Waste indicators of primary sector and health & veterinary services for regional planning
Bibliographic record
Abstract
When there is a need to move smoothly and effectively from an abundance of detailed fi eld data to summarized information, indicators and indices are used.Indicators are important tools that assist decision-makers in formulating and implementing plans for the management of waste at different geographic levels.In Cantabria, a northern Spanish region, all waste streams generated are covered through four specifi c Waste Plans recently adopted.The present study is focused on the Primary Sector, Health & Veterinary Services Waste Plan (PHWP), which is the framework to the decision-making processes related to the generation and management of forest, agricultural, livestock, food industry and health & veterinary wastes.In this work, 16 indicators have been proposed to track the evolution over time of the management of these waste streams in the region and the degree of achievement of the policy objectives.This article discusses the way to obtain, analyse and evaluate valuable information to build the indicators, fi nding that only eight indicators can be applied at short term.In addition, a summary of these indicators is included, showing in general, a good trend of the evolution of primary sector, health & veterinary waste management.Finally, different actions to improve the quality of data used for the indicators development are proposed in order to obtain more useful waste indicators to the stakeholders.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".