Bibliographic record
Abstract
Abstract Sedimentary basins are the largest structures on the surface of our planet and the most significant sources of energy‐related commodities. With time, sedimentary successions in basins normally are subjected to increasingly intense diagenesis that results in differential evolution of basin hydrology. This hydrologic structure is in turn vitally important in determining how and where deposition of metals may occur. Fluids in all basins originate and flow as a result of sedimentological and tectonic events, so that fluid histories should reflect the control of both lithology and tectonism on ore deposition. Sandstone lithologies, in particular, reflect fluid‐flow events because they are normally the major aquifers in basins. However, early cementation results in occlusion of primary permeability in some facies (diagenetic aquitards) whereas in others, permeability develops due to the dissolution of unstable grains (diagenetic aquifers). Particularly for ore deposits in Precambrian basins, identification of paleohydrologic systems during basin evolution requires the integration of data derived from tectonics, sedimentology, stratigraphy, diagenesis, geochemistry and geology. Assessment of all these data is a prerequisite for the ‘holistic basin analysis’ needed to guide the search for basin‐hosted ores. Recent results from the Paleoproterozoic Mt Isa and McArthur basins in northern Australia serve as a template for exploring for mineral deposits in basins. Basinal fluids were saline, 200–300°C and evolved primarily from meteoric water in the Mt Isa Basin and from seawater in the McArthur Basin during burial to depths of 4–12 km. The δDfluid and δ18Ofluid values in these brines were isotopically identical to those in the Zn‐Pb, Cu and U deposits. Geochemical changes of various lithologies during alteration support detrital minerals as the major source of the U, and volcanic units proximal to diagenetic aquifers as a source for the transition metals. Ages of diagenetic phases extracted from aquifer lithologies reveal that fluid migration from the diagenetic aquifers effectively covers the period of formation for U, Zn‐Pb and Cu mineralization, and that the deposits formed in response to tectonic events reflected in the apparent polar wandering path for the area. Sequence stratigraphic analysis and models of fluid flow also indicate that basinal reservoirs were likely sources for the mineralizing fluids. Thus, diagenetic aquifer lithologies were being drained of fluids at the same time as the deposits were forming from fluids that were chemically and isotopically similar, linking diagenesis and fluid events within the basin to the formation of the deposits.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".