Bibliographic record
Abstract
This past winter, for the second year in a row, seemed pretty extreme in both Europe and the United States. So this is a good time to check quantitatively how seasonal climate change is stacking up against expectations. People's perception of climate change may be the most important factor determining their willingness to accept the scientific conclusion that humans are causing global warming (or global climate disruption, as you please). It is hard to persuade people that they have lying eyes. In the paper attached to my congressional testimony in 1988 (1) we asserted that the perceptive person would notice that climate was changing by the early 21 st century. I used colored dice to illustrate how the frequency of unusually warm seasons was expected to change. We considered three scenarios for future greenhouse gas amounts. Figure 1 shows that the real world so far is close to scenario B. Temporary aside: there are two main reasons that greenhouse gas growth moved off the track of scenario A onto scenario B in the early 1990s, as shown in Figure 2: (1) the growth of CFCs (chlorofluorocarbons) was greatly diminished by successive tightenings of the Montreal Protocol, (2) the growth of methane slowed sharply. Fig. 1. Update of Fig. 2 of Reference 1, scenarios A, B and C being climate forcings of greenhouse gases used in climate model simulations. The real world (red curve) has closely followed scenario B. Fig.2. Annual growth of (a) CO2 and (b) climate forcing by greenhouse gases. The forcing is the 5-year
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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 | 0.008 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".