A Review of the Tetrapod Track Record in China, with Special Reference to Type Ichnospecies: Implications for Ichnotaxonomy and Paleobiology
Bibliographic record
Abstract
Abstract “Splitting” and “lumping” are perpetual problems in vertebrate, especially dinosaur, ichnotaxonomy. Chinese dinosaur ichnotaxonomy, which began in 1940, provides a series of interesting case studies, highlighting the dual problems of historical and dubious ichnotaxonomy. Chinese Mesozoic tetrapod track types have been placed into 63 ichnospecies (one Triassic, 28 Jurassic, and 34 Cretaceous), exclusive of other, non‐type ichnospecies or ichnotaxa identified from China. Fifty‐two (∼83%) of these 63 tetrapod ichnospecies were placed in monospecific ichnogenera. At the ichnogenus level, we prune—either by recognizing nomina dubia or by synonymy—17 from the list of 53 dinosaurian ichnogenera (a 32% reduction), leaving 36 ichnotaxa that we consider valid. Most of the cuts affect Jurassic theropod ichnotaxa, which are reduced from 23 to only nine because most ichnogenera are subjective junior synonyms of Grallator and Eubrontes. Fewer Chinese Cretaceous ichnotaxa (only six of 21 ichnogenera) are obvious nomina dubia or subjective synonyms, suggesting greater east Asian endemism during this time. Because ichnospecies differences are subtle, we provisionally retain ichnospecies as valid pending detailed comparative analyses of congeneric ichnospecies. This synthesis is long overdue and is necessary to address problems of historical and provincial ichnotaxonomy, which severely hamper comparisons of tetrapod ichnofaunas in space and time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".