A novel big-data perspective on earth system evolution
Bibliographic record
Abstract
The many components of the Earth System are linked through complex feedbacks that can be revealed in large compilations of diverse geochemical proxy data for paleoclimate and ocean conditions. In those archives, for example, temporal and spatial trends can be visualized as topologies (‘landscapes’) defined by areas of high-density data, corresponding to steady states (= stability basins) maintained by negative feedbacks. The boundaries between those stability basins, representing low-density data areas (potential tipping points), are crossed when external drivers are involved, moving the system to a new steady state. These external drivers are often associated with positive feedbacks. As a proof of concept, we produced a stability ‘landscape’ using an extensive set of published carbon (δ13C) and oxygen (δ18O) isotope data from sedimentary carbonates spanning the last 2.5 billion years. The superimposed C-O isotopic pathways show a preference for particular regions of the ‘landscape’ at different times in Earth history. Major excursions reflect positive loops often set into motion by external inputs (drivers) that can overwhelm the system, such as major volcanic and tectonic events and human-induced climate effects. Our approach can be applied to other proxy datasets, and multivariate statistical treatments, including machine-learning approaches, can potentially yield a robust, high-resolution ‘landscape’ of the Earth System through time.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.016 |
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; both teacher heads agree on what is shown here.
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".