Bibliographic record
Abstract
There is considerable uncertainty about how the rapid, recent rise in greenhouse gas concentrations driven by anthropogenic emissions will interact with on-going changes in orbital position to affect climate in the very long run – the next several thousand years. Here we study the evolution of climate over the next hundred thousand years using a statistical climate model estimated on the paleo record that represents physically consistent relations between orbital position and climate. This climate model is able to use orbital position alone to simulate the timing, magnitude, and saw-toothed pattern of ice volume, CO2 concentrations, and other climate time series both in- and out-of-sample. The model is used to run experiments that simulate climate with- and without human intervention in the global carbon cycle. Without human intervention, the next glacial maximum is forecast to occur in about 20,000 years. This result is relatively unaffected by the current anthropogenic spike in CO2 concentrations. Conversely, the glacial maximum can be avoided - and the current climate maintained - by geo-engineering carbon concentrations to stabilize at around 325 ppm. The emissions needed to sustain these concentrations can be generated from known resources of fossil fuels. This suggests that CO2 is a cost effective control variable that - if managed carefully - can be used to sustain a hospitable climate in the short-run (by reducing emissions) and the long-run (by stabilizing concentrations).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".