Bibliographic record
Abstract
“The climate is what you expect, the weather is what you get”: The climate is a kind of average weather. But is it really? Those of us who have thirty years or more of recall are likely aware of subtle but systematic changes between today’s weather and the weather of their youth. I remember Montreal winters with much more snow and with longer spells of extreme cold. Did it really change? If so, was it only Montreal that changed? Or did all of Quebec change? Or did the whole planet warm up? And which is the real climate? Todays’ experience or that of the past? The key to answering these questions is the notion of scale, both in time (duration) and in space (size). Spatial variability is probably easier to grasp because structures of different sizes can be visualized readily (Fig. 1.1). In a puff of cigarette smoke, one can casually observe tiny wisps, whirls, and eddies. Looking out the window, we may see fluffy cumulus clouds with bumps and wiggles kilometers across. With a quick browse on the Internet, we can find satellite images of cloud patterns literally the size of the planet. Such visual inspection confirms that structures exist over a range of 10 billion or so: from 10,000 km down to less than 1 mm. At 0.1 mm, the atmosphere is like molasses; friction takes over and any whirls are quickly smoothed out. But even at this scale, matter is still “smooth.” To discern its granular, molecular nature, we would have to zoom in 1,000 times more to reach submicron scales. For weather and climate, the millimetric “dissipation scale” is thus a natural place to stop zooming, and the fact that it is still much larger than molecular scales indicates that, at this scale, we can safely discuss atmospheric properties without worrying about its molecular substructure. Clouds are highly complex objects. How should we deal with such apparent chaos? According to Greek mythology, at first there was only chaos; cosmos emerged later.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.020 |
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".