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Record W1972837472 · doi:10.1139/f00-092

Population dynamics of rainbow smelt (<i>Osmerus mordax</i>) in Lakes Ontario and Erie: a modeling analysis of cannibalism effects

2000· article· en· W1972837472 on OpenAlexvenueaboutno aff
Brian F. Lantry, Donald J. Stewart

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSmeltCoregonusCannibalismPelagic zoneFisheryPopulationBioenergeticsEnvironmental scienceAbundance (ecology)BiologyZooplanktonEcologyPredationFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

We used a stochastic stage-based matrix model (annual time step) and a bioenergetics model (daily time step) to simulate population dynamics, production, consumption, and conversion efficiency for rainbow smelt (Osmerus mordax) populations in Lakes Ontario and Erie. Cannibalism on young-of the-year (YOY) smelt by yearlings was the only scenario that reproduced alternate-year recruitment cycles observed in Lakes Ontario and Erie. Assuming constant survivorship and 5% variation in cannibalism, less than one YOY consumed per yearling smelt per year in both lakes could produce fluctuations greater than those observed. We found that at estimated daily mortality rates and during the pelagic phase of larvae only, 2% of the yearling smelt in Lake Erie and 5.1% in Lake Ontario need to consume one YOY per day to induce the observed abundance fluctuations. Bioenergetics simulations of alternating recruitment produced fluctuations in simulated values for annual gross production of approximately 6-7 and 31-59% for the Lake Ontario and Lake Erie smelt populations, respectively.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.383

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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 designSimulation or modeling
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

Citations23
Published2000
Admission routes2
Has abstractyes

Explore more

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