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Abundance and distribution of fish in the lower Mnembo River, Malawi‐Mozambique

2006· article· en· W2053570997 on OpenAlexafffund
Leanda Marie Delaney, Daniel Jamu, Christine Campbell

Bibliographic record

VenueAfrican Journal of Ecology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFish Biology and Ecology Studies
Canadian institutionsMemorial University of Newfoundland
FundersConsortium of International Agricultural Research CentersMemorial University of Newfoundland
KeywordsBarbusLabeoAbundance (ecology)FisheryBiomass (ecology)Population dynamics of fisheriesPopulationEcologyDrainage basinGeographyFish migrationBiologyFish <Actinopterygii>Cyprinidae

Abstract

fetched live from OpenAlex

Abstract Endorheic Lake Chilwa is one of the most productive lakes for fisheries in Africa. High human population densities and agricultural practices in the Chilwa catchment have been linked to observed declines of the annual fish catch in the lake. The Mnembo River is a major inflow into Lake Chilwa which has received little scientific study to date. In 2003/2004, fish diversity, abundance and distribution were monitored monthly at three sites in the Mnembo River. Twelve fish species were collected, a subset of the fourteen fish species present in Lake Chilwa. Although Barbus spp. were the most abundant species sampled in the Mnembo, total biomass was higher for Labeo cylindricus (6709 and 10,434 g, respectively). Total biomass of Pareutropius longifilis (1741 g) and Brycinus imberi (1174 g) were also high in the river. Catches of Barbus and Labeo were highly correlated ( r = 0.763). Compared with other inflows into Lake Chilwa (Likangala and Domasi), the Mnembo River appears to be less adversely influenced by human populations, with high abundance of Barbus and strong presence of the riverine species Labeo , Pareutropius and Brycinus .

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)0.000
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 score0.011
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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

Citations7
Published2006
Admission routes2
Has abstractyes

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