A combined approach to identify isolated theropod teeth from the Cenomanian Kem Kem Group of Morocco: cladistic, discriminant, and machine learning analyses
Bibliographic record
Abstract
The Kem Kem Group of Southeastern Morocco, North Africa, is well known for theropod remains, especially isolated teeth. Here, a collection of isolated theropod teeth is assessed for diversity using a combination of linear discriminant, phylogenetic, and machine learning analyses for the first time. The results confirm earlier studies on Kem Kem theropod diversity, with teeth referred to Abelisauridae, Spinosaurinae, and Carcharodontosauridae. A single tooth is ascribed to a non-abelisauroid ceratosaur or a megaraptoran and may represent the enigmatic averostran Deltadromeus. Spinosaurine teeth are clearly differentiated by all three methodologies, whereas abelisaurid and carcharodontosaurid teeth could only be distinguished by the machine learning and phylogenetic analyses. This study shows that a combination of independent methods is most effective at providing strong evidence on theropod dental diversity in a particular assemblage, and that cladistic and machine learning analyses are the most reliable approaches to identify isolated dinosaur teeth. The methodology used here is likely to yield results in other dinosaur assemblages where isolated teeth are more abundant than body fossils.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".