Bibliographic record
Abstract
The last two decades have seen five large tailings dam failures, which developed suddenly and statically with damages of US$ billions and many deaths. These are company-threatening events, far outside societal tolerance, with investors questioning the situation. While media attribute these failures to mining companies, the underlying cause is a failure in engineering education: engineers of record, and their reviewers, lacked an adequate understanding of soil behaviour. The initiative to improve tailings stewardship by the International Council on Mining & Metals, with its focus on process and governance, will not achieve its aims unless this shortfall in understanding of soil behaviour is addressed. Critical state theory quantifies how and why void ratio controls soil behaviour, and was necessary to understand the Fundoa, Cadia and Brumadinho liquefactions (the rapid drained to undrained transition in particular); this critical state framework must become a ‘core competence’ for tailings dam engineers of record. Little additional cost will arise from doing this, with the largest change in practice being adoption of finite-element analysis for stability assessment. The biggest challenge is education, with engineers needing to familiarise themselves with the largely untaught critical state theory (there are public-domain resources for this, as given in the Appendix to this paper).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".