Bibliographic record
Abstract
The broad objective of multivariate data analysis in biology is to summarize associations among species (the dependent or response variables), and to elucidate species responses to one or more environmental factors (the independent or predictor variables). This objective is achieved by reducing the dimensionality of variable space to an efficient, low-dimensional summative model of the underlying data structure that reflects the coordinated response of species to environmental factors. While multivariate methods have proven indispensable for analyzing both experimental and survey data in the biological sciences, considerable confusion persists regarding the selection of appropriate analytical strategies. The selection of an appropriate analytical strategy, which includes important decisions regarding data transformation, variable standardization and methodological approach, should be based on fundamental considerations of statistical appropriateness, data structure, and study objectives. Unfortunately, past and more recent assessments of multivariate analytical strategies have been based largely on empirical models of questionable relevance. This empirical approach has led to misleading recommendations and erroneous generalizations regarding the relative efficacy of the available multivariate methods. This paper dispels these misleading recommendations and provides some general guidelines for selecting appropriate data transformations, variable standardizations and methodological approaches in the multivariate analysis of biological data. Key words: Ordination, canonical analysis, co-inertia analysis, principal component analysis, correspondence analysis, non-metric multidimensional scaling
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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 | 0.045 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".