Unifying and distinguishing diversity ordering methods for comparing communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Diversity indices have been widely used in ecological research, but they remain problematic in that different indices may rank communities inconsistently. This problem can be solved by using diversity ordering methods, the output of which is a diversity profile in graphical form for each community being compared. In this paper, we demonstrate that existing diversity ordering methods can be classified into four groups and that within‐group methods are essentially equivalent, while among‐group methods are not. We find that the intrinsic diversity‐related methods—i.e., the group containing the right tail‐sum method, the logarithmic dominance plot, the majorization method, and the k ‐dominance plot—provide the most stringent test of diversity ordering, and we recommend the right tail‐sum method as the method of preference for practical purposes.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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 it