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Record W2896559036 · doi:10.1111/eff.12451

Comparing three methods to estimate the average size at first maturity: A case study on a Curimatid exhibiting polyphasic growth

2018· article· en· W2896559036 on OpenAlexaff
Danielly Torres Hashiguti, Bruno Eleres Soares, Kyle L. Wilson, Roberta Dannyele Oliveira Raiol, Luciano Fogaça de Assis Montag

Bibliographic record

VenueEcology Of Freshwater Fish · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFish Biology and Ecology Studies
Canadian institutionsUniversity of Calgary
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsRange (aeronautics)Logistic regressionMaturity (psychological)StatisticsFish <Actinopterygii>BiologyEcologyMathematicsFishery

Abstract

fetched live from OpenAlex

Abstract The average size at first |maturity (L 50 ) is among the most important parameters for fisheries management and conservation. This paper aims to compare three different methods for its estimation. Considering a classical approach, a logistic model was used (a) by determining the gonadal stage macroscopically; and (b) by using the GSI as proxy of sexual maturity; and finally; (c) by using the length–weight relationship ( LWR ) in a theoretical approach. The proposed methods were applied using data of a detritivorous fish, Cyphocharax abramoides , monthly sampled using gill nets. Captured individuals were measured, weighed, sexed and the gonadal stage was classified macroscopically and weighed. Estimated L 50 values using the macroscopic identification, GSI approach and LWR were not different from each other considering confidence intervals. Between the three different techniques, we concluded that the analysis of the LWR in fishes with polyphasic growth presented promising results as it only requires length and weight data to be performed and estimate a L 50 within the range of both classical logistic models analysed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.041
GPT teacher head0.312
Teacher spread0.271 · 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.

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
Published2018
Admission routes1
Has abstractyes

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