Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> In the geosciences, complex computational models have become a common tool for making statements about past earth system evolution. However, the relationship between model output and the actual earth system (or component thereof) is generally poorly specified and even more poorly assessed. This is especially challenging for the paleo sciences for which data constraints are sparse and have large uncertainties. Bayesian inference offers, in principle, a self-consistent and rigorous framework for assessing this relationship as well as a coherent approach to combining data constraints with computational modelling. Though “Bayesian” is becoming more common in paleoclimate and paleo ice sheet publications, our impression is that most scientists in these fields have little understanding of what this actually means nor are they able to evaluate the quality of such inference. This is especially unfortunate given the correspondence between Bayesian inference and the classical concept of the scientific method. Herein, we examine the relationship between a complex model and a system of interest, or in equivalent words (from a statistical perspective), how uncertainties describing this relationship can be assessed and accounted for in a principled and coherent manner. By way of a simple example, we show how inference, whether Bayesian or not, can be severely broken if uncertainties are erroneously assessed. We explain and decompose Bayes Rule (more commonly known as Bayes Theorem), examine key components of Bayesian inference, offer some more robust and easier to attain stepping stones, and provide suggestions on implementation and how the community can move forward. This overview is intended for all interested in making and/or evaluating inferences about the past evolution of the Earth system (or any of its components), with a focus on past ice sheet and climate evolution during the Quaternary.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.044 | 0.041 |
| Insufficient payload (model declined to judge) | 0.081 | 0.066 |
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".