Hydrocarbon resources potential mapping using evidential belief functions and frequency ratio approaches, southeastern Saskatchewan, Canada
Bibliographic record
Abstract
The purpose of the present study is to model the hydrocarbon resources potential mapping using geographic information systems (GIS). The presented method is based on petroleum system concepts; therefore, petroleum system elements were used to define criteria for petroleum potential mapping. Several statistical methods can be used to effectively predict potential areas for hydrocarbon resources. In this study, two statistical methods were used (frequency ratio and evidential belief functions) to predict the potential distribution of petroleum resources in the study area. A case study in the Red River – Red River petroleum system of the Canadian Williston Basin in southeastern Saskatchewan in Canada is proposed to assess the feasibility of this new modelling technique. The accuracy of the hydrocarbon potential maps was evaluated by success rate and prediction rate efficiency curves. The resultant petroleum potential maps resulted in delineation of high-potential zones occupying about 15% of the study area. The validation results showed that the prediction rate for the best model is 88.14%. This study was carried out at a regional scale; therefore, the results can be used to guide exploration works at early stages.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".