A unifying analysis of the microplastic kinetics with the operative plastic potential
Bibliographic record
Abstract
The studies on the existence of a plastic potential in metal systems pre-dates by decades those of thermally activated flow as elucidated by the seminal work of Basinski in . However, to this date a direct correlation between the microplastic kinetic events which integrally manifests itself as the macroscopically determinable plastic potential has not been made. In this work a trial notion of using the activation volume as the fundamental internal variable results in re-expressing the macroscopic rate of doing work in terms of the work of activation necessary to overcome an internal discrete obstacle to dislocation passage. The subsequent differentiation results in a rate of power dissipation relation which under strict conditions of constant-strain-rate sensitivity is only dependent on the square of the strain rate and a linear work-hardening coefficient. On the other hand, if the time dependences of the internal parameters are considered, the power dissipation rate becomes a function of not only the strain rate but also the microstructural evolutionary parameters. The examinations of these predictions suggest means of materials characterization to assess which alloy heat treatment will enhance greater ductility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".