The Maximum Entropy Principle to predict foragerspatial distributions: an alternate perspective formovement ecology
Bibliographic record
Abstract
Abstract The Maximum Entropy Principle (MaxEnt) is a powerful inference principle that allows to determine the distribution that describes a system on the basis of the information available, usually in the form of averages of observables (random variables) of interest for the system, and the assumption of maximal ignorance (maximum entropy) beyond the stated prior information.In this work we focus on the use of MaxEnt in the context of spatial ecology for building theory to predict equilibrium foraging distributions. This represents a new application of MaxEnt and a novel approach to compute spatial foraging distributions, which is able to incorporate mechanisms such as resource-depletion, optimallity of the foraging strategy, travel costs, information uncertanity, and inter-specific and intra-specific competition into the statistical inference. Our results show how our model predictions can resemble both, the predictions of optimal and random foraging, and give a simple quantitative way to connect these two extreme foraging behaviours. In addition, our work show the capability of modelling using energy and entropy arguments to relax most of the basic assumptions of the Ideal Free Distribution of ecology to expand its range of applicability.Overall, we show the power of MaxEnt to build theory and to relate existing models in spatial ecology by use of a universal principle. We further discuss the potential applicability of MaxEnt to build theory in other contexts of ecology, such as to formulate population dynamics models, and the potential use of the dynamic form of MaxEnt, i.e. the Maximum Caliber Principle, to develop further theory on dynamical systems in spatial 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".