Bibliographic record
Abstract
The MobileMT system measures natural-field EM data, acquiring three-component airborne magnetic-field data while monitoring the horizontal electric field at a base station. Data sensitivities of this configuration are similar to those of TE-mode MT data. Synthetic 2D modeling results across a conductive body demonstrate how the host conductivity and the location of the base station affect the conductor response. The strong sensitivity of the data to the conductivity structure at the base station results in inversion results being quite sensitive to the inversion start models used. MobileMT survey data from the Grindstone Copper-Nickel-Cobalt project, New Brunswick indicate a strong correlation between conductivity highs and magnetic anomalies. 2D inversions suggest conductivity structures to be detected up to 1000–2000 m depth. Model results from a VMS exploration survey across the Broken Evil prospect, Northern Ontario show good correlation with conductivity structures derived from a previous VTEM survey, including the location of a suspected VMS body. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 8:30 AM Presentation Time: 11:00 AM Location: 301B Presentation Type: Oral
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.019 |
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".