The importance of scaling of multivariate analysis in ecological studies
Bibliographic record
Abstract
Principal component scores are used widely in summarizing information from ecological data sets, but little attention has been given to the scaling methods producing them. We describe the most common scales and how their properties can contribute dramatically to pattern interpretation. We applied morphological null models to present a case where a method commonly applied by community ecologists shows contradictory results (rejection or not) depending on the particular scaling choice in producing PC scores. Our intention is not to condemn the use of PC scores but to call attention to the fact that different scaling methods emphasize different aspects of the data.The contradictions in results found by our null models, and possibly in the literature, are simply the consequence of different hypotheses being tested. In order to provide general guidelines, we discuss the adequacy of different scaling methods when analyzing particular ecological situations.
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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 | 0.217 | 0.559 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".