Identification of the Oligocene to early Miocene loricariid catfish †<i>Taubateia paraiba</i> as a member of the Rhinelepinae
Bibliographic record
Abstract
Correct identification of fossil taxa is immensely important for dating molecular phylogenies and understanding when and how quickly modern biodiversity evolved. Fossils that are available for a clade of interest and can be directly incorporated in the phylogenetic analysis are considered primary sources of time calibration, whereas calibrations inferred from other studies are secondary (Arroyave et al., 2013). Studies of taxonomic groups that lack fossils must either expand their analyses to include fossilized outgroup lineages, use secondary calibrations, or use more problematic primary calibrations, e.g., vicariant geologic events. The use of vicariant geologic events to calibrate phylogenies poses the risk of circular reasoning, because the goal of many such studies is to determine how geologic events have affected diversification. Near et al. (2012) argued that fossil calibrations external to clades of interest, but still within the broader Actinopterygian (ray-finned fishes) tree, could be used as means of calibrating a generalized molecular clock, but internal calibrations are still valuable for refining such inferences (Arroyave et al., 2013).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".