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Record W2332423003 · doi:10.1139/f2011-097

Stocked trout do not significantly affect wood frog populations in boreal foothills lakes

2011· article· en· W2332423003 on OpenAlexafffundvenueabout
C.M.M. Schank, Cynthia A. Paszkowski, William M. Tonn, Garry J. Scrimgeour

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsParks CanadaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaDirectorate for Biological SciencesAlberta Conservation Association
KeywordsTroutBiologyStockingEcologyTrophic levelBorealForage fishPopulationBrown troutAbundance (ecology)FisheryPredationFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Stocking salmonids into lakes can have negative consequences for some ecosystem components, including amphibians. In the boreal foothills of Alberta, Canada, we compared populations of wood frog ( Lithobates sylvaticus ) at lakes with (n = 5) and without (n = 6) stocked trout over 3 years; all 11 lakes also supported native populations of forage fishes. Abundance and size of adult and young-of-year (YOY) frogs did not differ significantly between stocked and unstocked lakes. We also compared the wood frog population from a twelfth, fishless lake with populations from the 11 fish-bearing lakes (with or without trout); abundance and size of adults and YOY were greater, and YOY emerged earlier in the absence of fish. Based on patterns compiled from a literature review of effects of stocked trout on anuran amphibians, we suggest that characteristics of our study systems, including the presence of native fish, the length of the anuran larval period, lake trophic status, and the existence of complex, littoral habitats, contributed to the lack of major impacts of stocked trout on wood frog 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 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 score0.870
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.046
GPT teacher head0.238
Teacher spread0.192 · 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

Citations8
Published2011
Admission routes4
Has abstractyes

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