Bibliographic record
Abstract
SummaryA mine development in the high Andes of north-western Argentina requires a supply of fresh water for various uses, the largest being mineral processing. Complementary survey techniques in a gravel basin have provided vastly different and sometimes contradictory results. Mapping of the basin geometry proceeded via downhole resistivity logging, moving loop ground EM, and galvanic resistivity. The expected layering of low resistivity gravel fill over high resistivity basement rock, which was demonstrated by prior downhole logging, was turned on its head by surface electrical and EM surveys. Furthermore, subsequent borehole resistivity logs, which do not reach bedrock, support the increase of conductivity with depth, while water sampling indicates this is not due to greater salinity. Surface nuclear magnetic resonance was trialled for the direct detection of the upper part of the water table. The majority of these data were contaminated by mysterious noise sources in an area hundreds of kilometres from the nearest atmospheric activity, and fifty kilometres from the closest settlement. False negative readings appear to be common. The most recent drilling, demonstrating much deeper gravel than previously drilled or imagined, may explain an inability to image the bottom, but the reason for the deep conductivity remains a mystery.
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".