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Record W4402140541 · doi:10.1051/alr/2024010

Gravel washing as a lacustrine spawning habitat restoration method for smallmouth bass

2024· article· en· W4402140541 on OpenAlexafffundabout
Daniel M. Glassman, Benjamin L. Hlina, Lisa Donaldson, Alice E.I. Abrams, Jordanna N. Bergman, Auston D. Chhor, Lauren J. Stoot, Steven J. Cooke

Bibliographic record

VenueAquatic Living Resources · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaParks CanadaOntario Ministry of Natural Resources and ForestryMinistry of Natural Resources
KeywordsBass (fish)HabitatFisheryEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

Smallmouth bass ( Micropterus dolomieu ) spawn on gravel and cobble in the littoral zone of lakes that may become degraded by the presence of fine sediments and decomposing organic matter. Substrate size and composition have been identified as important variables for nest site selection by male smallmouth bass. We tested whether ‘cleaning’ substrate by removing sediment with a pressure washer would increase the number of bass nests or the average total length (mm) of nesting smallmouth bass in selected areas of Big Rideau Lake, Ontario, Canada the following year using a before-after control-impact design. Treatment was not a significant predictor of nest abundance or average male length. Considering the strength of the experimental design it is reasonable to conclude that this intervention failed to enhance spawning substrate for smallmouth bass. Understanding the factors that maintain productive spawning sites for smallmouth bass is important to restoration effectiveness and determining where habitat enhancement will provide the greatest benefits.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.221
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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.

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

Citations1
Published2024
Admission routes3
Has abstractyes

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