Guilty as charged: Nile perch was the cause of the haplochromine decline in Lake Victoria
Bibliographic record
Abstract
Debate on the contribution of Nile perch (Lates niloticus) to the demise of Lake Victoria’s 500+ endemic haplochromine cichlids centers around the “top-down” and “bottom-up” hypotheses. The former suggests Nile perch destroyed the haplochromines, causing the disruption of food chains and nutrient cycling and so initiating the accelerated eutrophication of the lake. The latter proposes that haplochromines suppressed Nile perch by preying on its eggs and fry or competing with juveniles for food. A recent paper argued that accelerated eutrophication caused by a climatic event led to their collapse, allowing Nile perch to explode. However, the Nile perch population grew before haplochromines decreased, and they only collapsed once Nile perch biomass exceeded theirs. The chronology indicates that accelerated eutrophication of the lake followed rather than preceded the haplochromine collapse, suggesting that eutrophication was not its cause. A size-selective predation model developed to support the bottom-up hypothesis is discussed in light of existing data, but does not support the bottom-up hypothesis. It was concluded that the top-down model better fits the data and conforms to the law of parsimony.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".