GESTÃO INTEGRADA DE RECURSOS HÍDRICOS: UMA REVISÃO INTEGRATIVA
Bibliographic record
Abstract
This article analyzes the literature of case studies in the last decade on integrated water resources management. For this, a systematic search of the literature was made in the Scopus and SciELO databases. The categorized publications were divided among the selected case study countries, namely the United States, Canada, Israel, Sweden, China, the Netherlands, Australia, Vietnam, Thailand, Scotland, Oman, Brazil, and Colombia. The objectives of each case study are presented, besides the problems pointed out in each place and the respective solutions pointed out and challenges. Among the problems pointed out, there is a decline in the number of fish species and populations; Water pollution; disconnected governance; lack of water, due to increased consumption, combined with the decrease in rainfall. As for the achievements, one innovation is the environmental water bill”, specifically in California. Canada has amended legislation. Israel has established a new water authority and China has achieved important results in the recovery of the Yangtze river basin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".