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Record W2137278774 · doi:10.1186/1746-4269-7-11

Ethnobiology of snappers (Lutjanidae): target species and suggestions for management

2011· article· en· W2137278774 on OpenAlexaff
Alpina Begossi, S. V. Salivonchyk, Luciana Gomes de Araujo, Tainá B. Andreoli, Mariana Clauzet, Claudia M Martinelli, Allan GL Ferreira, Luiz EC Oliveira, Renato Azevedo Matias Silvano

Bibliographic record

VenueJournal of Ethnobiology and Ethnomedicine · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsUniversity of Manitoba
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsLutjanidaeFisheryFishingEthnobiologyArtisanal fishingFisheries managementGeographyPopulationTraditional knowledgeSocioeconomicsIndigenousEcologyBiologyFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

In this study, we sought to investigate the biology (diet and reproduction) and ethnobiology (fishers knowledge and fishing spots used to catch snappers) of five species of snappers (Lutjanidae), including Lutjanus analis, Lutjanus synagris, Lutjanus vivanus, Ocyurus chrysurus, and Romboplites saliens at five sites along the northeast (Riacho Doce, Maceió in Alagoas State, and Porto do Sauípe, Entre Rios at Bahia State) and the southeast (SE) Brazilian coast (Paraty and Rio de Janeiro cities at Rio de Janeiro State, and Bertioga, at São Paulo State.).We collected 288 snappers and interviewed 86 fishermen. The stomach contents of each fish were examined and macroscopic gonad analysis was performed. Snappers are very important for the fisheries of NE Brazil, and our results indicated that some populations, such as mutton snapper (L. analis) and lane snapper (L. synagris), are being caught when they are too young, at early juvenile stages.Local knowledge has been shown to be a powerful tool for determining appropriate policies regarding management of target species, and artisanal fishermen can be included in management processes. Other suggestions for managing the fisheries are discussed, including proposals that could provide motivation for artisanal fishermen to participate in programs to conserve resources, such as co-management approaches that utilize local knowledge, the establishment of fishing seasons, and compensation of fishermen, through 'payment for environmental services'. These suggestions may enhance the participation of local artisanal fishermen in moving to a more realistic and less top-down management approach of the fish population.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.035
GPT teacher head0.263
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations55
Published2011
Admission routes1
Has abstractyes

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