Climate change: A call to action for the United Nations
Bibliographic record
Abstract
In recent decades, increased burning of fossil fuels for electricity, heating, and transportation have led to increases in greenhouse gases (eg, carbon dioxide [CO2], methane, nitrous oxide, and fluorinated gases) while deforestation and decreased biodiversity have reduced the Earth's ability to remove CO2, the major greenhouse gas emission. Greenhouse gases trap the sun's energy leading to fundamental shifts in the physical and chemical nature of our planet. They also increase global temperatures both on land and in the oceans and increase acidification of the ocean. More than 90% of the warming that happened on the Earth between 1971 and 2010 occurred in the oceans. In the 141 years that the National Oceanic and Atmospheric Administration (NOAA) has tracked global heat, the 10 warmest years on record have occurred since 2005.1 The 2020 Annual Climate Report by the NOAA reported that the combined land and ocean temperature has increased at an average rate of 0.08°C per decade since 1880; however, the average rate of increase since 1981 (0.18°C/0.32°F) has been more than twice that rate (0.18°C).1 The oceans, by absorbing the excess CO2, have increased in acidity, which is now around 25% higher than they were during preindustrial times. Increased acidity of rainfall has also been another consequence of global warming.2 These developments are having tremendous effects on the Earth's climate. Rising temperatures have resulted in increased frequency and ferocity of forest fires, dust storms, hurricanes, flooding, heat waves, and droughts.3, 4 Global warming and climate change deleteriously affect many aspects of planetary and human health (Figure 1).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.070 | 0.035 |
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".