Automatic Labeling Method of Geological Codes Based on Multi-factor Optimization
Bibliographic record
Abstract
Automatic labeling of geological codes is an important part of automatic mapping of geological maps. A multi-factor optimization based automatic labeling method is proposed to address the issue of existing polygon feature label placement methods being unable to achieve multi-position placement in geological codes. Firstly, classify geological bodies based on whether they can accommodate geological codes within the area; Subsequently, sufficient candidate positions are obtained for the geological body and a candidate position evaluation method that integrates multiple factors is proposed; Finally, based on the candidate position evaluation method, sorting and particle swarm optimization algorithms are used to achieve single position labeling and multi-position labeling. The simulation experiment compares it with existing polygon feature placement methods from the perspectives of coverage, shape, and method. The method proposed in this paper can automatically placement non-conflicting geological codes for geological bodies of different shapes and areas, and has better performance than existing methods in complex geological bodies.
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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 | 0.000 |
| 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 | 0.000 | 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".