Limitations of entropy maximization in ecology: a reply to Haegeman and Loreau
Bibliographic record
Abstract
Haegeman and Loreau published a paper that is primarily a criticism of a maximum entropy model of trait‐based community assembly (by Shipley et al.) and purports to show the limitations of this method in ecology. However, they misunderstood the basic purpose, logic and justification of the maximum entropy formalism and, because of this, leveled criticisms of Shipley et al. that are unfounded. Part of the confusion can be traced to sloppy presentation of the underlying approach in Shipley et al. The confusion arises because maximum entropy models are justified based on information theory and Bayesian logic while the interpretation that Haegeman and Loreau present is based on substantive empirical assumptions about microstate allocations and a combinatorial argument that do not apply to maximum entropy models and which I do not apply to my model in particular.
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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 | 0.028 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.006 | 0.026 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.023 | 0.045 |
| 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".