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Record W4206947735 · doi:10.1111/1556-4029.14986

Preliminary study of gull (Laridae) scavenging and dispersal of vertebrate remains, Shoals Marine Laboratory, Coastal New England

2022· article· en· W4206947735 on OpenAlexaff
James T. Pokines

Bibliographic record

VenueJournal of Forensic Sciences · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsLarusBiological dispersalTaphonomyFisheryShoalHerring gullEcologyCharadriiformesFeatherInvertebrateBiologyHerringZoologyGeographyFish <Actinopterygii>OceanographyPopulationGeology

Abstract

fetched live from OpenAlex

The taphonomic effects of avian taxa upon forensic scenes has been little researched, and shore species including gulls (Laridae) have received even less attention, despite their well-known behavior as scavengers of human food waste. In order to begin assessing their potential impact, a pilot study was undertaken on Appledore Island, Maine, USA, at the Shoals Marine Laboratory. This location was chosen for its isolation from most other vertebrate scavengers and its large, seasonal breeding colonies of gulls, primarily great black-backed (Larus marinus) and herring (L. argentatus) gulls. Two locations with fresh bones were monitored for scavenging activity, using trail cameras and Tile Mate® tracking chips. In addition, portions of the breeding colonies nesting areas along the rocky coast were surveyed for bones that must have derived at some distance from human-generated trash sources. All (n = 16) fresh bones underwent dispersal by gulls, with an average distance of 10 m and a maximum distance (in one case) of 85 m. Multiple bones from trash sources were transported a minimum of 150 m if locally acquired and a minimum of 10 km if acquired from the mainland. These bones had a maximum length of 192 mm and mass of 46.0 g. Gulls, with their global distribution, have the potential to be significant dispersers of human skeletal remains and should undergo additional taphonomic research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.281
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Forensic SciencesSame topicPleistocene-Era Hominins and ArchaeologyFrench-language works237,207