A consistent terminology to communicate ground-related uncertainty
Bibliographic record
Abstract
Engineering geology is highly affected by uncertainty related to geology , geotechnical parameters, models and methods. While the technical aspects of ground-related uncertainty are increasingly well investigated, the terminology to communicate uncertainty - e.g., “It is likely that X will happen.” - has not yet been unified and experts use it however they see fit. Due to varying experience, personal biases and societal backgrounds, people may understand uncertainty statements very differently, which is misleading and can even result in legal disputes. This contribution investigates the usage of uncertainty communicating terminology in ground-related disciplines and finds that there is a pronounced prevalence of uncertainty terminology in them. Furthermore, there is a special need to express uncertainty related to quantities (e.g. “most of the project area consists of…”). In response, we propose a framework to consistently communicate ground-related uncertainty encompassing three steps: 1. When you are certain about a statement, do not use uncertainty communicating language. 2. Assess and state the degree of confidence in a statement based on the quantity and quality of the available evidence vs. the agreement of the evidence. 3. If you have high or very high confidence in the statement, communicate the uncertainty in a consistent manner, otherwise elaborate how higher confidence can be achieved. The proposed approach feeds into new uncertainty-aware standards, such as Eurocode 7, and goes beyond them by addressing uncertainty in text and speech. This paper provides the premises for increased awareness of uncertainty communication and encourages further works on the topic.
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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 | 0.036 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".