SECTION 1: Summary of Graduate Student Research Activities (a) Distribution of Warm/Cool Season Precipitation Associated with 500 hPa Cutoff Cyclones
Bibliographic record
Abstract
progress. The first part of this research involved updating the 500 hPa cutoff low climatology performed by Smith (2003). The 6-h 2.5 ° x 2.5 ° NCEP–NCAR reanalysis grids and FORTRAN programs were used to extend this global and regional climatology. Figure 1 shows the total number of cutoff low events per grid point for 1948–2007. Maxima of cutoff low activity include the North Pacific Ocean, Hudson Bay, Canadian Maritimes, and off the coast of southeast Greenland. This climatology will be updated through 2008 after the conclusion of this year. Another important component of this project is to study several warm season cases of cutoff lows in the CSTAR domain using 6-h 0.5 ° x 0.5 ° GFS grids. These case studies will focus on the precipitation patterns of various cutoffs. Consistent with this focus, cases will be chosen that illustrate various problems with forecasting heavy precipitation and severe weather associated with cutoffs. Common synoptic-dynamic features throughout the troposphere will be composited along with selected parameters used in convective weather forecasting. Null cases, in which only nonsevere thunderstorms or light precipitation amounts occurred, also will be considered. Precipitation plots will be created from 6-h analyses obtained from the NWS National Precipitation Verification Unit Quantitative Precipitation Estimates.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.149 | 0.091 |
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".