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Record W3184876031 · doi:10.1111/ibi.13000

No evidence that cameras affect shorebird nest survival on the coastal plain of Arctic National Wildlife Refuge, AK

2021· article· en· W3184876031 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIbis · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsEnvironment and Climate Change Canada
FundersU.S. Fish and Wildlife ServiceAlaska Department of Fish and GameOffice of ScienceMassachusetts Department of Fish and Game
KeywordsNest (protein structural motif)TundraPredationArcticWildlifeEcologyWildlife refugeGeographyPredatorEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Cameras are important tools used to determine nest fate, identify predators and evaluate behaviour; however, they may impact the parameters they are used to measure, thereby biasing results. We evaluated the impact of cameras ˜ 10 m from the nest on shorebird nest survival at the Canning River Delta, Alaska, 2017–2018 ( n control = 122, n camera = 109) using a much larger sample size than in previous studies conducted in the Arctic and random assignments at nest discovery. We found no effect of camera presence at the nest on daily nest survival (model‐averaged daily survival rate (DSR) 85% confidence interval (CI); control: 0.971–0.983, camera: 0.969–0.982). We suggest that nest survival studies of tundra‐nesting birds should consider the use of cameras to minimize researcher disturbance, increase the accuracy of fate assignments, and broaden the ecological data collected (e.g. incubation behaviour, predator identification and non‐anthropogenic non‐predation disruption such as by caribou).

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.045
GPT teacher head0.283
Teacher spread0.238 · 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