Frontiers in quantitative paleogeography and paleomagnetism
Bibliographic record
Abstract
The last few years have been marked by a number of motivating novel ideas and methodological advancements in paleomagnetic analysis (e.g. trans-hierarchical uncertainty propagation), observational and theoretical geodynamics, and paleogeographical modeling (e.g. optimisation and Bayesian approaches). Many of these developments offer new insights on, and/or approaches to estimating, the past motions of tectonic plates—but so far these developments have largely unfolded in isolation of one another. In November 2021 an international group of 15 young scientists with highly complementary backgrounds (spanning the aforementioned fields) gathered to explore and discuss these exciting new developments and to brainstorm strategies that may enable their integration. We anticipate that the integration of these diverse new ideas and methods will open new frontiers in plate tectonic research, and notably lead to much better-constrained paleogeographic models. In this presentation, we will share some of the insights and strategies that emerged from the workshop, including the advantages of conducting paleomagnetic analysis at the site-level, the application of emerging paleomagnetic Euler pole analysis frameworks, and the use of insights extracted from Earth-like geodynamic models (which self-generate plate tectonic behavior) to further constrain the results of these paleomagnetic methods. We also present some preliminary results of early experiments putting these strategies into practice on a paleomagnetic dataset from North America.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".