Fidelity and diagnostic species concepts in vegetation classification in the Rocky Mountains, northern Utah, USA
Bibliographic record
Abstract
The concepts of diagnostic species and fidelity have been used frequently in European phytosociology but rarely in North American vegetation classification. We developed a classification of the vegetation of a mountainous area of northern Utah and compared the diagnostic species approach with the indicator-species approach of habitat type classification sensu Daubenmire prevailing in the U.S. Interior West. A total of 157 forest and nonforested plots were described by vascular plants and basic environmental factors. Clustering with RandomForest classification and ordination reduced the original number of plots to 26 meaningful vegetation units. Of these 26 units, 22 were strong, having four or more faithful species. Four units were weak, having less than three faithful species. We identified species diagnostic of particular vegetation units that are potentially useful for recognition of these units in the field. We proposed vegetation types at the level of vegetation alliances and associations, and correlated them with environmental factors. We found our vegetation units to be more strongly associated with the underlying environment than major habitat types sensu Daubenmire. Our approach to classification has the potential to directly link vegetation with the physical environment and could be the basis for a substantial improvement of vegetation classification in the central Rocky Mountains.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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 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".