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Aggregation of nests in an Arctic-breeding shorebird and daily survival rates

2023· dataset· en· W6906696000 on OpenAlexaffabout

Bibliographic record

VenueECCC Data Catalogue · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsGovernment of CanadaGovernment of QuebecEnvironment and Climate Change Canada
Fundersnot available
KeywordsSandpiperNest (protein structural motif)CalidrisSpatial distributionPopulationPopulation densityNesting (process)

Abstract

fetched live from OpenAlex

Our current understanding of the factors that influence where birds nest is incomplete, yet such information is important for accurate demographic assessments. To address questions related to spatial distributions of shorebird nests and to evaluate factors that may affect nest distribution in these species, during 2017 and 2019, we studied a small population of semipalmated sandpiper (Calidris pusilla) breeding in the Central Canadian Arctic, near the Karrak Lake Research Station, in Nunavut. The spatial distribution of semipalmated sandpiper nests at this site suggested loose aggregation, with median nearest neighbour distances of 73.8 m and 92.0 m in 2017 and 2019, respectively. Evidence for an influence of nesting distribution on daily survival rate of nests, however, was mixed. Neither nearest neighbour distance nor local nest density had a significant effect on daily nest survival in 2017, but in 2019, the best approximating model included an effect of local nest density, which indicated that nests in areas of high density had reduced survival rates. Contrary to other studies assessing settlement and nest site selection in semipalmated sandpipers, the spatial distribution of nests in this population demonstrates a role for social attraction in an otherwise territorial species and suggests that aggregated nesting can impose a cost on nest survival under certain conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.604
Threshold uncertainty score0.796

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.099
GPT teacher head0.349
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2023
Admission routes2
Has abstractyes

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Same venueECCC Data CatalogueFrench-language works237,207