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Record W2605335792 · doi:10.1051/alr/2017010

Length–weight relationships for 22 crustaceans and cephalopods from the Gulf of Cadiz (SW Spain)

2017· article· en· W2605335792 on OpenAlexaff
María Ángeles Torres, Y. Vila, Luis Silva, Juan Acosta, Fernando Ramos, Maria Lourdes D. Palomares, Ignacio Sobrino

Bibliographic record

VenueAquatic Living Resources · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCrustaceanOceanographyFisheryBiologyGeographyGeology

Abstract

fetched live from OpenAlex

Life history traits are available for many fish species in different regions, but less so for invertebrates such as cephalopods and crustaceans, though, they are increasingly needed for implementing an ecosystem-based approach. Recent food web modelling in the Gulf of Cadiz has identified invertebrates as keystone groups. However, information on life history traits of such groups remains incomplete in this region. To fill this knowledge gap, we report length-weight relationships for 12 cephalopods and 10 crustaceans collected in the Gulf of Cadiz from 2009 to 2013. This study reports, for the first time, life history traits of nine species in the area (Chlorotocus crassicornis, Pasiphaea sivado, Plesionika heterocarpus, Plesionika martia, Processa canaliculata, Solenocera membranacea, Allotheutis media, Sepia orbignyana and Sepietta oweniana). For each species, length-weight relationships, minimum and maximum lengths, mean weights, and depth ranges are presented. Overall, the results revealed that all species showed negative allometric growth (hypoallometry), except P. sivado, the only species showing an isometric growth pattern. We expect that this study will contribute to link sustainable fisheries with biodiversity conservation goals enabling the implementation of operational ecosystem-based management in the Gulf of Cadiz.

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)0.003
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 score0.132
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.258
Teacher spread0.227 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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