Development of Skill by Students Enrolled in a Weather Forecasting Laboratory*
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
Abstract Daily values of forecast scores are evaluated for students in a weather analysis and forecasting class (ATMS 452) offered by the Department of Atmospheric Sciences of the University of Washington during the spring terms of 1997–2007. The objective of this study is to determine the rate at which senior-level undergraduate students develop proficiency at short-term (next day) weather forecasting. Separate analyses are carried out for different categories of forecast parameters. Time series of the average skill achieved over the course of the quarter are presented for the median and the best–worst two student forecasters each year. An overall improvement in student forecast skill occurs over roughly the first 6 weeks of the quarter, followed by minimal systematic changes. Negligible trends in average forecast skill have occurred over the past 10 yr. The correlation coefficient between the students’ overall forecast performance and test scores in ATMS 452 is about 0.4. The results are relevant to the design of effective instructional programs for weather forecasting.
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 | 0.002 | 0.001 |
| 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) | 0.000 | 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