Frequency analysis of seasonal extreme precipitation in southern Quebec (Canada): an evaluation of regional climate model simulation with respect to two gridded datasets
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.
Bibliographic record
Abstract
This study proposes an assessment procedure to compare two gridded (Cubic Spline, CS, and ANUSPLIN) datasets and one regional climate model simulation series (CRCM 4.1.1) of seasonal maximum precipitation (SMP) over southern Quebec (Canada). This study consists of: (1) identifying the appropriate models that could provide the most accurate SMP estimates at a particular grid point; (2) delineating the climatic homogeneous regions; and (3) providing sub-regional intensity–duration–frequency (IDF) estimates. More specifically, five popular probability distributions (Generalized Extreme Value, Generalized Logistic, Weibull, Gamma, Log-Normal) are compared; cluster analysis was employed to delineate a set of homogeneous sub-regions and one empirical model (Montana) was used to represent IDF relationships. From the results, it was found that: (1) CS product is more compatible with mean and maximum observed SMP time series than that of ANUSPLIN and CRCM 4.1.1 datasets, especially in summer; (2) Generalized Extreme Value represents the primary distribution pattern for the study area; (3) southern Quebec can be delineated into two distinct homogeneous sub-regions, especially in winter; and (4) Montana equation provides an accurate IDF model. This study can be viewed as an initial step towards the development of IDF curves under non-stationary conditions within the context of seasonal features in the regional precipitation regime.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it