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Fishing down and fishing hard: ecological change in the Nile perch of Lake Nabugabo, Uganda

2009· article· en· W2150119444 on OpenAlexafffund
Jaclyn Paterson, Lauren J. Chapman

Bibliographic record

VenueEcology Of Freshwater Fish · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Biodiversity
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsLatesFishingFisheryPerchWetlandCatch per unit effortEcologyGeographyThreatened speciesHabitatBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract – Fishing is a potent ecological force. In Lake Victoria, East Africa, Nile perch, Lates niloticus contributes to a multi‐million dollar fishing industry but is threatened by over‐exploitation. We quantified spatial and temporal trends in the distribution, diet and size of Nile perch in Lake Nabugabo, Uganda, a satellite of Lake Victoria. From 1995 to 2007, we detected a decline in catch per unit effort of Nile perch, a shift in their distribution and diet, and a decrease in their body size. A greater proportion of Nile perch were found near wetland ecotones than in the 1990s. This may reflect intensive size‐selective fishing in open waters, and encroachment of Vossia cuspidata, an emergent macrophyte that has expanded across the lakeshore. Results highlight the strength of fishing in inducing phenotypic changes in target stocks as well as large‐scale changes to the aquatic community and are of value in understanding changes in Lake Victoria.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.023
GPT teacher head0.222
Teacher spread0.199 · 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

Citations38
Published2009
Admission routes2
Has abstractyes

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