Bibliographic record
Abstract
Machine learning approach to predict the mineral compositions using conventional and geochemical well logs Github: Jon-GSC/Mineralogical_Characterization Download link-note: The goal of this study is using a combined approach with previous U-Net and XGBoost algorithm for mineralization composition analysis with petrophysical logs from selected formations. The code has been tested without any issue in PyCharm IDE. Requirements: Install python 3.8/newer and anaconda packages: conda install keras-2.8.0, tensorflow-2.8.0, scipy, scikit-image, tqdm, pandas, numpy, seaborn, shutil, matplotlib, lasio etc. libraries. Dataset: The original datasets of conventional and geochemical logs are from Horn River Basin. The loaded data are edited and used for testing purpose only. Instruction: The three main python codes and /itools, /data need be save in same folder. Before run the code, need install open-source Python packages. forWeb1_Data_prepare.py is used to loading '.las' files, generate the training dataset, and save as '.csv/.pkl'. forWeb2_Model_training.py is used to create the models, and training/validation with datasets from step 2. All of the weight parameters will be saved in one folder, code will run through each of the components and save all of the results in one folder. forWeb3_Prediction.py is used for predicting mineral composition with conventional petrophysical logs, and plotting output figures. For pilot running, user does not need change any parameters, just run the code files in steps 3,4. The code should run without any error if environment setting is correct. Please contact at following email address if any bugs popup. Hardware tested: HP-7920 workstation: 56core CPU; 64G memory; one Nvidia Quadro P5000 GPU. Acknowledgments: This Mineralogical characterization study used open source codes and library from github, google, and open-sourced geoscience packages lasio. Please cite the related references in your publications.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.103 |
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".