The CARA Network: Building Latin American Capacity in Hydrogeology and Water Resource Management
Bibliographic record
Abstract
Abstract: Ground water supplies the vast majority of water supply in Central America yet, prior to the last 15 years, there have been only a handful of appropriately educated hydrogeologists in the region. The CARA Network ( http://www.caragua.org ) began in 1999 with the intention of building capacity in hydrogeology and water resource management in Central America. Capacity is built at national (public) universities through applied two‐year M.Sc. programs with related teaching and research. Each university is strengthened through the creation of new faculty positions, faculty training, the procurement of equipment/books/software, and the dedication of infrastructure. To date, the CARA M.Sc. programs have trained (or have in a current program) almost 160 Latin American hydrogeologists at the M.Sc. level. CARA short courses and workshops have trained over 2000 water‐sector professionals in the region in a variety of water themes related to groundwater and water resource management.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".