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Record W7011560464

Movement and life history diversity of Yellow Perch (Perca flavescens) between Lake Ontario and two barrier beach wetlands in the Braddock Bay Wildlife Management Area

2022· dissertation· en· W7011560464 on OpenAlexaboutno aff

Bibliographic record

VenueSUNY Digital Repository Support (State University of New York System) · 2022
Typedissertation
Languageen
FieldMedicine
TopicBiomedical and Chemical Research
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatWetlandPerchBayWaterfowlOtolithδ15NIsotope analysisFauna
DOInot available

Abstract

fetched live from OpenAlex

Yellow Perch (Perca flavescens) utilize both nearshore and coastal wetland habitats of the Laurentian Great Lakes during their lifetime and are known to exhibit different movement life histories. However, uncertainty persists in quantifying variability in the duration of habitat use and whether such variation manifests as morphometric differences depending on the degree of nearshore use. To explore these uncertainties, I used a multi-metric approach that included water and otolith microchemistry, tissue stable isotopes (𝛿15N, 𝛿13C), and body morphometric analysis. Manganese was useful for identifying movements between wetland and lake habitats while carbon and nitrogen tissue isotopes revealed variable duration of wetland use related to ontogeny. Morphometrically, Yellow Perch caught in Lake Ontario had smaller features relative to wetland caught Yellow Perch. My research suggests that otolith microchemistry is a useful tool for describing habitat transitions of Yellow Perch between these two habitat types. Tissue stable isotopes indicate that some Yellow Perch spend more time in coastal wetland habitats than others, which may influence their susceptibility to recreational harvest. Body morphometrics appear to reflect either use of more open habitats (e.g., Lake Ontario), or use of more complex habitats (e.g., coastal wetland).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.222
Teacher spread0.200 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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