Advanced classification of carbonate sediments based on physical properties
Bibliographic record
Abstract
Abstract Physical properties such as bulk density (gamma ray attenuation), P‐wave velocity (primary or compressional wave acoustic velocity), electrical resistivity and magnetic susceptibility are related to characteristics of the marine sediments that, in turn, are indicative of the lithology. Non‐destructive physical properties are routinely measured during Mission Specific Platform expeditions conducted by the Integrated Ocean Drilling Program using a multi‐sensor core logger on whole cores. The goal of this study was to develop linear and non‐linear relations among physical properties and different types of carbonate sediment to identify relevant information that may aid in the classification of carbonates. The database and model presented here integrate sedimentology with physical properties data. Data were analysed using three techniques: Linear Discriminant Analysis, Random Forest and Support Vector Machines. The models that best describe the nature of the data are Random Forest and Support Vector Machines, reaching up to 79% and 74% total accuracy, respectively. This article presents an application of machine learning as a potentially useful tool for classifying sediment types, developed specifically for assisting with the challenging identification of the lithologies in coral cores. This technique can also be used for provisional core description prior to splitting, thereby enabling identification and preservation of potentially critical intervals for special analyses and studies. These methods of data analysis can also assist with sample selection for specific studies. Other applications include the interpretation of lithotypes from wireline geophysical logging data, particularly in boreholes where core recovery is poor or sampling is limited to drill cuttings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".