Applying the Concept of 'Holistic' Basin Analysis to Unconformity-related Uranium Prospects in Under-explored Paleoproterozoic Basins
Bibliographic record
Abstract
Introduction Sedimentary basins of nearly all ages host economic petroleum and mineral resources. Exploration for the next generation of concealed, sedimentary-hosted mineral deposits that lack clear surface geochemical or geophysical anomalies, and the evaluation of under-explored or unproven basins will benefit from what has been termed ‘holistic’ basin analysis (Kyser, 2007; Kyser and Cuney, 2008). Holistic basin analysis integrates aspects of sedimentology and sequence stratigraphy, geochemistry, geochronology, and additional disciplines to determine how the basin evolved with respect to diagenesis and fluid composition, when major geological events that may have triggered fluid movement in the basin occurred, and what exploration strategies would be effective to discover a deposit. This methodology has recently been applied to the Paleoproterozoic Athabasca Basin, Canada (Cloutier et al., 2009; Alexandre et al., 2009) and McArthur and Mt. Isa Basins, Australia (Southgate et al., 2006; Kyser, 2007) that host uranium and base metal deposits, resulting in illumination of some of the critical factors necessary to produce economic mineralization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".