Seeing the Forest Despite the Trees: Large Scale Spatial-Temporal\n Decision Making
Bibliographic record
Abstract
We introduce a challenging real-world planning problem where actions must be\ntaken at each location in a spatial area at each point in time. We use forestry\nplanning as the motivating application. In Large Scale Spatial-Temporal (LSST)\nplanning problems, the state and action spaces are defined as the\ncross-products of many local state and action spaces spread over a large\nspatial area such as a city or forest. These problems possess state\nuncertainty, have complex utility functions involving spatial constraints and\nwe generally must rely on simulations rather than an explicit transition model.\nWe define LSST problems as reinforcement learning problems and present a\nsolution using policy gradients. We compare two different policy formulations:\nan explicit policy that identifies each location in space and the action to\ntake there; and an abstract policy that defines the proportion of actions to\ntake across all locations in space. We show that the abstract policy is more\nrobust and achieves higher rewards with far fewer parameters than the\nelementary policy. This abstract policy is also a better fit to the properties\nthat practitioners in LSST problem domains require for such methods to be\nwidely useful.\n
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".