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Record W1841606297 · doi:10.1139/cjfas-2013-0295

Fish growth and degree-days I: selecting a base temperature for a within-population study

2013· article· en· W1841606297 on OpenAlexafffundvenue
Kyle A. Chezik, Nigel P. Lester, Paul Venturelli

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsTrent UniversityMinistry of Natural Resources and Forestry
FundersMinistry of Natural Resources
KeywordsDegree (music)Fish <Actinopterygii>PopulationStatisticsRange (aeronautics)Base (topology)BiologyMathematicsVariation (astronomy)Animal scienceEcologyFisheryDemographyPhysicsMaterials scienceSociology

Abstract

fetched live from OpenAlex

Degree-days (DD) are an increasingly popular method for explaining variation in fish growth and development. By including a base temperature (T o ) the DD formula limits calculations to temperatures that are relevant to growth. However, our review of growth studies shows multiple T o values in use for a given fish species. To determine how T o affects the ability of DD to explain within-population growth variation, we first show that the ability of DD to describe a growing season is robust to low values of T o . We then analyze immature length data from eight species and 85 water bodies in North America to show that there is a broad range of T o values that effectively explain growth variation. Based on these results, we argue that precise T o estimates are unwarranted for most single-population studies and recommend standard T o values (0, 5, 10, 15 °C). Standardization facilitates comparative studies and promotes the use of DD in future research. To this end, we provide equations for converting annual DD at a given T o to annual DD at a standard T o .

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 score0.212
Threshold uncertainty score0.996

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.210
Teacher spread0.191 · 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

Citations167
Published2013
Admission routes3
Has abstractyes

Explore more

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