Recursive multivariate principal‐monotonicity inferential climate downscaling
Bibliographic record
Abstract
A recursive multivariate principal‐monotonicity inferential downscaling approach (ReMPMID) is proposed for climate downscaling under complexities of data uncertainties, nonlinear predictor–predictand correspondences, predictand dependencies, non‐normal distributions, spatial homogeneities, and temporal non‐stationarities. This approach is applied to the Athabasca River Basin (ARB) to verify methodological effectiveness. Many findings are revealed. For instance, ReMPMID may enable improvement of statistical downscaling reliability in comparison with selected existing approaches under these complexities at least for the ARB. The overall accuracies of ReMPMID are relatively high for temperature, while being acceptable for precipitation at the multi‐year scale. This approach may overestimate temperature in spring and winter and precipitation in summer and autumn while underestimating temperature in summer and autumn and precipitation in spring and winter to a relatively small extent. The modelling accuracies are not sensitive to one parameter (i.e. the statistical significance level) and significantly vary with another parameter (i.e. the minimum partition row number, Nmin). The calibration accuracies decrease with the climbing of Nmin and there is not a significant monotonic relationship between Nmin and the verification accuracies. The optimal value of Nmin varies with grids and predictands and shows higher uncertainty for temperature compared with precipitation. The uncertainties in ReMPMID simulations increase from summer, autumn, spring to winter for temperature and from winter, spring, autumn to summer for precipitation. These findings are helpful for gaining insights into ReMPMID and the regional climate in the ARB or neighbouring regions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".