Results of a critical state line testing round robin programme
Bibliographic record
Abstract
A critical state testing round robin programme was carried out on sandy silt gold tailings. This involved 15 laboratories around the world testing a sandy silt tailings to infer its critical state line (CSL). Methods to be used were intentionally not supplied to participants, to enable the current methods being employed in industry and academia to be obtained in an unbiased manner. All but one of the laboratories involved in the study used the moist tamping sample preparation technique, generally to produce loose, contractive specimens. Void ratio was measured using a variety of means, including cell calibration, end-of-test water content and end-of-test soil freezing (EOTSF) to assist in measuring the final water content. Of the 15 entries, four were excluded from the primary comparison owing to various issues that appear to have led to their divergence from most of the entries received. Of the remaining entries, the best reproducibility was produced by laboratories that used EOTSF to measure void ratio. Most other test procedure variations appeared to have a negligible effect, with the exception of fixing of the top platen and possibly sample size. A CSL elevation range of 0·04 void ratio for laboratories using EOTSF was observed.
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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".