An Improved Planner for Intelligent Monitoring of Sustainable Development of Forests
Bibliographic record
Abstract
An intelligent system for data fusion of remotely sensed imagery and geographic information, System of Experts for Intelligent Data Management (SEIDAM), incorporates a reasoning system or planner. The planner organizes automatically a collection of image processing, GIS, communications, and data base agents or expert systems. The agents are organized to accomplish a userspecified goal, such as perform a forest inventory update. A new planner, PALERMO/TO, has been developed which is 65% faster than an earlier version. SEIDAM software is available on the web; www.aft.pfc.forestry.ca. INTRODUCTION Global concerns about greenhouse gas emission and absorption have led nations to examine the utilization of forests. Forests which are managed in a sustainable manner should not contribute to further global warming. Canada, as with other nations with strong interests in forests, has developed a set of criteria and indicators for ensuring sustainable development of forests [1]. We have proposed...
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".