3D Chemometric Model Simulating the Acitheca Polymorpha Frond: Implications for Reconstructing Carboniferous Ferns (Marattiales, Canada)
Bibliographic record
Abstract
Abstract Reported are results of an initial approximate imitation of a Carboniferous fern frond, i.e., marattialean Acitheca polymorpha (Schimper), Middle Pennsylvanian Sydney Coalfield, Canada. The simulation experiment is based on the analysis of 14 infrared spectra obtained by means of Fourier Transform Infrared spectroscopy from four detached fragments of sterile polymorphic penultimate-pinna compressions. The calculated relative, semi-quantitative, chemical data from the infrared spectra are the input for principal component analysis deriving a 3D (three-dimensional) chemometric model. To interpret it, the four specimens are placed in hypothetical-frond positions simulating a tripinnate frond, based on diminishing penultimate-rachial widths from 1-mm (distal) to 10-mm (proximal). Hypothetical conclusions include position-dependent chemistries, specifically that of opposing trends of aromaticity vs. aliphaticity in pinnules-rachises. This, in turn, would suggest potential for (i) fern-frond reconstruction, and (ii) for determination of a most likely frond position of fragmentary specimens by “chemical classification”; the predictive aspect. However, further experimental refinement is necessary particularly based on larger frond segments to confirm or disconfirm the overall hypothetical results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".