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Northern pike (<i>Esox lucius</i>) growth and mortality in a northern Ontario river compared with that in lakes: influence of flow

2004· article· en· W2052805322 on OpenAlexafffundabout
Ronald W. Griffiths, Nathaniel K. Newlands, David L. G. Noakes, F. W. H. Beamish

Bibliographic record

VenueEcology Of Freshwater Fish · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Guelph
FundersNatural Resources CanadaFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Natural Resources Limited
KeywordsEsoxPikePopulationProductivityAbiotic componentLatitudeMortality rateEcologyGeographyBiologyFish <Actinopterygii>DemographyFishery

Abstract

fetched live from OpenAlex

Abstract – We measured the growth and mortality characteristics of northern pike ( Esox lucius ) in a northern Ontario river and examined the influence of flow on these characteristics by comparing our measurements with those estimated for a lake at the same latitude based on published studies. Pike ranged in total length from 229 to 784 mm, in mass from 70 to 4250 g, and in age from 1 to 10 years. The population showed a preponderance of 2–5‐year olds, with few fish surviving beyond 7 years of age. Growth, in terms of length increase, was similar to that reported for circumpolar populations. Mean total length at 5 years of age was 577 mm, growth rate of young adults was 62.5 mm year −1 , growth was isometric, longevity was 10 years of age, and the adult annual mortality rate was 49%. Growth and mortality characteristics of this riverine population were similar to those estimated for a lacustrine population at the same latitude. Flow thus had little measurable effect on the growth or mortality of pike possibly because of the overwhelming effect of other abiotic variables such as temperature, length of growing season and productivity. Consequently, growth characteristics of lacustrine populations can be used to assess the health and condition of riverine 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 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)0.000
metaresearch head score (Gemma)0.000
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 score0.950
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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

Citations16
Published2004
Admission routes3
Has abstractyes

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