Ha impactado el TLCAN los recursos de agua y uso del suelo en la frontera de México y EE.UU.
Bibliographic record
Abstract
Las recientes negociaciones del Tratado de Libre Comercio de America del Norte (TLCAN) entre Estados Unidos (EE.UU.), Mexico y Canada han hecho visible la relacion estrecha que existe entre la manufactura automotriz y el comercio de los productos agricolas. Desde su inicio, en 1994, el TLCAN ha transformado las condiciones socioeconomicas a lo largo de los 3,000 km de la frontera entre Mexico y EE.UU., en parte, por las inversiones grandes relacionadas con la infraestructura agricola y manufacturera. Si bien el TLCAN incluye disposiciones para la proteccion del medio ambiente en ciertos sectores, se ha prestado poca atencion al uso de los recursos naturales que han sido requeridos para la expansion economica. ?Que consecuencias ha tenido el TLCAN en los recursos de agua y uso del suelo en la frontera de EE.UU.-Mexico? Esta pregunta es de importancia vital para la region debido a su clima arido y al hecho de que la disponibilidad de agua esta en el centro de las actividades economicas.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".