African fossil fish
Bibliographic record
Abstract
This chapter illustrates how palaeontology helps to retrace the evolution of African fish, and what can be learned about their palaeobiogeography, adaptation and ancient environment. The first part (1. Africa and fish through geological time) is a presentation of the fish fossil record within the environmental contexts that prevailed in continental Africa through geological time, followed by a focus on the fossils themselves. Using the case study of African characiform fossils, the second part (2. Which fossils? Case study of the African characiforms) illustrates the various kinds of fossils and fossil remains that can be recovered and explains what information fossils can give us. The third section (3. Old groups, old cradles!) focuses on biogeographical information, using the example of one emblematic archaic fish: the lungfish. The fourth part (4. When a marine fish adapts to freshwater) tracks the invasion and extinction of stingrays in the Turkana basin and discusses the still-debated origin of the Nile perch in Africa, to demonstrate the links between fossil fish and their environment, thus giving us information on long-term environmental change. The last section (5. Neogene changes in the African ichthyofauna) is a conclusion concerning the impact of the most recent long-term environmental changes which deeply modified Africa fish faunas, notably the uplift in Eastern Africa and the Saharan aridification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.013 |
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".