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Record W2488051352 · doi:10.5772/45635

New Advances and Contributions to Fish Biology

2012· book· en· W2488051352 on OpenAlexfundno aff
Hakan Türker

Bibliographic record

VenueInTech eBooks · 2012
Typebook
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaUniversitat Autònoma de BarcelonaUniversitat de BarcelonaCorporación de Fomento de la ProducciónUniversidad de Santiago de Chile
KeywordsFish <Actinopterygii>BiologyVariety (cybernetics)EcologyPopulationPopulation biologyPopulation dynamics of fisheriesCognitive sciencePsychologyComputer scienceSociologyFisheryArtificial intelligence

Abstract

fetched live from OpenAlex

This book provides an understanding on a large variety of related topics in fish biology. The further development on molecular and cellular biology and ecology leads to assimilate the newer scientific knowledge in this area. Leading research works from around the world are brought together in this book to produce a valuable source of reference for teachers, researcher, and advanced students of biological science. The fist three chapters of this book give a general description of the complex biology of the immune response. Detailed descriptions were also included on understanding of cytokine regulation in teleost immune system. The second three chapters provide information on the environmental stressors on the responses of freshwater fish across molecular to population level. Then, the following two chapters review two special topics; the roles of the atrium and the ventricle across teleost species and the tracer methodologies on the measurements of carbohydrate metabolism. The last chapter discusses the variables that are involved in the feeding behavior of a predatory freshwater fish species.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.052
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0520.026

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 head0.008
GPT teacher head0.262
Teacher spread0.253 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations79
Published2012
Admission routes1
Has abstractyes

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