Revealing rangeomorph species characters using spatial analyses
Bibliographic record
Abstract
Rangeomorphs dominate the Ediacaran Avalonian macrofossil assemblages of Charnwood Forest, UK (~562Ma). However, their unfamiliar fractal architecture makes distinguishing phylogenetically reliable characters from intraspecific features difficult. Fortunately, spatial analysis of large in-situ populations offers an independent means of assessing their taxonomy. Populations of a single biological species are likely to exhibit similar spatial distributions due to their shared responses to the biological and ecological processes acting upon them. As such, spatial analyses can be used to interrogate which are the most taxonomically deductive characters in similar species. We used Random Labelling Analyses to investigate the presence/absence of characters of Primocandelabrum boyntoni, P. aethelfalaedia and P. aelfwynnia on the North Quarry ’B’ surface. The resultant spatial distributions were compared to observed characters using goodness-of-fit tests to determine which characters were associated with unique populations, and which were found across multiple populations. We found that P. boyntoni and P. aelfwynnia had statistically indistinguishable character distributions, suggesting that they represent a single biological species, and that they exhibited significantly different distributions to P. aethelfalaedia, suggesting that there are two (rather than three) Primocandelabrum species present on the B surface. Furthermore, we found that the distribution of Concealed versus Unconcealed 1st order branches across all specimens exhibited significantly different, density-dependant behaviour, with Unconcealed branching occurring in areas of higher density populations, and Concealed branching occurring in the lower Primocandelabrum density areas. We speculate that unconcealed branches may have been a response to the reduced availability of resources in higher density areas, implying rangeomorphs were capable of ecophenotypic responses.
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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 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".