Bibliographic record
Abstract
“Expect the cold weather to continue for the next ten days, followed by a warm spell.” This might have been the fourteen- day weather forecast for Montreal on December, 31, 2006 (Fig. 5.1, top). But imagine what it might have been if Earth rotated about its axis ten times more slowly, so that the length of the day coincided with the ten- day weather– macroweather transition scale— an alignment of scales almost achieved on Mars. In that case (Fig. 5.1, bottom) we would have heard, “Expect mild weather on Monday, followed by freezing temperatures, until a warm spell on Thursday, followed by a brisk Friday and Saturday, a warming on Sunday and Monday, followed by freezing on Tuesday, then a four- day warm period followed by freezing and then warming.” Although long- term trends in weather can persist for up to ten days or so, in macroweather, the upshifts tend to be followed immediately by downshifts (and vice versa) and, although longer term trends exist, they are much more subtle, resulting from imperfect cancelations of successive fluctuations. The tendency of macroweather fluctuations to cancel rather than to accumulate is its defining feature, and cancelation is synonymous with stability. Quantitatively, it implies that the temporal fluctuation exponent H is negative. In the weather regime with positive H, the temperature, wind, and other variables wander up and down with prolonged swings. The weather is a metaphor for instability. If we average macroweather over longer and longer times, its variability is reduced systematically so that it appears to converge to a well-defined value. In that sense, macroweather is what you expect, the weather is what you get. But what about macroweather’s spatial properties? As usual, forecasts can be explained with recourse to maps. For example, Plate 5.1 (left) shows the day- to-day evolution of the daily temperatures corresponding to the forecast in Figure 5.1 (top).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".