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Record W2069695461 · doi:10.1139/f04-123

Effects of clay turbidity and light on the predatorprey interaction between smelts and chaoborids

2004· article· en· W2069695461 on OpenAlexvenueno aff
Jukka Horppila, Anne Liljendahl‐Nurminen, Tommi Malinen

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTurbidityThermoclinePredationLight intensityEcologyPredatorTurbidity currentIntraguild predationEnvironmental scienceBiologyPhysicsOptics

Abstract

fetched live from OpenAlex

The effects of clay turbidity and light on the predator–prey interaction between planktivorous smelts (Osmerus eperlanus) and phantom midge (Chaoborus flavicans) larvae were studied by means of laboratory experiments. Irrespective of light intensity, fish-mediated mortality of chaoborid larvae was highest at intermediate turbidity (20 nephelometric turbidity units (NTU)). Increases in light intensity enhanced the feeding rate of smelts at very low light intensities. A regression model describing the dependence of smelt-mediated mortality of chaoborids on light intensity and turbidity was fitted to the data. The model suggested that turbidity exceeding 30 NTU combined with light intensity below 0.1 µE·m–2·s–1provides an efficient daytime refuge for chaoborids even in the presence of planktivorous smelts. In the field studies, the depth distribution of chaoborids followed the predictions of the model. The depth at which chaoborid density was highest depended on the existence and location of the thermocline, the densest swarms occurring beneath the turbidity maximum in the metalimnion. The smelts occupied water layers above the chaoborids, suggesting that the chaoborids used the steep turbidity gradient in the thermocline as a shelter against predation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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

Citations94
Published2004
Admission routes1
Has abstractyes

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