all 39 references
  1. [9]

    P.S. Chib, P. Singh, Recent advancements in end-to-end autonomous driving using deep learning: A survey, IEEE Transactions on Intelligent Vehicles (2023)

    2023

  2. [10]

    Xiao, T.H

    W. Xiao, T.H. Wang, R. Hasani, M. Chahine, A. Amini, X. Li, D. Rus, Barriernet: Differentiable control barrier functions for learning of safe robot control, IEEE Transactions on Robotics (2023)

    2023

  3. [11]

    Chahine, R

    M. Chahine, R. Hasani, P. Kao, A. Ray, R. Shubert, M. Lechner, A. Amini, D. Rus, Robust flight navigation out of distribution with liquid neural networks, Science Robotics 8(77), eadc8892 (2023)

    2023

  4. [12]

    Quach, M

    A. Quach, M. Chahine, A. Amini, R. Hasani, D. Rus. Gaussian splatting to real world flight navigation transfer with liquid networks (2024). ://arxiv.org/abs/2406.15149

    2024 arXiv

  5. [13]

    T.H. Wang, W. Xiao, M. Chahine, A. Amini, R. Hasani, D. Rus, in Learning for Dynamics and Control Conference (PMLR, 2023), pp. 1099--1111

    2023

  6. [15]

    Kaufmann, L

    E. Kaufmann, L. Bauersfeld, A. Loquercio, M. Mueller, V. Koltun, D. Scaramuzza, Champion-level drone racing using deep reinforcement learning, Nature 620, 982 (2023). doi:10.1038/s41586-023-06419-4

    2023 doi

  7. [16]

    Chahine, A

    M. Chahine, A. Quach, A. Maalouf, T.H. Wang, D. Rus. Flex: End-to-end text-instructed visual navigation with foundation models (2024)

    2024

  8. [17]

    Maalouf, N

    A. Maalouf, N. Jadhav, K.M. Jatavallabhula, M. Chahine, D.M. Vogt, R.J. Wood, A. Torralba, D. Rus, Follow anything: Open-set detection, tracking, and following in real-time, IEEE Robotics and Automation Letters 9(4), 3283 (2024). doi:10.1109/LRA.2024.3366013

    2024

  9. [18]

    Kuhn, The hungarian method for the assignment problem, Naval Research Logistics Quarterly 2(1-2), 83 (1955)

    H.W. Kuhn, The hungarian method for the assignment problem, Naval Research Logistics Quarterly 2(1-2), 83 (1955). doi:https://doi.org/10.1002/nav.3800020109

    1955 doi

  10. [19]

    H. Aziz, A. Pal, A. Pourmiri, F. Ramezani, B. Sims. Task allocation using a team of robots (2022)

    2022

  11. [20]

    Panagou, M

    D. Panagou, M. Turpin, V. Kumar, in 2014 IEEE International Conference on Robotics and Automation (ICRA) (2014), pp. 6757--6762. doi:10.1109/ICRA.2014.6907857

    2014

  12. [21]

    Ismail, L

    S. Ismail, L. Sun, in 2017 International Conference on Unmanned Aircraft Systems (ICUAS) (2017), pp. 23--28. doi:10.1109/ICUAS.2017.7991447

    2017

  13. [22]

    Chopra, G

    S. Chopra, G. Notarstefano, M. Rice, M. Egerstedt, A distributed version of the hungarian method for multirobot assignment, IEEE Transactions on Robotics 33(4), 932 (2017). doi:10.1109/TRO.2017.2693377

    2017

  14. [23]

    M. Lee, Y. Xiong, G. Yu, G.Y. Li, Deep neural networks for linear sum assignment problems, IEEE Wireless Communications Letters 7(6), 962 (2018). doi:10.1109/LWC.2018.2843359

    2018

  15. [24]

    H. Liu, T. Wang, C. Lang, S. Feng, Y. Jin, Y. Li. Glan: A graph-based linear assignment network (2022)

    2022

  16. [25]

    Aironi, S

    C. Aironi, S. Cornell, S. Squartini, in Applied Intelligence and Informatics, ed. by M. Mahmud, C. Ieracitano, M.S. Kaiser, N. Mammone, F.C. Morabito (Springer Nature Switzerland, Cham, 2022), pp. 90--101

    2022

  17. [26]

    Goarin, G

    M. Goarin, G. Loianno, Graph neural network for decentralized multi-robot goal assignment, IEEE Robotics and Automation Letters 9(5), 4051 (2024). doi:10.1109/LRA.2024.3371254

    2024

  18. [27]

    Y. Chen, G. Medioni, Object modelling by registration of multiple range images, Image and vision computing 10(3), 145 (1992)

    1992

  19. [28]

    Joshi, C.H

    A. Joshi, C.H. Lee, On the problem of correspondence in range data and some inelastic uses for elastic nets, IEEE transactions on neural networks 6(3), 716 (1995)

    1995

  20. [29]

    Myronenko, X

    A. Myronenko, X. Song, Point set registration: Coherent point drift, IEEE transactions on pattern analysis and machine intelligence 32(12), 2262 (2010)

    2010

  21. [30]

    Wang, J.M

    Y. Wang, J.M. Solomon, in Proceedings of the IEEE International Conference on Computer Vision (2019), pp. 3523--3532

    2019

  22. [31]

    Agarwal, J.M

    P.K. Agarwal, J.M. Phillips, in Proceedings of the Eighteenth Canadian Conference on Computational Geometry’, Kingston, Canada (2006), pp. 143--146

    2006

  23. [32]

    Haddad, M

    W.M. Haddad, M. Chahine, A hybrid thermodynamic control protocol for semistability and consensus of network systems with intermittent information, IEEE Transactions on Automatic Control 66(6), 2754 (2021). doi:10.1109/TAC.2020.3008301

    2021

  24. [33]

    Leung, O

    S. Leung, O. Montenbruck, Real-time navigation of formation-flying spacecraft using global-positioning-system measurements, Journal of Guidance, Control, and Dynamics 28(2), 226 (2005). doi:10.2514/1.7474

    2005 doi

  25. [34]

    Chahine, R

    M. Chahine, R. Firoozi, W. Xiao, M. Schwager, D. Rus, in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2023), pp. 880--887. doi:10.1109/IROS55552.2023.10341677

    2023

  26. [35]

    Chahine, R

    M. Chahine, R. Firoozi, W. Xiao, M. Schwager, D. Rus, Intention communication and hypothesis likelihood in game-theoretic motion planning, IEEE Robotics and Automation Letters 8(3), 1223 (2023). doi:10.1109/LRA.2023.3238131

    2023

  27. [36]

    Jadhav, S

    N. Jadhav, S. Bhattacharya, D. Vogt, Y. Aluma, P. Tønnesen, A. Prabhakara, S. Kumar, S. Gero, R.J. Wood, S. Gil, Reinforcement learning–based framework for whale rendezvous via autonomous sensing robots, Science Robotics 9(95), eadn7299 (2024). doi:10.1126/scirobotics.adn7299

    2024 doi

  28. [37]

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    ENTRY address archive author booktitle chapter doi edition editor eid eprint howpublished institution journal key month note number organization pages publisher school series title type url volume year label extra.label sort.label short.list INTEGERS output.state before.all mi...

  29. [38]

    write newline

    " write newline "" before.all 'output.state := FUNCTION add.period duplicate empty 'skip "." * add.blank if FUNCTION if.digit duplicate "0" = swap duplicate "1" = swap duplicate "2" = swap duplicate "3" = swap duplicate "4" = swap duplicate "5" = swap duplicate "6" = swap dupl...

  30. [39]

    , " * write output.state after.block = add.period write newline

    ENTRY address author booktitle chapter doi edition editor eid howpublished institution journal key month note number organization pages publisher school series title type url volume year label INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION in...

  31. [40]

    write newline

    " write newline "" before.all 'output.state := FUNCTION if.digit duplicate "0" = swap duplicate "1" = swap duplicate "2" = swap duplicate "3" = swap duplicate "4" = swap duplicate "5" = swap duplicate "6" = swap duplicate "7" = swap duplicate "8" = swap "9" = or or or or or or...

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Reviewed August 5, 2026 · model on record in the stance chip above.