Evolution of Sedimentary Basins as Recorded in Silica Concretions: An Example from the Ionian Zone, Western Greece
Bibliographic record
Abstract
Chert concretions in thick limestone successions preserve a more complete paragenetic sequence of diagenetic minerals than their host limestone and interbedded shale. The goal of this study was to test the possible presence of a high-temperature mineralising system in the Ionian basin of western Greece. Upper Cretaceous chert nodules were sampled at Araxos, where rocks are highly faulted and uplifted by salt diapirism, and on Kastos Island, on the flanks of a regional anticline. Chert concretions have microporosity produced by recrystallisation of opal to quartz and fractures produced in the brittle chert during basin inversion. Diagenetic mineral textures were interpreted from backscattered electron images, and minerals were identified from their chemistry. Diagenetic minerals in pores and veins include sedimentary apatite (francolite), dolomite, Fe-chlorite, Fe oxide-hydroxide mixtures, sphalerite, barite and calcite. Sphalerite is restricted to Araxos, suggesting that inferred basinal fluids were hotter and more saline than at Kastos. At Araxos, the Fe oxide-hydroxide also includes minor Cu, Zn, and Ni. Whether the transported metals were derived from sub-salt clastic rocks and basement, or from enriched Mesozoic black shales, is unclear. The effectiveness of this novel approach to understanding fluid flow history in thick limestone successions is validated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".