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Record W2026368203 · doi:10.1139/f05-075

Food partitioning within the species-rich benthic fish community of Lake Malawi, East Africa

2005· article· en· W2026368203 on OpenAlexvenueno aff
Fabrice Duponchelle, Anthony J. Ribbink, Amulike V. Msukwa, Jorge Mafuka, Davis.S Mandere, Harvey A. Bootsma

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Biodiversity
Canadian institutionsnot available
Fundersnot available
KeywordsBenthic zoneDemersal zonePelagic zoneDemersal fishBiologyFisheryFood chainEcologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Temporal diet patterns were studied for nine species of haplochromine cichlids in Lake Malawi, East Africa, using a combination of stable isotope and stomach content analyses. The results illustrate the complementarity of the two approaches in elucidating dietary differences. Evidence was found that benthic algal production contributed to the energy requirements of offshore fishes that occupy depths between 10 and 30 m. The importance of the lake fly Chaoborus edulis as a food source for demersal fishes was confirmed, supporting the recent hypothesis that deep demersal fish production is principally sustained through the pelagic food chain rather than from benthic detrital sources. Isotopic differences were observed among species with apparently similar diets, feeding behavior, and depth preferences, suggesting that important resource partitioning exists among Lake Malawi benthic haplochromine cichlids.

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 score0.030
Threshold uncertainty score0.059

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 studies0.0010.000
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 head0.041
GPT teacher head0.194
Teacher spread0.153 · 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

Citations19
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicAquatic Ecosystems and BiodiversityFrench-language works237,207