Pith Number

pith:4RPCRM7W

pith:2026:4RPCRM7W6K5TI2NDYM55MZ32XB

not attested not anchored not stored refs resolved

Improving ecological inference and uncertainty quantification from camera trap data through the fusion of AI confidences and manual annotations

Adira Cohen, Erin M. Schliep, Matthew Snider, Mohammad Alyetama, Roland Kays

A Bayesian hierarchical model fuses AI predictions with human annotations to improve ecological inferences from camera trap images.

arxiv:2605.13660 v1 · 2026-05-13 · stat.AP

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\usepackage{pith}
\pithnumber{4RPCRM7W6K5TI2NDYM55MZ32XB}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We propose a new Bayesian hierarchical data-fusion model which combines the strengths of human annotations and AI predictions... We find that bucks in rut have higher body condition than other deer and that green, open habitats are correlated with high body condition. Our new model derived novel ecological inference compared to a traditional approach using the same data.

C2weakest assumption

That AI confidence scores and manual annotations can be jointly modeled as conditionally independent observations of a latent ecological state without systematic bias from either source or from the choice of hierarchical structure.

C3one line summary

A Bayesian data-fusion model combines AI predictions and manual labels from camera traps to yield improved ecological inference and uncertainty quantification for white-tailed deer body condition.

References

50 extracted · 50 resolved · 0 Pith anchors

[1] Steen-Adams , title =

[2] Journal of the American statistical Association , volume= 1993

[3] Journal of the Royal Statistical Society: Series B (Methodological) , author = 1982 · doi:10.1111/j.2517-6161.1982.tb01195.x

[4] Strictly Proper Scoring Rules, Prediction, and Estimation.Journal of the American Statistical Association, 102(477):359–378, March 2007 2007 · doi:10.1198/016214506000001437

[5] Performance Metrics for Probabilistic Ordinal Classifiers , booktitle = 2023

Receipt and verification

First computed	2026-05-18T02:44:17.330369Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e45e28b3f6f2bb3469a3c33bd6677ab87213f87278694c56032c328e12715a48

Aliases

arxiv: 2605.13660 · arxiv_version: 2605.13660v1 · doi: 10.48550/arxiv.2605.13660 · pith_short_12: 4RPCRM7W6K5T · pith_short_16: 4RPCRM7W6K5TI2ND · pith_short_8: 4RPCRM7W

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/4RPCRM7W6K5TI2NDYM55MZ32XB \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e45e28b3f6f2bb3469a3c33bd6677ab87213f87278694c56032c328e12715a48

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4fc8cdf03430414c4f21d37ada90e54d3b98949a01cb856742d4fc2b39ae4d87",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.AP",
    "submitted_at": "2026-05-13T15:18:03Z",
    "title_canon_sha256": "9a2109686c2227534ce3c35ff56c8e45a7fdc2ac51bb70f60aef360dc7d03891"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13660",
    "kind": "arxiv",
    "version": 1
  }
}