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Record W6990351927

The decline of the native fisheries of Lakes Kyoga and Victoria and the impact of Nile perch, Lates niloticus on these fisheries

2021· book· en· W6990351927 on OpenAlexfundno aff

Bibliographic record

VenueAquaDocs (United Nations Educational, Scientific and Cultural Organization) · 2021
Typebook
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Biodiversity
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsLatesHabitatNettingCompetition (biology)Introduced speciesFish stockPredatorFishing
DOInot available

Abstract

fetched live from OpenAlex

There has been a decline almost to the total disappearance and in some cases apparently the extinction of most of the native fish species of Lakes Victoria and Kyoga since the development of the fisheries of these lakes begun at the beginning of this century. The Nile perch, Lates niloticus, a large voracious predator which was introduced into these lakes about the middle of the century along with several tilapiine species is thought to have depleted stocks of other fish. But other factors, such as overfishing, changes in the habitat which can result in fish kills or affect breeding and recruitment, plus competition with other species, appear to have contributed to the diminution in the stocks of other fish.The available information indicates that by the time the Nile perch was established, the stocks of the native tilapiine species had been reduced by over fishing. The Labeo victorianus fishery had similarly been destroyed by intensive gill netting of gravid individuals on breeding migrations. L. niloticus is however, capable of depleting the stocks of species which have disappeared and could have consumed the remnants - thus preventing their recovery. It is also directly responsible for the decline in the populations of the haplochromine cichlids which were abundant over most of these lakes when it was established. The native tilapiine species were also affected by the introduced species which have similar ecologicalrequirements.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.003
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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