Modeling trait-based environmental filters: Bayesian statistics, information theory and the Maximum Entropy Formalism
Bibliographic record
Abstract
The previous chapter described a verbal model of community assembly: trait-based environmental filtering. This conceptual model views the environmental conditions of a site as a series of “filters” consisting of various selection pressures. These selection pressures reflect the probability of a given species being able to immigrate into the site and then to survive and reproduce. Such demographic probabilities vary between species because each species has a unique set of functional traits and because such functional traits bias these probabilities. The problem with this verbal model is that we don't know how to formally link traits with such probabilities. We now have to build such formal links. With a chapter title including the words “Bayesian statistics”, “information theory” and the “Maximum Entropy Formalism” you might be tempted to skip to the next one. Please resist this understandable temptation because, although this chapter is more statistical than ecological, it will develop the statistical and mathematical methods upon which the ecological theory is based. If you are a typical reader then you will be reading this book in order to learn about the links between organismal traits and ecological communities. If so then, for you, the theoretical content of the book is only a means to an end. In order to convince you that reading this chapter will be worthwhile let's be clear about what we are trying to accomplish and, equally important, what we are trying to avoid.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".