Rangeomorph classification schemes and intra-specific variation: are all characters created equal?
Bibliographic record
Abstract
Abstract Rangeomorphs from the Ediacaran of Avalonia are among the oldest known complex macrofossils and our understanding of their ecology, ontogeny and phylogenetic relationships relies on accurate and consistent classification. There are a number of disparate classification schemes for this group, which dominantly rely on a combination of their branching characters and shape metrics. Using multivariate statistical analyses and the diverse stemmed, multifoliate rangeomorphs in Charnwood Forest (UK), we assess the taxonomic usefulness of the suite of characters currently in use. These techniques allow us to successfully discriminate taxonomic groupings without a priori assumptions or weighting of characters and to document a hitherto unrecognized level of variation within single taxonomic groups. Variation within the currently defined genus Primocandelabrum is too great to be realistically assigned to different species and may instead reflect primary character diversity, ontogenetic changes in character state or ecophenotypic variability. Its recognition cautions against generic-level diagnoses based on single differences in character state and will be crucial in understanding the mode of growth of these enigmatic organisms. Supplementary material: Data tables, definition of the characters used in the analyses, and detailed descriptions and breakdowns of methods and results are available at https://doi.org/10.6084/m9.figshare.c.3726937
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".