Bibliographic record
Abstract
The story of statistics in geotechnical engineering can be traced to Lumb’s classical Canadian Geotechnical Journal paper on “The Variability of Natural Soils” published in 1966. In parallel, the story of risk management in geotechnical engineering has progressed from design by prescriptive measures that do not require site-specific data, to more refined estimation of site-specific response using limited data from site investigation as inputs to physical models, to quantitative risk assessment (QRA) requiring considerable data at regional/national scales. In an era where data is recognised as the “new oil”, it makes sense for us to lean towards decision making strategies that are more responsive to data, particularly if we have zettabytes coming our way. In fact, we already have a lot of data, but the vast majority is shelved after a project is completed (“dark data”). It does not make sense to reduce one zettabyte to a few bytes describing a single cautious value. It does not make sense to expect big data to be precise and to fit a particular favourite physical model as demanded by the classical deterministic world view. This paper advocates the position that there is value in data of any kind (good or not so good quality, or right or wrong fit to a physical model) and the challenge is for the new generation of researchers to uncover this value by hearing what data have to say for themselves, be it using probabilistic, machine learning, or other data-driven methods including those informed by physics and human experience, and to re-imagine the role of the geotechnical engineer in an immersive environment likely to be imbued by machine intelligence.
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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".