Pith. sign in

REVIEW 4 major objections 6 minor 28 references

Mitigating Detector Ageing Effects with Graph-Based Multi-Modal Track Reconstruction at Belle II

T0 review · 4 major / 6 minor · reviewed 2026-08-01 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Retraining a graph neural network on aged-detector data keeps Belle II track finding at 96% purity, where the staged baseline loses a quarter of its efficiency.

desk verdict A solid, clearly-written proceedings paper with a concrete empirical benchmark: retraining a GNN track finder on one simulated CDC ageing map clearly beats the staged Belle II baseline, but the abstract's 28% number does not match the table and the test is a single map used for both fine-tuning and evaluation. read the letter →

arxiv 2607.15361 v1 pith:ZB4FHEEJ submitted 2026-07-16 hep-ex

Lea Reuter , Tristan Brandes , Giacomo De Pietro , Torben Ferber This is my paper · ORCID
classification hep-ex
keywords trackreconstructiongraphneuralnetworksdetectorageingBelleIIcentraldriftchamberobjectcondensationdomainshiftdisplacedvertices
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

Belle II's central drift chamber (CDC) is slowly wearing out under high-luminosity running: wires lose gain, readout boards die, and gaps open between the silicon tracker and the first working wires. Standard tracking, which stitches hits in geometric order, sheds efficiency abruptly as these gaps grow. This paper argues that ageing is best treated as a domain shift in hit patterns, and that a graph neural network (the BAT Finder) that clusters all SVD+CDC hits in one learned space can absorb the shift by retraining. On a realistic degraded-detector simulation, the retrained network limits the track-efficiency loss for displaced muons to about 14% (versus roughly 25% for the staged baseline in the table; the abstract quotes 28%) and keeps track purity at 96%, compared with 90% for the baseline. If true, Belle II can keep reconstructing tracks reliably as the detector degrades, just by updating the network.

What carries the argument

The carrying object is the BAT Finder: a graph neural network that takes all CDC and SVD hits as unordered nodes, selects neighbours via GravNet in a learned latent space, and uses an object-condensation loss to cluster hits belonging to the same particle while rejecting noise. It predicts per-hit cluster coordinates, a condensation score, and initial track parameters, with no explicit use of detector geometry, layer ordering, or hit continuity. The adaptation mechanism is data-driven fine-tuning: a previously trained model is retrained for about 100 epochs on degraded-detector samples, including wire inefficiencies parametrized by Eq. (1) with C=0.35, a disabled first superlayer, and 14 dis

What would settle it

Run the same retrained BAT Finder on simulated data with a different ageing map (e.g., C=0.5 or different board locations) or on real Belle II runs with known dead regions: if its relative efficiency loss versus nominal substantially exceeds 14%, or purity falls to baseline levels, the domain-shift robustness claim is contradicted.

Watch

Extended reading notes

Core claim

Under realistic CDC ageing—reduced wire efficiencies, a disabled inner superlayer, and 14 dead readout boards—the paper's GNN-based BAT Finder, which treats every SVD and CDC hit as a node in a single graph and finds tracks as compact clusters in a learned latent space, is retrained on simulated degraded data. The central result is that the retrained BAT Finder keeps its track-efficiency loss to about 14% when going from nominal to degraded conditions (0.7469 to 0.6417), while the Belle II staged baseline loses roughly a quarter of its efficiency (0.4804 to 0.3618) and its purity drops to about 90%. The paper concludes that detector degradation is a domain shift in hit patterns rather than a

Load-bearing premise

The single simulated degradation map—wire efficiencies from Eq. (1) with C=0.35, a disabled first superlayer, and 14 disabled readout boards—is assumed to represent real CDC ageing; the paper reports results only for this one configuration.

Editorial extensions

If this is right

  • Retraining on a broad mixture of degradation patterns lets the same model run on the high-level trigger, coping with suddenly changing detector conditions without redesign.
  • Offline reconstruction can be re-fine-tuned whenever the measured degradation map changes, recovering efficiency and purity at low cost.
  • The unified SVD+CDC clustering removes the clone-track problem caused by the enlarged gap between the SVD and the first active CDC layers.
  • Tracks that originate inside the CDC (displaced beyond the SVD), as well as low-momentum and endcap tracks, benefit most from the retrained model.
  • The approach suggests detector ageing does not force an early end to CDC operation; operational lifetime can be extended by data-driven model updates.

Reading between the lines

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

  • If the domain-shift interpretation holds, periodic fine-tuning on fresh measured degradation maps should keep performance stable indefinitely, as long as the latent clustering structure itself remains stable; this could be tested by progressively worsening simulated maps.
  • The same retrain-on-degradation recipe may transfer to other learned track finders and to other subdetectors (e.g., pixel detectors) that suffer efficiency losses, though this is not shown here.
  • The abstract's '28%' baseline loss does not match Table 1's roughly 25% relative loss; clarifying the definition of 'absolute track efficiency loss' would make the headline numeric claim easier to verify.
  • Because the model learns associations in a latent space rather than geometric proximity, it may also tolerate non-ageing sparse patterns such as temporary readout-board failures or high-background masking, but the paper only demonstrates one degradation configuration.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper investigates the robustness of a graph-neural-network-based track reconstruction algorithm (BAT Finder) to simulated ageing of the Belle II central drift chamber. Degradation is modeled with a wire-efficiency parametrization (Eq. 1, C=0.35), a disabled first superlayer, and 14 disabled readout boards. The BAT Finder is fine-tuned on this degraded configuration and compared with the Belle II baseline and the earlier CAT Finder in Table 1. The retrained BAT Finder achieves 0.6417 efficiency and 0.9613 purity under degraded conditions, versus 0.3618 and 0.9018 for the baseline. The authors conclude that detector ageing can be treated as a domain shift and that retraining the GNN-based finder recovers most of the lost efficiency while preserving high purity.

Significance. If the quantitative claims hold, the result is practically relevant for Belle II operation at higher luminosities: it suggests a single GNN architecture can be adapted to degraded detector conditions without redesign. The paper provides a full-detector simulation with beam backgrounds, a direct comparison to the production baseline, and concrete measured numbers with statistical uncertainties. The manuscript is also appropriately explicit in places, noting that the study is limited to offline reconstruction and a single measured detector configuration. However, the headline claim in the abstract is not fully supported by Table 1, and the single-map evaluation limits the generality of the robustness statement. The central comparison is measured rather than derived, so I do not see a circularity problem, but the fine-tuning/evaluation protocol is optimistic for real-world generalization.

major comments (4)
  1. [Abstract and Table 1] The abstract's headline numbers are not reproduced by the data. From Table 1, the baseline relative efficiency loss is (0.4804−0.3618)/0.4804 = 24.7%, not 28%; the absolute loss is 11.9 percentage points. The retrained BAT Finder relative loss is (0.7469−0.6417)/0.7469 = 14.1%, or 10.5 percentage points absolute. Thus 'absolute track efficiency loss ... 14%, compared to 28%' is internally inconsistent: no calculation in the paper yields 28%, and the term 'absolute' conflicts with the relative-loss interpretation. Please correct the abstract and state explicitly whether the quoted numbers are relative losses or absolute percentage-point changes.
  2. [Sec. 4 and Sec. 5 (single degradation map)] The evaluation uses one fixed degradation map: C=0.35, disabled first superlayer, and 14 disabled readout boards (Fig. 5). The BAT Finder is fine-tuned on this exact map and evaluated on the same map. The text itself says the paper 'focuses exclusively on' offline mode with 'a single measured detector configuration.' This demonstrates adaptation to one known configuration, not robustness to unseen spatial or time-dependent ageing patterns. The abstract's claim of 'robustness to irregular hit patterns' is therefore stronger than the evidence. Please add a held-out degradation test (different C values, board patterns, random seeds, or time-dependent maps) or reframe the claim as a proof-of-principle for offline fine-tuning on a known condition.
  3. [Sec. 5, Table 1 (systematics)] Only statistical uncertainties are reported. The degradation model parameters (C, board count/pattern, background overlay) are fixed, and no systematic uncertainties are given. The numerical differences between retrained and non-retrained BAT Finder (efficiency 0.6417 vs 0.6256; purity 0.9613 vs 0.9337) might be sensitive to these modeling choices. A sensitivity scan over C and board configurations, or at least a clear statement of the expected systematic size, is needed to support the quantitative comparison in Table 1 and the abstract.
  4. [Sec. 4 and Sec. 5 (asymmetric comparison)] The BAT Finder is fine-tuned on the degraded dataset, while the Belle II baseline is not reoptimized or retrained for degraded conditions. The comparison therefore conflates algorithmic robustness with the benefit of adaptation. The manuscript should state this asymmetry explicitly and, if possible, show the baseline with adjusted track-quality criteria. Without this, the headline 'compared to 28% for the baseline' is not a like-for-like comparison.
minor comments (6)
  1. [Sec. 4, last paragraph] The sentence '... improve efficiency and resolution. [23]. we focus exclusively on the second mode.' is incomplete and mis-capitalized. Please rephrase.
  2. [Eq. (1)] The symbols N0 and L0 are not defined. Presumably they refer to reference-layer values; please define explicitly.
  3. [Sec. 4] The training dataset is 'defined in detail in [17]' but the reader needs the number of events and the exact mix of prompt/displaced muons to judge the statistical coverage. Please include these details or a reference to a publicly available dataset.
  4. [Fig. 5 caption] The caption calls the map the 'current CDC degradation (C=0.35)' while Sec. 4 says C=0.35 'approximately reproduces' the current conditions. Please clarify whether this is a measured or simulated configuration.
  5. [Sec. 5, Fig. 6] In Fig. 6(b), the purple curve label 'retrained BAT Finder' is not fully visible in the caption text. Please ensure the legend entries match the caption.
  6. [Sec. 4, C parameter] The value C=0.35 is taken from the first author's PhD thesis [23]. Since this is not publicly available to all readers, please include a short description of how this value was derived from accumulated charge measurements.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the robustness claim is an empirical simulation benchmark, not a derived or self-referential result.

full rationale

The central claim—that a retrained GNN-based BAT Finder limits efficiency loss under CDC ageing—is established by simulation measurements reported in Table 1 and Figure 6, not by a derivation from an input. Track efficiency and purity are defined operationally (N_reco,matched/N_true and N_reco,matched/N_reco,total) and evaluated on a full Belle II detector simulation with an external baseline (the Belle II Baseline Finder). The ageing model in Eq. (1) with C=0.35 is taken from the first author's prior thesis [23], but it is an input simulation condition, not a parameter fitted to the target efficiency/purity results; the paper's conclusions are not obtained by substituting the output into the input. Self-citations to [11], [17], and [23] describe the architecture and the ageing parametrization, but the performance comparison is new and externally grounded in the baseline reconstruction and the detector simulation. The paper does fine-tune on the same degradation map on which it evaluates, which limits generalization to unseen ageing patterns, but this is a scope limitation rather than circularity: the reported numbers remain measured outputs, not identities. The abstract's '28%' baseline loss is not reproduced by Table 1 (the baseline relative loss is about 24.7% from 0.4804 to 0.3618), but this is an internal reporting inconsistency, not a circular step. No load-bearing step reduces by definition or by self-citation to the claim being demonstrated.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the realism of a simulated degradation model and on the correctness of the previously published GNN architecture. The only tuned simulation parameter is C=0.35; the disabled-board pattern is another scenario choice. No new physical entities are introduced.

free parameters (2)
  • C (ageing strength) = 0.35
    Controls the wire-efficiency loss in Eq. (1); set to 0.35 to approximate current Belle II CDC conditions, based on [23]. All central results are for this value only.
  • Disabled-board pattern = 14 boards incl. 3 consecutive; first superlayer disabled
    Chosen to reflect realistic failure modes but not varied in the offline evaluation; results may be configuration-specific.
assumptions (4)
  • domain assumption Wire efficiency follows ε_layer,i = 1 − C·(N0/Ni)·(L0/Li) (Eq. 1), with C=0.35 reproducing current CDC conditions.
    Used to generate all degraded-condition samples; no validation against real degraded tracking data in this paper.
  • domain assumption The GravNet + object condensation architecture from [11,17] clusters hits correctly under sparse and irregular hit patterns.
    The paper does not re-derive or formally verify the model; it relies on prior results for correctness.
  • domain assumption Overlaid beam-induced background from simulation and recorded data [24,25] accurately represents real operating conditions.
    Training and evaluation use this background; incorrect background modeling would change efficiency and purity.
  • domain assumption Track efficiency/purity defined by unique matching to true particles (as in [12,17]) is an appropriate performance measure.
    The definitions are conventional in the collaboration; no alternative metrics are discussed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Mitigating Detector Ageing Effects with Graph-Based Multi-Modal Track Reconstruction at Belle II." pith.science (2026). https://pith.science/paper/ZB4FHEEJ

@misc{pith2026260715361,
  author       = {Pith},
  title        = {Pith review of: Mitigating Detector Ageing Effects with Graph-Based Multi-Modal Track Reconstruction at Belle II},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZB4FHEEJ}},
  note         = {Machine review of arXiv:2607.15361}
}
read the original abstract

Large backgrounds that can lead to hardware failures and the degradation of detector gain impact the track finding in the Belle II central drift chamber. These conditions lead to spatially non-uniform and time-dependent inefficiencies, which results in inactive regions and missing hits which challenges conventional tracking algorithms and necessitate the development of new track finding algorithms. In this work, we evaluate the performance of our previously developed unified graph neural network (GNN) based track-finding algorithm under realistic long-term detector ageing conditions. Track finding is formulated as a global relational clustering problem using object condensation, which enables the reconstruction of an unknown and variable number of tracks per event. Using a realistic full detector simulation incorporating beam-induced backgrounds, detector noise, and measured detector ageing effects, we evaluate the tracking performance and compare it to the current Belle II baseline reconstruction. We show that detector degradation can be treated as a domain shift in the observed hit patterns, rather than requiring a fundamentally new reconstruction strategy. After retraining on degraded detector conditions, the GNN-based approach limits the absolute track efficiency loss for uniformly displaced muons to 14%, compared to 28% for the baseline tracking, while maintaining a track purity of 96%. Under the same conditions, the baseline reconstruction achieves only 90% track purity. These results demonstrate that the unified GNN-based reconstruction provides increased robustness to irregular hit patterns and extended inactive regions, enabling stable tracking performance under long-term detector ageing at Belle II.

Figures

Figures reproduced from arXiv: 2607.15361 by the authors.

Figure 1
Figure 1. The Belle II track finding chain for high level trigger tracks and tracks used in offline reconstruction. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Geometrical structure of the CDC shown in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The distribution of the reduced wire efficiency, where the yellow, green and blue marked wires [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: BAT Finder model architecture, taken from [11]. • displaced tracks traverse fewer SVD layers and therefore rely more heavily on CDC measurements, which have worse momentum and vertex resolution due to missing inner hits. Such hit patterns primarily violate the geometri…
Figure 5
Figure 5. Figure 5: Example event displays illustrating the current CDC degradation (C=0.35, see eq. (1) for details). [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 5
Figure 5. Figure 5: fig. 5. Track efficiency is defined as [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Track efficiency as a function of transverse displacement [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

28 extracted references · 3 linked inside Pith

  1. [1]

    Measured and projected beam backgrounds in the Belle II experiment at the SuperKEKB collider,

    A. Natochii et al., “Measured and projected beam backgrounds in the Belle II experiment at the SuperKEKB collider,” Nucl. Instrum. Methods Phys. Res. A1055, 168550 (2023) doi:10.1016/j.nima.2023.168550

    arXiv 2023

  2. [2]

    Belle II Technical Design Report,

    T. Abe et al., “Belle II Technical Design Report,” [arXiv:1011.0352 [physics.ins-det]]

    arXiv

  3. [3]

    The Belle II Detector Upgrades Framework Conceptual Design Report,

    H. Aihara et al., “The Belle II Detector Upgrades Framework Conceptual Design Report,” [arXiv:2406.19421 [hep-ex]]

    arXiv

  4. [4]

    Full Report of the Annual Review Meeting — 3–5 March 2025, Tsukuba, Japan,

    B-factory Programme Advisory Committee, “Full Report of the Annual Review Meeting — 3–5 March 2025, Tsukuba, Japan,” Belle II Collaboration,https://docs.belle2.org/files/4563/ BELLE2-REPORT-2025-002/1/BELLE2-REPORT-2025-002.pdf

    2025

  5. [5]

    Thin Film Field Emission,

    L. Malter, “Thin Film Field Emission,” Phys. Rev.50, 48–58 (1936) doi:10.1103/PhysRev.50.48

    doi:10.1103/physrev.50.48 1936

  6. [6]

    Central Drift Chamber for Belle-II,

    N. Taniguchi, “Central Drift Chamber for Belle-II,” J. Inst.12, C06014 (2017) doi:10.1088/1748- 0221/12/06/C06014

    doi:10.1088/1748- 2017

  7. [7]

    The Concurrent Track Evolution algorithm: extension for track finding in the inhomogeneous magnetic field of the HERA-B spectrometer,

    R. Mankel and A. Spiridonov, “The Concurrent Track Evolution algorithm: extension for track finding in the inhomogeneous magnetic field of the HERA-B spectrometer,” Nucl. Instrum. Methods A426, 268–282 (1999) doi:10.1016/S0168-9002(99)00013-3

    doi:10.1016/s0168-9002(99)00013-3 1999

  8. [8]

    Pattern recognition and event reconstruction in particle physics experiments,

    R. Mankel, “Pattern recognition and event reconstruction in particle physics experiments,” Rep. Prog. Phys.67, 553 (2004) doi:10.1088/0034-4885/67/4/R03

    doi:10.1088/0034-4885/67/4/r03 2004

Show all 28 references
  1. [9]

    Description and performance of track and primary-vertex reconstruction with the CMS tracker,

    S. Chatrchyan et al. (CMS Collaboration), “Description and performance of track and primary-vertex reconstruction with the CMS tracker,” J. Inst.9, P10009 (2014) doi:10.1088/1748-0221/9/10/P10009

    2014 doi

  2. [10]

    Performance of the ATLAS track reconstruction algorithms in dense environments in LHC Run 2,

    M. Aaboud et al. (ATLAS Collaboration), “Performance of the ATLAS track reconstruction algorithms in dense environments in LHC Run 2,” Eur. Phys. J. C77, 673 (2017) doi:10.1140/epjc/s10052-017- 5225-7

    2017 doi

  3. [11]

    Multi-Modal Track Reconstruction using Graph Neural Networks at Belle II,

    L. Reuter et al., “Multi-Modal Track Reconstruction using Graph Neural Networks at Belle II,” [arXiv:2602.10897 [hep-ex]]

  4. [12]

    Track finding at Belle II,

    V. Bertacchi et al., “Track finding at Belle II,” Comput. Phys. Commun.259, 107610 (2021) doi:10.1016/j.cpc.2020.107610

    2021

  5. [13]

    A Novel Generic Framework for Track Fitting in Complex Detector Systems,

    C. Hoppner et al., “A Novel Generic Framework for Track Fitting in Complex Detector Systems,” Nucl. Instrum. Methods Phys. Res. A620, 518–525 (2010) doi:10.1016/j.nima.2010.03.136

    2010 doi

  6. [14]

    Implementation of GENFIT2 as an experiment independent track-fitting framework,

    T. Bilka et al., “Implementation of GENFIT2 as an experiment independent track-fitting framework,” [arXiv:1902.04405 [physics.data-an]]

    1902 arXiv

  7. [15]

    GENFIT - a Generic Track-Fitting Toolkit,

    J. Rauch and T. Schl¨ uter, “GENFIT - a Generic Track-Fitting Toolkit,” J. Phys. Conf. Ser.608, 012042 (2015) doi:10.1088/1742-6596/608/1/012042

    2015 doi

  8. [16]

    GenFit/GenFit,

    Jojosito et al., “GenFit/GenFit,”https://doi.org/10.5281/zenodo.10301439

    doi

  9. [17]

    End-to-End Multi-track Reconstruction Using Graph Neural Networks at Belle II,

    L. Reuter et al., “End-to-End Multi-track Reconstruction Using Graph Neural Networks at Belle II,” Comput. Softw. Big Sci.9, 6 (2025) doi:10.1007/s41781-025-00135-6 9 Connecting the Dots. November 10-14, 2025

    2025 doi

  10. [18]

    Calibration and alignment of the Belle II central drift chamber,

    T. V. Dong et al., “Calibration and alignment of the Belle II central drift chamber,” Nucl. Instrum. Methods Phys. Res. A930, 132–141 (2019) doi:10.1016/j.nima.2019.03.072

    2019 doi

  11. [19]

    Wire chamber aging,

    J. A. Kadyk, “Wire chamber aging,” Nucl. Instrum. Methods Phys. Res. A300, 436–479 (1991) doi:10.1016/0168-9002(91)90381-Y

    1991 doi

  12. [20]

    Review of wire chamber aging,

    J. Va’Vra, “Review of wire chamber aging,” Nucl. Instrum. Methods Phys. Res. A252, 547–563 (1986) doi:10.1016/0168-9002(86)91239-8

    1986 doi

  13. [21]

    Learning representations of irregular particle-detector geometry with distance- weighted graph networks,

    S. R. Qasim et al., “Learning representations of irregular particle-detector geometry with distance- weighted graph networks,” Eur. Phys. J. C79, 608 (2019) doi:10.1140/epjc/s10052-019-7113-9

    2019 doi

  14. [22]

    Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data,

    J. Kieseler, “Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data,” Eur. Phys. J. C80, 886 (2020) doi:10.1140/epjc/s10052-020-08461-2

    2020 doi

  15. [23]

    Track Finding with Graph Neural Networks in the Belle II Drift Chamber,

    L. Reuter, “Track Finding with Graph Neural Networks in the Belle II Drift Chamber,” PhD thesis, Karlsruher Institut f¨ ur Technologie (KIT), 2026 doi:10.5445/IR/1000189859

    2026

  16. [24]

    Measurements of beam backgrounds in SuperKEKB Phase 2,

    Z. J. Liptak et al., “Measurements of beam backgrounds in SuperKEKB Phase 2,” Nucl. Instrum. Methods Phys. Res. A1040, 167168 (2022) doi:10.1016/j.nima.2022.167168

    2022

  17. [25]

    Beam Background Expectations for Belle II at SuperKEKB,

    A. Natochii et al., “Beam Background Expectations for Belle II at SuperKEKB,” [arXiv:2203.05731 [hep-ex]]

    arXiv

  18. [26]

    The Belle II Core Software,

    T. Kuhr et al., “The Belle II Core Software,” Comput. Softw. Big Sci.3, 1 (2019) doi:10.1007/s41781- 018-0017-9

    2019 doi

  19. [27]

    Belle II Analysis Software Framework (basf2) (release-09-00-00),

    Belle II Collaboration, “Belle II Analysis Software Framework (basf2) (release-09-00-00),”https:// doi.org/10.5281/zenodo.16268234, 2025

    2025 doi

  20. [28]

    Review of Particle Physics,

    S. Navas et al. (Particle Data Group), “Review of Particle Physics,” Phys. Rev. D110, 030001 (2024) doi:10.1103/PhysRevD.110.030001. 10

    2024 doi

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.