MétaCan
Menu
Back to cohort
Record W4253565724 · doi:10.1093/condor/103.1.98

Reproductive Biology of Sabine's Gull in the Canadian Arctic

2001· article· en· W4253565724 on OpenAlexaffabout
Iain J. Stenhouse, H. Grant Gilchrist, William A. Montevecchi

Bibliographic record

VenueOrnithological Applications · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsLagopusHatchingTernNest (protein structural motif)ArcticReproductive biologyAvian clutch sizeArctic foxPredationBiologyReproductive successEcologySternaPhenologyZoologyCharadriiformesBreedSeasonal breederLarusFisheryReproductionFish <Actinopterygii>DemographyPopulation

Abstract

fetched live from OpenAlex

Abstract We studied the reproductive biology of Sabine's Gulls (Xema sabini) breeding on Southampton Island, in the eastern Canadian Arctic, from May to August in 1998 and 1999, and compared our results to information collected from the same region in 1980. Breeding phenology was 10 days earlier in 1998 than in these other years, and reflects an earlier onset of snowmelt in that year. Nests were dispersed, with a density of 7.6 to 8.7 nests per km2. Sabine's Gulls exhibited strong interannual fidelity to breeding sites. Mean clutch size was lower in 1999 than 1998, and lower in both these years than in 1980. Hatching success was 63% in 1998, but only 21% in 1999 due to increased predation, most likely by arctic fox (Alopex lagopus). Adult gulls and chicks abandoned nest-sites within a few hours of the hatching of the last chick and relocated to coastal ponds, where adults continued to attend chicks. In comparisons of the reproductive biology of Sabine's Gull to closely related “tern-like” gull species and other “black-headed” gulls, Sabine's Gull showed a number of distinct ecological and behavioral differences and represents an ecological outlier within the Laridae.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.275
Teacher spread0.249 · 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.

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

Citations7
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueOrnithological ApplicationsSame topicAvian ecology and behaviorFrench-language works237,207