Formal taxa, species groups, and perception of the genus Diplodactylus (Reptilia: Gekkonidae)
Bibliographic record
Abstract
Genera with large numbers of species present particular difficulties; the analysis of relationships, of included taxa may be roblematic. One attempt to aproach this problem involves the reco of clusters of species tIat may be informally assembgd into species groups. The problems tE:g the recognition of such assemblages may induce are exlored. It is not that the species groups, as originail formulated, are problematic as they are initiafy erected to demarcate clusters withm an imperfectly known phylogeny of a supposedly monophyletic group. These species groups, however, tend to become recognized as “taxa” rather than operationaf clusters and as such tend to influence the approach to the inclusive taxon taken by subsequent workers. Rather than testing the concept of the species grous, there is a tendency to retain them and to insert other groups between them that do not exactly fit the original scheme. The establishment of species groups, first used to clarify a complex situation, has teen a source of problems for subseuent workers. The history of this aproach is traced for the gekkonid genus Diplodactylus and the problems that have arisen are outlined.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".