Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Exploration for deep-seated mineral deposits in mature mining camps requires integration of large and heterogeneous spatial data-sets. Traditionally, geological, geochemical, and geophysical observations are acquired, processed and analysed independently within separate spatial contexts or more commonly, for geochemical data, in non-spatial feature space. Although methodological developments are still in progress, 3D GIS (geographic information system) technologies already provide powerful tools that can be used to integrate such heterogeneous data-sets to visualize, compare, and characterize geological relationships in a more supportive interpretive environment. Importantly, this technology provides better opportunities to embed all these properties in a more robust geometric framework in which structural history and palaeogeographic setting can be taken into account. We present 3D GIS applications that aid in interpreting relationship patterns amongst faults, folds and geochemical trends. Examples from the Noranda mining region, a classic VMS mining camp, demonstrate the applicability of 3D GIS to support the discovery of new mineral resources at depth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.019 | 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 it