MétaCan
Menu
Back to cohort

Strength in diversity: fish sanctuaries and deep‐water pools in Lao PDR

2006· article· en· W2105396558 on OpenAlexaff
Ian G. Baird

Bibliographic record

VenueFisheries Management and Ecology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFish <Actinopterygii>Diversity (politics)FisheryGeographyBiologyEcology

Abstract

fetched live from OpenAlex

Abstract Fish sanctuaries are types of aquatic protected areas common throughout the landlocked country of Lao PDR. The key to their successful implementation in Laos appears to be local support. Therefore, they fit well within the context of community‐based fisheries/co‐management programmes. Fish sanctuaries are almost always established in deep‐water pools, which serve as important dry season refuges for fish. The best‐known fish sanctuaries in Laos are known as Fish Conservation Zones (FCZs), and are found in the mainstream Mekong River in Khong district, Champasak province, southern Laos. In Khong, there is increasing evidence that FCZs established by villagers, but with government support, are important places of concentration for various fish species. FCZs can benefit fish stocks, especially relatively sedentary species, but also highly migratory ones. FCZs are not panaceas for solving all the fisheries problems in Laos or the Mekong River Basin, but they represent important management tools that should not be overlooked.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.160
Teacher spread0.155 · 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 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

Citations68
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueFisheries Management and EcologySame topicFish Ecology and Management StudiesFrench-language works237,207