Rock mass performance monitoring with topology preserving quantised vector spaces
Bibliographic record
Abstract
Abstract This work explains the development of a so-called rock mass system performance map, that provides a backdrop to assist in performance interpretation. The rock mass system performance maps presented in this paper are 2D graphical devices prepared using Sammon mappings, Learning Vector Quantisation, Self-Organising Topological Maps and combinations of these mathematical techniques. Using supervised or unsupervised learning methods, these algorithms project and partition high-dimensional vector spaces, of a dimension equal to the number of environmental and rock mass condition parameters considered, into 2D categories of rock mass condition. In providing 2D renderings, the techniques aim to preserve properties of the characterising vector space such as adjacency of states and topology. The condition of a rock mass can be ‘plotted’ on that map by identifying its k-nearest neighbours and interpreted relative to stability metric categories. Should the defining vector space include rock mass properties or environmental parameters that are repeatedly measured, or possibly continuously monitored, then the rock mass condition marker traces out a performance trajectory across the performance map. An example of rock mass performance map synthesis and use is presented.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".