Determinación de cambios en la variabilidad climática bajo diferentes escenarios de cambio climático. Caso de estudio: Ensenada de Alberni Robertson, Isla de Vancouver
Bibliographic record
Abstract
Simulations from the Canadian Coupled General Circulation Model version 3.1 Special Report on Emissions Scenarios A2 and A1B and the artificial neural networks (RNA) for downscaling statistically the maximum temperature and the minimum temperature daily values to the Alberni Robertson Creek weather station level, located at the Vancouver Island, Canada were used. The data generated for the station was analyzed and mean and variance were estimated; in addition, comparisons between the values for each scenario in the base period (1961-2000) and the simulations in the 21st century were carried out. The results show an increase in the values of the minimum and maximum temperature means between 1.16 and 1.47 Celsius degrees in the zone for the 21st century. The models developed accurately simulated the temperature inter-annual cycles, as well as the series mean temperature. However, the variance of the original series is greater than that of the model for the period recorded. The method employed proved to be flexible and easy to implement, with low computational requirements. Given these characteristics, using it in other regions which have reliable records for macro climatic variables is recommended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".