Geological boundary modeling with uncertainty using an indicator interpolated threshold approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Estimating the quality and quantity of minerals is an important step in evaluating the feasibility of a mining project. Before estimation of resources occurs, the domain extents must be defined. Uncertainty in the placement of boundaries is ubiquitous, and proper evaluation of uncertainty is integral to aiding subsequent engineering decisions. Implicit modeling of boundaries is a popular technique as it is data driven, fast, and automatic. Signed distance functions (SDF) are commonly used in implicit boundary modeling. The SDF in its basic form is the signed-dependent shortest Euclidean distance between data that are not of the same category. However, in the presence of spatial-data asymmetry, the SDF introduces a conservative bias leading to lower global tonnages for estimating resources. Moreover, uncertainty through an additive constant to the SDF results in homogenous and unreasonable uncertainty. A novel approach to implicit boundary modeling with uncertainty is to interpolate a field of probabilities from indicator data and threshold the estimate for boundary extraction. Uncertainty is captured by varying the indicator thresholds, which provides eroded and dilated boundaries. The result is a globally unbiased boundary model that closely follows the structure of the conditioning data and provides a realistic uncertainty bandwidth.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it