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Record W2969841072 · doi:10.22215/etd/2018-12634

An Investigation of Herring Gull Population Decline in Pukaskwa National Park, Lake Superior

2018· dissertation· en· W2969841072 on OpenAlexafffund
Bruce Laurich

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsCarleton University
FundersU.S. Geological SurveyEnvironment and Climate Change CanadaParks Canada
KeywordsLarusHerringHerring gullPopulationFisheryNational parkGeographyPredationEcologyFood chainFish <Actinopterygii>TernLimitingFishingBiology

Abstract

fetched live from OpenAlex

In Pukaskwa National Park(PNP) on Lake Superior, Herring Gull(Larus argentatus) population is used as an indicator of ecological integrity. Since the 1970s, their populations have declined by 70%. Lake-wide declines in prey fish may be limiting natural food sources for Pukaskwa gulls. In the southern section of the park there's little access to human sources of food. In the northern section of the park, impacts of food declines may be buffered as birds can obtain anthropogenic food from nearby dumps. To assess regional differences, Herring Gull eggs were collected from northern and southern PNP. Markers of diet composition, stable isotopes of nitrogen and carbon, fatty acids, were measured in the eggs. Analysis supports the hypothesis that gulls from the southern PNP rely to a greater extent on natural foods. Understanding the degree to which anthropogenic food supports Gull populations is critical when utilizing gulls as indicators of ecological integrity in PNP.

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 score0.899
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.012
GPT teacher head0.278
Teacher spread0.266 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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