A MULTIVARIATE STATISTICAL ANALYSIS OF THE EDIACARAN RANGEOMORPH TAXA <i>BEOTHUKIS</i> AND <i>CULMOFRONS</i>
Bibliographic record
Abstract
ABSTRACT The Avalon assemblage of Newfoundland, Canada contains abundant fossils of enigmatic soft-bodied Ediacaran organisms, many with remarkable preservation. One of the most numerically dominant groups of organisms in the assemblage is the Rangeomorpha, a frondose clade characterized by self-similar, repeating branching architecture known worldwide from rocks of Ediacaran age. Variations in branching characters and gross morphology have historically been used to divide this group, but there has been little consistency in taxonomic approach to the Rangeomorpha, concomitantly there are conflicting opinions that have resulted in some overlapping taxonomic diagnoses. Here we investigate one such taxonomic dispute, the Beothukis/Culmofrons problem. The two genera were recently synonymized into Beothukis based on the assertion that some characters were of different taxonomic rank than others. Subsequent debate has focused on which taxonomic characters displayed by the Rangeomorpha should be used for genus- and species-level subdivision. To test the validity of using continuous versus discrete characters in rangeomorph taxonomy we use a combination of morphometrics and statistical analysis to identify natural clusters within our specimen dataset which was collected from Beothukis sensu lato including material that was, until recently, attributed to Culmofrons. The results of the cluster assignment validates the differentiation between Beothukis mistakensis and Beothukis (Culmofrons) plumosa, but cannot—in isolation—be used to determine at what taxonomic rank that distinction should be made. We demonstrate a considerable degree of variation within Beothukis and Culmofrons, which has not yet been recorded for unifoliate rangeomorph taxa.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".