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Record W2044896391 · doi:10.1139/f01-159

The role of measurement error on the interpretation of otolith increment width in the study of growth in larval fish

2001· article· en· W2044896391 on OpenAlexfundvenueno aff
Pierre Pepin, John F. Dower, Hugues P. Benoît

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOtolithLarvaObservational errorStatisticsGrowth rateFish <Actinopterygii>Animal scienceBiologyPopulationEnvironmental scienceMathematicsEcologyFisheryDemography

Abstract

fetched live from OpenAlex

Patterns in increment widths and the associated measurement error were studied in the otoliths of radiated shanny (Ulvaria subbifurcata) larvae sampled from the field. Mean increment widths increased with larval age, whereas the relative measurement error decreased. Furthermore, the level of serial correlation in increment widths showed a clear increase with age. When measurement error was taken into consideration, the pattern of serial correlation indicates that an individual's growth increments did not show the effect of changes in local environmental conditions for at least 3 days, independent of age. Only after that period can we hope to detect the effect of environmental influences on increment widths (and by inference growth rates). The patterns of measurement error and serial correlation will have direct impacts on our ability to find the influence of environmental conditions on growth rates of individual larvae and determine patterns of selective loss in a population of larval fish.

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)0.050
metaresearch head score (Gemma)0.242
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.266

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.242
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.032
GPT teacher head0.241
Teacher spread0.209 · 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

Citations37
Published2001
Admission routes2
Has abstractyes

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