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Record W2594163691 · doi:10.1139/facets-2015-0009

Recent changes in the diet and survival of Atlantic puffin chicks in the face of climate change and commercial fishing in midcoast Maine, USA

2016· article· en· W2594163691 on OpenAlexvenueno aff
Stephen W. Kress, Paula Shannon, Christopher Mitchell O'Neal

Bibliographic record

VenueFACETS · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersU.S. Fish and Wildlife ServicePew Charitable Trusts
KeywordsFisheryHerringClupeaBiologyHaddockCapelinSebastesForage fishFishingFish <Actinopterygii>

Abstract

fetched live from OpenAlex

We examined the diet of Atlantic puffin ( Fratercula arctica) chicks at three midcoast Maine, USA, colonies during the years 2005–2014 and found that the puffins at each island have a distinct diet that has changed in recent years. White hake ( Urophycis tenuis) is by far the most frequently delivered prey at each island. Atlantic herring ( Clupea harengus) is the second most frequently delivered food, but has declined in recent years on two islands. In contrast, butterfish ( Poronotus triacanthus), haddock ( Melanogrammus aeglefinus), and redfish ( Sebastes spp.) have increased in the puffin diet on all islands. Chick condition declined significantly from 1993 to 2009. We demonstrate that puffin chicks with greater body weight experience a higher chance of postfledging survival as compared to chicks with lower body weight. The years 2012–2013 were a period of extreme sea surface warming, in which puffin hatching success and productivity sharply declined. This study provides new insight into changes in marine communities, examining changes in chick diet. We discuss our findings in relation to warming sea surface temperatures, recent climate-related decline in puffin productivity in the Gulf of Maine, and the impact of commercial fisheries on forage fish.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.051
GPT teacher head0.275
Teacher spread0.224 · 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
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

Citations27
Published2016
Admission routes1
Has abstractyes

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