Statistical Measures of Fidelity Applied to Diagnostic Species in Plant Sociology
Bibliographic record
Abstract
The idea of a diagnostic species is an important concept in plant sociology. However, since over a century ago, when the term “association” was introduced, the identification of diagnostic species has been among the most controversial topics in phytosociological practice. With the aim of promoting methodological standardization in plant sociology, this paper addresses: 1) the need to distinguish between the concepts and methods involved in the definition of syntaxa (analysing relevés, characterization, diagnosis, naming and typification), and 2) the need to support and improve existing syntaxonomical classification schemes using statistical measures of fidelity to identify diagnostic species. The phytosociological literature describes numerous different approaches to the designation of diagnostic species. Here, we examine two such approaches to determine diagnostic species using as an example the class Atriplici julaceae-Frankenietea palmeri within the context of a data set of 5092 relevés taken of coastal plant communities distributed along the Pacific rim of North America. Diagnostic species were determined using both the phi-coefficient of association to detect differential species and the Ochiai index to designate character species. Our findings support the results obtained by combining classic phytosociological methods (expert knowledge, rearrangement of relevé tables, presence tables, etc.) with clustering methods.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".