EFFECTS OF DATA CATEGORIZATION ON PALEOCOMMUNITY ANALYSIS: A CASE STUDY FROM THE PENNSYLVANIAN FINIS SHALE OF TEXAS
Bibliographic record
Abstract
Abstract Paleocommunity research efforts have explored a multitude of faunal assemblages using a wide range of sampling and analytical methods to infer a paleoecological signal. Here, we derive six secondary datasets from a single stratigraphic series of faunal assemblages in the Finis Shale (Pennsylvanian) of Jacksboro, Texas, USA, using a variety of data categorization decisions (i.e., abundance versus calcified biomass, all taxa versus selected indicator taxa, and generic versus higher clade resolution). Biomass- and abundance-derived datasets were not significantly different in terms of evenness, Shannon's information index, or Simpson's diversity index. Using Bray-Curtis and nonmetric multidimensional scaling ordinations, with Sorenson and relative Sorenson distance measures, ordination axis scores of the six derived datasets were all significantly correlated with one another, suggesting little difference in their respective paleoecological signals. Three potential explanations for this consistent paleoecological signal, regardless of which data categorizations are employed, include: (1) the dominance of a few brachiopod taxa overwhelmingly influenced the community structure, (2) relatively constrained environmental conditions limited community variation, and (3) low variation in specimen size minimized potential differences among abundance and calcified biomass categorizations. We suggest that other datasets with greater diversities, greater evenness, or from a wider range of paleoenvironments might not show this consistency. Thus, to the degree possible and appropriate, paleoecological investigators should test the effects of these data categorization decisions on a paleoecological signal, regardless of the analytical method employed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".