Characterizing weekday-weekend difference in precipitation
Bibliographic record
Abstract
In an attempt to characterize the possible effect of human activities on precipitation, weekday versus weekend precipitation is analyzed over the United States. Past works on the subject using different datasets and statistical methods, including one done at McGill in 2013 by Cazenave, have obtained mixed results. The main focus of this work is to diminish as much as possible errors in the calculation of the significance of the weekly cycle in precipitation by determining an appropriate statistical test and dealing with the issue of temporal autocorrelation. The difference in precipitation between weekday and weekend is first characterized by analyzing data of reflectivity probability from composite images of radar and data of accumulation that have both been generated and archived since late 1995 and up to September 2014 for twelve months each year. To assess the statistical significance, we perform calculations using two different methods, one by taking into account an effective number of independent observations [Bayley, 1946] and the other by using a Monte Carlo approach where we compare results with a random data simulation.Both methods reveal similar regions with a potentially significant weekly cycle in precipitation such as small areas over Texas and Florida in addition to a bigger area in the northeastern part of the United States centered on Pennsylvania. However, additional analyses of significant values reveal that less than 5% of the continental US area shows significance beyond the 95% confidence interval, suggesting that significant signatures are likely due to random variability.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".