Using Fuzzy Spatial Relations to Control Movement Behavior of Mobile Objects in Spatially Explicit Ecological Models
Bibliographic record
Abstract
Spatial relations are fundamental to the modeling of spatially explicit ecological processes. An information-based framework relates geographical information system (GIS) data to object representations of individual mobile animals. It is used to organize a discourse on the incorporation of fuzzy logic into spatially explicit, individual-based ecological models of animal movement across a landscape of habitat. Spatial relations such as proximity, direction, overlap, and containment are used in a spatial reasoning process to control movement of individual animal objects over a landscape. It is shown that an animal’s perceptual range can be specified as a function of proximity to an animal object and used as a fuzzy spatial constraint region for object queries operating over a landscape database. The role of fuzzy relations in models of habitat evaluation is addressed. The potential use of fuzzy spatial relations in modeling movement behavior primarily associated with foraging is demonstrated. It is shown that spatially explicit ecological modeling is a complex domain rich in the potential for intelligent applications using fuzzy spatial relations
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".