Análisis crítico de la metodología implementada por el IDEAM y las empleadas en el ámbito internacional para la definición del índice de escasez del agua superficial.
Bibliographic record
Abstract
The following document approach the thematic from methodology for calculation of the scarcity index design by IDEAM (by its acronym in Spanish), for which will aboard the following topics: description from some calculation methodologies used at international levels, exactly in countries such as USA, Israel, Netherlands and Canada; some critics by national authors about the methodology, advantage and disadvantage from the scarcity index and finally the proposed readjustments for the index calculation.\n\nAdditionally would be design a practical guide for the calculation taking in consideration the obtained results by the performed analyzes, it will be suggested some improvements for the currently methodology with new ranges to measure the scarcity index, it will be presented the conclusions and recommendations and finally all the procedure will be applied in a representative case. \n\nThis document is presented as a tool to facilitate the use to estimate the scarcity index, being useful in the decision making in the correct use of water resources in the country.
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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 | 0.028 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".