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Record W2909485485 · doi:10.1139/cjfas-2018-0455

Effects of ontogeny and invasive crayfish on feeding ecology and mercury concentrations of predatory fishes

2019· article· en· W2909485485 on OpenAlexaffvenue
Kate S. Prestie, Iain D. Phillips, Douglas P. Chivers, Timothy D. Jardine

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsWater Security AgencyUniversity of Saskatchewan
Fundersnot available
KeywordsTrophic levelLittoral zoneCrayfishEsoxPredationPikeFood webBiologyEcologyForagingFishery

Abstract

fetched live from OpenAlex

Lake food web structure dictates the flow of energy and contaminants to top predators, and addition of invasive species can shift these flows. We examined trophic position (TP), proportional reliance on the littoral zone (Proplittoral), and mercury (Hg) concentrations across the life-span of two predatory fishes, walleye (Sander vitreus) and northern pike (Esox lucius), in lakes with and without invasive virile crayfish (Faxonius virilis). The littoral zone was the dominant foraging zone for both species regardless of size, accounting for 59% and 80% of the diet of walleye and pike, respectively. Both species increased in TP and Hg with body size, as did crayfish. Walleye in crayfish-present lakes had lower Proplittoral, TP, and Hg concentrations compared with non-present lakes, but trophic magnification of Hg through the food web was consistent across all six lakes. These findings underscore a strong role for the littoral zone in channeling energy and contaminants to higher trophic levels and how invasive species can occupy new habitats at low abundance while altering food web structure and contaminant bioaccumulation.

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)0.001
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.009
GPT teacher head0.191
Teacher spread0.183 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

Explore more

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