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African fossil fish

2017· book-chapter· en· W2939737112 on OpenAlexaff
Olga Otero, Alison M. Murray, Lionel Cavin, Gaël Clément, Aurélie Pinton, Kathlyn M. Stewart

Bibliographic record

VenueIRD Éditions eBooks · 2017
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicFish Biology and Ecology Studies
Canadian institutionsCanadian Museum of NatureUniversity of Alberta
Fundersnot available
KeywordsGeographyFish <Actinopterygii>LungfishFossil RecordPaleontologyAridificationLand bridgeFaunaStructural basinGeologyEcologyFisheryBiologyAridBiological dispersal

Abstract

fetched live from OpenAlex

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.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.177
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.219
Teacher spread0.181 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2017
Admission routes1
Has abstractyes

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