A TERATOLOGICAL PYGIDIUM OF THE UPPER CAMBRIAN TRILOBITE<i>EUGONOCARE</i>(<i>PSEUDEUGONOCARE</i>)<i>BISPINATUM</i>FROM THE MACHARI FORMATION, KOREA
Bibliographic record
Abstract
Abundant examples of exoskeletal abnormalities have been known in various trilobites since Portlock's (1843) first report on a teratological pygidium of Phillipsia ornata (cf. Babcock, 2000). Owen (1985) and Babcock (1993) recognized three types of trilobite malformations: healed injuries, teratological conditions, and pathological conditions. It is not always easy, however, to distinguish between the various types of malformations, especially in case of teratological or pathological conditions. In general, healed injuries are considered to have resulted from trauma during molting (Walcott, 1883; Whittington, 1956; Henningsmoen, 1975; Snajdr, 1981; Owen, 1983; Ramsköld, 1984) or wounds by predatory attack (Ludvigsen, 1977; Rudkin, 1979; Ŝnajdr, 1979, 1981; Owen, 1985; Conway Morris and Jenkins, 1985; Babcock, 1993; Pratt, 1998). Many healed injuries are indicated by broken spine stumps, indented and cicatrized edges of exoskeletons, and callused or regenerated exoskeleton around the broken surface (Ŝnajdr, 1981; Owen, 1983; Babcock, 1993). Teratological conditions are represented primarily by the irregular development of glabellar lobes, cephalic borders, and pygidial border spines, and anomalous number of segments on the thorax and pygidium. For example, Owen (1980) described a teratological cranidium of Calyptaulax norvegicus with two additional lateral glabellar lobes between the l p and 2p lobes. On the other hand, pathological conditions are marked by gall-like swellings and vermiform borings on exoskeletons that are thought to have been caused by diseases or parasitic infestation (Lochman, 1941; Ŝnajdr, 1978; Conway Morris, 1981; Owen, 1985).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".