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Record W2942902577 · doi:10.3390/d11050071

Utility of Condition Indices as Predictors of Lipid Content in Slimy Sculpin (Cottus cognatus)

2019· article· en· W2942902577 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDiversity · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFish Biology and Ecology Studies
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNew Brunswick Innovation FoundationUniversity of New Brunswick
KeywordsSculpinFish <Actinopterygii>MathematicsStatisticsBiologyFishery

Abstract

fetched live from OpenAlex

Slimy sculpin (Cottus cognatus) are increasingly being used as indicator species. This has primarily entailed measuring their condition, the assumption being that condition can be used as a surrogate for lipid content. While there is evidence to suggest this assumption is applicable to some fish, it has yet to be validated for C. cognatus. Further, there are several means by which one may calculate condition, the most commonly employed of which are indirect measurements of lipid content (namely, Fulton’s K, somatic K (Ks), and Le Cren’s relative condition factor (Kn)). We compared the ability of each of these morphometric indices to predict whole-body lipid content in C. cognatus. There was a moderate degree of evidence that Fulton’s K, Ks, and Kn are reliable predictors (Ks and Kn in particular). Of the latter we recommend Kn be used because, unlike Ks, it does not require that fish be killed. And while Fulton’s K did not perform quite as well, we consider it a sufficient substitute if the data necessary to calculate Kn are unavailable.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.204
Teacher spread0.175 · 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