Bibliographic record
Abstract
This chapter’s title reflects both the profound climactic disruptions and the exponential increase in scientific understanding that have occurred since the first Earth Day in 1970. Schneider begins with a personal anecdote, recalling a childhood spent skiing in the Harz mountains of Germany, now no longer possible, due to the dramatic shortening of winter and the loss of snow cover. In contrast, his present home of Los Angeles now experiences an extra two weeks of above average hot days compared to 1970. This change is paralleled by the development of climate science, pioneered by Swedish chemist and Nobel Laureate Svante Arrhenius, whose often inaccurate measurements first connected rising and falling CO2 levels to global warming and cooling. Today, a combination of fossil fuels and deforestation have resulted in CO2 levels of 415 ppm (parts per million) compared to 320 ppm in 1970 – 20% above pre-industrial levels. Industrialized nations have therefore added twice as much carbon dioxide to the atmosphere since 1970 as in all of previous human history before. The chapter emphasizes the continuing need for more accurate data and modeling to predict the effects on incredibly complex global systems. By dividing the earth into manageable grids, scientists are more accurately able to predict atmospheric variations, using supercomputers to break down the impossibly large variables. The chapter ends on a stark note: even if all greenhouse emissions were to be halted today (virtually impossible given the nature of our global energy economy), temperatures would still rise by 0.4–1.7°C, as a new baseline would take centuries, if not millennia, to establish. The conclusion is simple: every facet of human activity will be impacted, and we will have to adapt.
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.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.102 |
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".