Application of GIS-Model Builder Technology for National Mineral Resource Assessment
Bibliographic record
Abstract
Mineral resources quantitative assessment involves processes of constructing models of various types from conceptual model,logical model,and mathematical model to GIS model etc..Large project like the national project for mineral resource potential assessment currently under operation by the Chinese Ministry of Land and Resources also involves interdisciplinary research teams specialized in geology,mineral deposits,mathematical model and GIS operation etc..It is essential to provide a common platform for these people to exchange ideas and make collective decisions on constructing geological concept models and quantitative models and on GIS procedures for model implementation with actual data input and output.This paper proposes a model builder technology to serve the above purpose.Sophisticated functions such as model iteration and user-machine interaction are developed and implemented in GeoDAS GIS.Case studies of information extraction,spatial analysis,prediction unit delineation and posterior probability mapping involved in gold mineral deposit prediction in Nova Scotia,Canada are covered to illustrate how model builder technology can be used in the whole processes of mineral resource assessment.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".