Geological characterisation guided by fuzzy k-means clustering of physical properties measured from core samples from the Victoria property, Sudbury Ontario
Bibliographic record
Abstract
Core logging is a subjective practice done by geologists, which documents the mineralogy, \ntextures, alteration, mineralisation and other features to give core a rock name. Pattern \nrecognition techniques are able to characterise the rocks and link the geophysical and geological \ndata quantitatively. The fuzzy-k means algorithm is an unsupervised pattern recognition \ntechnique, which groups data into clusters based on properties measured. This study will use the \nfuzzy-k means algorithm to characterise core samples from 2 drillholes from the Victoria \nproperty in Sudbury with thin section examination to identify how mineralogical changes can \naffect the measurements. Four different physical properties (density, gamma ray, conductivity \nand magnetic susceptibility) were measured from a total of 203 core samples of quartz diorite, \nmetagabbro, metabasalt, pyroxenite, olivine diabase and metasedimentary rocks. The samples \nwere classified into 4 different physical units, with additional confusion index values that \nindicate how well the data was classified. Quartz diorite, metagabbro and metabasalt have the \nhighest confusion index values while the olivine diabase and metasedimentary rocks have the \nlowest confusion index values. Combining the fuzzy k- means results and thin section \nexamination proved to be successful because heterogeneities in sulphide minerals, ore \nmineralisation and variation in rock forming minerals cause an overlap in physical properties \nwith other rock samples, increasing the confusion index while homogeneity in mineralogy results \nin a low confusion index.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".