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Record W2147815385 · doi:10.1139/f01-170

Intercohort competition effects on survival, movement, and growth of brown trout (<i>Salmo trutta</i>) in Swedish streams

2001· article· en· W2147815385 on OpenAlexvenueno aff
Fredrik Nordwall, Ingemar Näslund, Erik Degerman

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSalmoBrown troutElectrofishingTroutBiologyCompetition (biology)Abundance (ecology)SalmonidaeFish <Actinopterygii>Age structureEcologyFisheryDemographyPopulation

Abstract

fetched live from OpenAlex

The effects of density-dependent intercohort competition on growth and mortality in stream-resident brown trout (Salmo trutta) were tested by experimentally reducing the densities of age-1 fish and fish older than age 1 in six small streams. When densities of age-1 fish were reduced, abundance of age-0 and age-1 fish increased the following year, while age-1 fish experienced a reduced mean size. Reduced densities of fish older than age 1 resulted in increased apparent survival of age-0, age-1, and age-2 fish in the subsequent year. Mean size of age-2 fish increased as well. Many older immigrants (age >2) were found in the treatment sections where densities previously had been reduced. Data from the Swedish Electrofishing RegiSter (SERS) showed that mean body size of age-0 brown trout was negatively related to both age-0 and age >0 densities. The database also verified the inverse relationship between age-0 abundance and abundance of older cohorts. Our results indicate that intercohort competition regulates territorial fish populations, even when simple single populations in restricted environments are considered.

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)0.001
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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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

Citations84
Published2001
Admission routes1
Has abstractyes

Explore more

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