Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Earth System models (ESMs) are global climate models with the added capability to explicitly represent biogeochemical processes that interact with the physical climate and so alter its response to forcing such as that associated with human‐caused emissions of greenhouse gases. Representing the global carbon cycle allows for feedbacks between the physical climate and the biological and chemical processes in the ocean and on land that take up some of the emitted carbon dioxide and so act to reduce warming. The sulfur cycle is also important in that both natural and human emissions of sulfur contribute to the production of sulfate aerosols which reflect incoming solar radiation (a direct cooling effect) and alter cloud properties (an indirect cooling effect). Other components such as ozone are also being incorporated into some ESMs. Evaluating the physical component of an ESM is becoming increasingly comprehensive and sophisticated, but the evaluation of the biogeochemical components suffer somewhat from a lack of comprehensive global‐scale observational data. Nevertheless, such models provide valuable insight into climate variability and change, and the role of human activities and possible mitigation actions on future climate change. Internationally coordinated experiments are increasingly important in providing a multimodel ensemble of climate simulations, thereby taking advantage of some ‘cancellation of errors’ and allowing better quantification of uncertainty. WIREs Clim Change 2011, 2:783–800. doi: 10.1002/wcc.148 This article is categorized under: Climate Models and Modeling > Earth System Models
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 it