Year-to-year correlation, record length, and overconfidence in wind resource assessment
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. Interannual variability of wind speeds presents a fundamental source of uncertainty in preconstruction energy estimates. Our analysis of one of the longest and geographically most widespread extant sets of instrumental wind-speed observations (62-year records from 60 stations in Canada) shows that deviations from mean resource levels persist over many decades, substantially increasing uncertainty. As a result of this persistence, the performance of each site's last 20 years diverges more widely than expected from the P50 level estimated from its first 42 years: half the sites have either fewer than 5 or more than 15 years exceeding the P50 estimate. In contrast to this 10-year-wide interquartile range, a 4-year-wide range (2.5 times narrower) was found for "control" records where statistical independence was enforced by randomly permuting each station's historical values. Similarly, for sites with capacity factor of 0.35 and interannual variability of 6 %, one would expect 9 years in 10 to fall in the range 0.32–0.38; we find the actual 90 % range to be 0.27–0.43, or three times wider. The previously un-quantified effect of serial correlations favors a shift in resource-assessment thinking from a climatology-focused approach to a persistence-focused approach: for this data set, no improvement in P50 error is gained by using records longer than 4–5 years, and use of records longer than 20 years actually degrades accuracy.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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