Dynamic Indentation Characteristics for Various Spacings and Indentation Depths: A Study Based on Laboratory and Numerical Tests
Bibliographic record
Abstract
Laboratory and numerical study tests were conducted to investigate the dynamic indentation characteristics for various spacings and indentation depths. First, laboratory tests indicate that the increase in the indentation depth first resulted in enlarged groove volumes, caused by fiercer rock breakages between indentations for a fixed spacing; then, the groove volume slightly increased for further increase in indentation depth, whereas the increase in spacing restrained rock breakages and resulted in shrunken grooves. In addition, the numerical study agreed well with laboratory tests that small chips formed at the shallow part of the rock specimen at the early indentation stage, and then, larger chips formed by the crack propagation at deeper parts of the rock specimens when the indentation depth increased. With further increase in indentation depth, crushed powders instead of chips formed. Moreover, the numerical analysis indicates that crack propagation usually leads to the decrease of the indentation force and the dissipation of the stress concentrations at crack tips, whereas the cessation of crack propagation frequently resulted in the increase of the indentation force and the stress concentrations at crack tip with the increase in indentation depth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".