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Record W2110828527 · doi:10.1648/0273-8570-72.3.338

USE OF TIPS BY NESTING KELP GULLS AT A GROWING COLONY IN PATAGONIA

2001· article· en· W2110828527 on OpenAlexaff
Marcelo Bertellotti, Pablo Yorio, Guillermo Blanco, Maricel Giaccardi

Bibliographic record

VenueJournal of Field Ornithology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsKelpFisheryBiologyAbundance (ecology)Nesting (process)EcologyEnvironmental science

Abstract

fetched live from OpenAlex

We evaluated the magnitude of use of waste tips by Kelp Gulls (Larus dominicanus) nesting at Isla de los Pájaros, a large and growing colony in Patagonia, Argentina, and we assessed the difference in use between tips with urban and fishery waste. We marked with color dye 1347 adult breeding Kelp Gulls to determine if they fed in urban and fishery tips and to estimate the number of birds which used those tips during incubation. Kelp Gulls were present during 100% and 64% of counts at the fishery waste tip and the urban waste tip, respectively. The number of adult gulls was always larger at the fishery waste tip (mean ± SD = 1694 ± 664) than in the urban waste tip (mean ± SD = 59 ± 68). Considering the total number of gulls flying from the colony to the tips and the proportion of marked gulls in the tips, we estimated that at least 54–69% of the birds of the colony were present at the tips. The use of fishery waste may have contributed to the increase in the number of Kelp Gulls breeding at Isla de los Pájaros, and the quality, abundance, and predictability of food disposed at the fishery waste tip could foster the use of this site relative to natural food sources located closer to their breeding colony. Removal of artificial food sources may be assessed at the Puerto Madryn and other Patagonian waste tips to reduce conflicts between human and gull populations.

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 score0.025
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.257
Teacher spread0.231 · 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

Citations53
Published2001
Admission routes1
Has abstractyes

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