Geological boundary modeling with uncertainty using an indicator interpolated threshold approach
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".