The phlogiston theory of rock mass classification: Philosophical and mathematical critique of ordinal data usage
Bibliographic record
Abstract
The widespread use of rock mass classification systems in engineering practice relies on mathematical operations and assumptions that violate fundamental principles of measurement theory. This paper presents a critical analysis of current classification methodologies, focusing on the Rock Mass Rating (RMR), Q-system, and Geological Strength Index (GSI), drawing parallels with historical scientific misconceptions such as the phlogiston theory. Through detailed examination of measurement theory principles and their application to geological characterization, we demonstrate that these classification systems contain inherent flaws in their treatment of ordinal data and parameter independence. The paper identifies four critical issues: the invalid summation of ordinal ratings in the RMR system, the inappropriate multiplication and division operations in the Q-system, the unjustified visual interpolation in the GSI system, and the universal problem of assumed parameter independence. Through examination of measurement theory principles and their application to geological characterization, we demonstrate that current classification systems violate basic mathematical rules in their treatment of ordinal data and parameter independence. The implications of these violations extend beyond theoretical concerns, affecting practical engineering decisions and risk assessment. We also illustrate how these theoretical flaws manifest in practice and propose directions for developing more theoretically sound approaches to rock mass characterization. This critical analysis aims to initiate a necessary dialogue about the future of rock mass classification in engineering practice. • Rock classification systems violate measurement theory principles by misusing ordinal data in mathematical operations. • Parameter independence assumptions contradict field evidence of significant interdependencies in rock masses. • Case studies show how theoretical flaws in classifications lead to inadequate designs and costly modifications. • This paper explains why current approaches create a false sense of precision in rock engineering practice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".