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The CARA Network: Building Latin American Capacity in Hydrogeology and Water Resource Management

2010· article· en· W2057662637 on OpenAlex
David N. Bethune, M. Cathryn Ryan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Contemporary Water Research & Education · 2010
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
FundersDivision of Graduate EducationUniversidad Nacional Autonoma de Honduras
KeywordsLatin AmericansProcurementVariety (cybernetics)Resource (disambiguation)HydrogeologyGroundwaterCapacity buildingBusinessWater resource managementGeographyEnvironmental planningEnvironmental resource managementEngineeringEnvironmental sciencePolitical scienceComputer scienceMarketing

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.334
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it