Rock strength as a metric of welding intensity in pyroclastic deposits
Bibliographic record
Abstract
Welding of pyroclastic deposits describes the flattening of glassy pyroclasts under a compactional load at temperatures above the glass transition temperature. Traditionally, this process is mapped using metrics such as density, porosity or fabric. Here we develop rock strength as an ancillary tool for mapping variations in welding intensity. Rock strength can be measured as point load strength or as uniaxial compressive strength (UCS). The point load strength test (PLST) is an efficient, portable means of measuring relative rock strength and is easily used in field studies. Our measurements on a variety of rock types, including welded ignimbrite, are used to develop an empirical relationship between the point load measurements and the more standardized rock strength rating based on UCS. Strong materials (PLST > 4 MPa) show a linear relationship described by UCS = 24.4◊PLST. Weaker materials, such as pyroclastic rocks, (PLST < 5 MPa) require a nonlinear model: UCS = 3.86◊PLST2 + 5.65◊PLST. The potential for using rock strength to map variations in welding intensity within pyroclastic deposits is demonstrated using data collected from a stratigraphic section through the Bandelier Tuff, New Mexico. Four discrete zones of welding intensity based on rock strength ratings are identified. This classification scheme provides an objective means of quantitatively tracking variations in welding intensity in the field.
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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 | 0.004 | 0.002 |
| 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".