Bibliographic record
Abstract
Abstract. We present curlew, an open-source python package for structural geological modelling using neural fields. This modelling framework incorporates various local constraints (value, gradient, orientation and (in)equalities) and tailored global loss functions to ensure data-consistent and geologically realistic predictions. Random Fourier Feature (RFF) encodings are used to improve model convergence and facilitate stochastic uncertainty quantification, while simultaneously improving the model's ability to learn naturally periodic features such as folds. These advances are integrated into a software framework that allows incremental construction of complex geological models through temporally-linked neural fields, each representing a specific deposition, intrusion or faulting event. Significantly, this framework allows semi-supervised learning to integrate diverse unlabelled datasets (e.g., geochemistry, petrophysics), reducing interpretation bias and potentially improving model robustness. We describe and demonstrate these various capabilities using synthetic examples and real data from a faulted stratigraphic digital outcrop model from Newcastle, Australia.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.026 | 0.021 |
| Insufficient payload (model declined to judge) | 0.086 | 0.054 |
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".