Unsupervised Statistical Learning with Integrated Pattern-Based Geostatistical Simulation
Bibliographic record
Abstract
Abstract This study presents a new geostatistics modeling methodology for the purpose of achieving the following three objectives. (1) Connecting geostatistics and machine learning methodologies, (2) Using non-linear topological mapping to reduce the original high dimensional data space to low dimensional subspace, as well as clustering for identifying and extracting meaningful patterns for both visualization and efficiency purposes, and (3) Using unsupervised learning algorithms to bypass potential problems encountered while using supervised learning algorithms. Past observations have indicated that due to lack of labeled input data, artificial neural network (ANN) architecture, based on feedforward and backpropagation supervised learning, is difficult to apply. To eliminate this difficulty and accomplish the 3 aforementioned objectives we introduce in this paper TopoSim, a neural topology-preserving based geostatistical simulation algorithm, which integrates (1) Self-Organizing Map (SOM) and its updated version, Growing Self-Organizing Map (GSOM), and (2) ANN with an unsupervised competitive learning structure. The proposed simulation integrates in a single step dimensionality reduction and extraction of input data structure pattern. Connectivity between geostatistical simulation problems and machine learning tasks are explained and constructed for the first time. We perform TopoSim unconditioned geostatistical realizations, which show improvements in continuity and pattern reproduction when compared with previously developed single normal equation simulation (SNESIM) algorithms. We conclude that the geostatistical simulation task is essentially a machine learning problem, i.e., getting the model to learn from available data and subsequently using the model for prediction purposes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".