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 Alteration mineral assemblages are important to the understanding of and exploration for hydrothermal ore deposits. Conventional mapping tools may not identify fine-grained minerals or define important compositional variations. Field portable shortwave infrared (SWIR) spectrometers solve some of these problems and provide a valuable tool for evaluating the distribution of alteration assemblages. Spectrometers such as the PIMA-II allow rapid identification of minerals and mineral-specific variations at a field base. Mineral assemblages, integrated with other exploration data, are then used to target drill holes and guide regional exploration programs. Data collection must be systematically organized and carried out by a trained operator. Analysis of data sets requires the use of spectral reference libraries from different geological environments and may be aided in some cases by computer data processing packages. Integration of results with field observations, petrography, and X-ray diffraction analysis is necessary for complete evaluation. The PIMA (portable infrared mineral analyzer) has been used successfully in the high-sulfidation epithermal, low-sulfidation epithermal, volcanogenic massive sulfide (VMS) and intrusion-related environments. Case studies from these systems demonstrate the ability to rapidly acquire and process SWIR data and produce drill logs and maps. The resulting information is critical for targeting.
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) | 0.000 | 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 it