What if your Inversion has no Numerical Target?
Bibliographic record
Abstract
We present a system for inverting geological models in cases where there are no established numerical criteria to act as inversion targets. The method of interactive evolutionary computation provides for the inclusion of qualitative geological expertise within a rigorous mathematical inversion scheme, by simply asking an expert user to visually evaluate a sequence of model outputs. The traditional numerical misfit is replaced by a human appraisal of misfit. A genetic algorithm provides optimal convergence into the target parameter space, while optimising an ensemble of solutions, so that the non-uniqueness of the problem may be explored. In order to facilitate analysis of the results, we employ a visualisation technique known as self-organised mapping to represent the parameter space covered by the numerous model outputs. The result is a simple view of an otherwise complicated multi-dimensional problem. A user may infer much about the controlling parameters in the model through a few graphical displays of the data.The potential of this interactive inversion and visualisation technique is demonstrated when we invert a geody-namic model for a conceptual pattern of fault spacing during crustal extension. We also present an example where the interactive scheme is linked to a numerical inversion of induced polarisation data. In this case, we are exploring for the numerical inversion parameters which lead to a particular geological output.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".