Bibliographic record
Abstract
Abstract Human activity is altering the composition of Earth's atmosphere through the addition of greenhouse gases and particulates. Anthropogenic changes in the properties of the atmosphere can be thought of as external forcing factors on the climate system. There is a fair degree of confidence in results on greenhouse gas forcing and the climate's response to that forcing. However, knowledge of the forcing and response due to aerosols remains highly uncertain. There are also natural external forcing factors that influence climate, such as changes in orbital geometry and changes in solar irradiance and volcanic activity. The climate system, even when not perturbed by external factors, produces substantial amounts of natural variability. Thus detection and attribution of the effects of external forcing is a statistical signal‐in‐noise problem. The detection part of this problem is the process of demonstrating that an observed change is not likely to have been entirely the result of natural internal variability. The attribution aspects of the problem are more difficult because it is not possible to conduct controlled experiments with the climate system. The practical approach that has been taken in the climate research community involves statistical analysis and the assessment of multiple lines of evidence to (a) demonstrate that observed changes are consistent with forcing of the climate by a combination of anthropogenic and natural external factors, and (b) demonstrate that the changes are inconsistent with alternative, physically plausible explanations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.014 |
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".