Bibliographic record
Abstract
ABSTRACT A Z-Axis Tipper Electromagnetic Technique (ZTEM) survey is an airborne natural source electromagnetic survey that relates the vertical magnetic field to the horizontal magnetic fields measured at a reference station on the ground. For large airborne surveys, the high number of cells required to discretize the entire area at a reasonable resolution can make the computational cost of inverting the data set all at once prohibitively expensive. We present an iterative methodology that can be used to invert large natural source surveys by using a combination of coarse and fine meshes as well as a domain decomposition that allows the full model area to be split into smaller subproblems, which can be run in parallel. For this procedure, the entire data set is first inverted on a coarse mesh. The recovered coarse model and computed fields are used as starting models and source terms in the subsequent tiled inversions. After each round of tiled inversions, the tiles are merged together to form an update model, which is then forward modeled to determine if the model achieves the target misfit. Following this procedure, we first invert the data computed from a large synthetic model of the Noranda mining camp. The inverted models from this example are consistent among our different tiling choices. The recovered models show excellent large-scale agreement with the true model and they also recover several of the mineralized zones that were not apparent from the initial coarse inversion. Finally, we invert a 30×30 km block of the 2010 ZTEM survey collected over the porphyry Pebble Deposit in Alaska. The inverted ZTEM results are consistent with the results obtained using other electromagnetic methods.
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 | 0.000 |
| 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 | 0.001 |
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 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".