Medium-high density soft micrites: <i>impact</i> of microstructural features on state and compressibility
Bibliographic record
Abstract
Three soft rock facies of the Middle Globigerina Limestone (MGL) from Malta, of mineralogical composition and index properties similar to some medium-high density Chalk facies, are disaggregated through prolonged agitation in water to create reconstituted samples. The significant activity of their clay-sized calcite grains can impart a medium-high plasticity. Scanning electron microscopy analyses of natural and reconstituted samples show the natural bonding as interlock, possibly induced by cohesive clay-sized calcite grains during the sediment compaction. Micro-analyses and compression test data also show that local calcite crystal overgrowth under burial has reduced further the soft rock porosity, making it lower than that of the reconstituted material one-dimensionally compressed to the geological preconsolidation pressure. Nonetheless, the low stress sensitivity of natural MGL suggests that this interlock bonding does not strengthen much the material with respect to the highly compressed reconstituted soil. The natural MGL is of very low permeability, which reduces further with compression. Concurrently, the soft rock creep coefficient increases, reaching values far above those typical for clays. The microstructural features and the mechanical properties of the different MGL facies are shown to be sensitive to clay mineral content.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".