Plant trait covariance and nonlinear averaging: a reply to Koussoroplis et al.
Bibliographic record
Abstract
SADIE (Spatial Analysis by Distance Indices) is designed specifically to quantify patterns in spatially-referenced count-based data. It was developed for dealing with data that can be considered ‘patchy’. Such distributions are commonly found, for example, in insect populations where discrete patches of individuals are often evident. The distributions of such populations have ‘hard edges’, with patches and gaps occurring spatially. In these cases variance of abundance does not vary smoothly, but discontinuously. In this paper we outline the use of SADIE and provide free access to the SADIE software suite, establishing Rethinking Ecology as its permanent home. Finally, we review the use of SADIE and demonstrate its use in a wide variety of sub-disciplines within the general field of ecology.
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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 | 0.026 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.005 | 0.019 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.021 | 0.056 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".