What can Palaeoclimate Modelling do for you?
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
In modern environmental and climate science it is necessary to assimilate observational datasets collected over decades with outputs from numerical models, to enable a full understanding of natural systems and their sensitivities. During the twentieth and twenty-first centuries, numerical modelling became central to many areas of science from the Bohr model of the atom to the Lorenz model of the atmosphere. In modern science, a great deal of time and effort is devoted to developing, evaluating, comparing and modifying numerical models that help us synthesise our understanding of complex natural systems. Here we provide an assessment of the contribution of past (palaeo) climate modelling to multidisciplinary science and to society by answering the following question: What can palaeoclimate modelling do for you? We provide an assessment of how palaeoclimate modelling can develop in the future to further enhance multidisciplinary research that aims to understand Earth’s evolution, and what this may tell us about the resilience of natural and social systems as we enter the Anthropocene.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it