Análisis de anomalías climáticas para la cuenca del río La Villa, Panamá, basado en los escenarios RCP
Bibliographic record
Abstract
The use of climate change projections is crucial for mitigation and adaptation, which are the basis for creating resilience. However, access to these scientific products is scarce in Latin America and the existing studies lack of an appropriate resolution to analyze small but highly vulnerable regions, such as river basins for planning purposes. La Villa river basin, Republic of Panama, is one of the watersheds of highest priority for adaptation to climate change. This study used downscaled projections from four climate models. The models are based on the Representative Concentration Pathways (RCP), presented in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change-IPCC. Results of this study suggest increases of the annual average precipitation in the watershed for the years 2050 and 2070. Meanwhile, maximum and minimum temperatures will increase an average of 1-2 ° C and near 4 ° C by the end of the 21st century. With these results, we observed that the use of small-scale climate projections in the RCP scenarios is feasible to determine the effects of climate change on small regions.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".