Some Bounds on the Sensitivity of Human Population Dynamics to the Effects of Greenhouse-Gas Reduction of Land Productivity
Bibliographic record
Abstract
The 7 August 2019 IPCC special report on land and climate change predicts that the average global temperature will rise more than 1.5 C if human production of greenhouse gases (GHGs) continues at the 2019 rate to 2030, significantly compromising land productivity and the world food supply. Given a relationship between GHGs and land productivity, the World3 simulator can help to bound estimates of the sensitivity of human population dynamics to GHG reduction of land productivity. World3 projects that in the worst case the peak size of the human population could be reduced by 4% - 37% by GHG reduction of land productivity, compared to World3’s “benchmark scenarios”, during calendar years 1900-2100. In particular, World3’s “business-as-usual” (BAU) scenario implies that in the worst case the peak size of the human population would be reduced by ~20% by reduction of land productivity. This BAU-specific result is consistent, to within a factor of two, with GHG/wheat-productivity relations described in the literature.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".