Geophysical transects in the Abitibi greenstone belt of Canada from the mineral-exploration-oriented Metal Earth project
Bibliographic record
Abstract
Abstract The Metal Earth project integrates geophysics, geology, geochemistry, and geochronology to improve the understanding of metal endowment in Precambrian terranes. Magnetics (airborne), gravity, magnetotellurics, and reflection seismic methods are the primary geophysical tools employed. Data were collected along 13 transects in the initial phase of the project. All geophysical tools are crucial for understanding the structure of the shallow, middle, and deeper crust and identifying pathways along which the constituents of critical minerals might have migrated from a source to a deposit. The magnetic data are used predominantly to help map the geology away from the transects, and the gravity data are useful for extending the near-surface geology to depths up to 8 km. The magnetotelluric data show the upper Archean crust to about 10 km as highly resistive, except for some conductive subvertical zones that correspond to major deformation zones, many of which are known to be metalliferous. This suggests that these conductive zones could have been hydrothermal fluid pathways feeding the mineral deposits. These zones can be traced to larger horizontal conductive zones in the midcrust. The seismic reflection data are consistent with and complement this: the upper crust is primarily nonreflective; however, the midcrust shows many horizontal reflectors, usually with a consistent dip to the north. Processing crooked-line seismic data is problematic, and techniques have been developed to improve the imaging, including multifocusing, 3D processing, full-waveform inversion, and cross-dip moveout methods. Passive seismic data have also been collected. Ambient-noise surface-wave tomography can be used to infer broad zones of similar seismic velocity between major reflectors, while receiver function analysis has been used to identify deeper structures such as horizontal features at or below the Moho and a dipping structure evident to about 70 km 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.
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 | 0.000 | 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 | 0.000 |
| 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 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".