Bibliographic record
Abstract
Synthetic ZTEM responses computed with 2D and 3D algorithms are compared. Excellent agreement is observed between 2D and 3D responses for structures with long strike lengths. Using the 2D inversion algorithm on synthetic 3D responses indicates artifacts being introduced when limited strike length is present: the conductivity of structures such as resistive hills and conductive structures is underestimated. Synthetic 3D models of a conductive target in a resistive host are used to demonstrate the effect of target strike length and target conductance on the ZTEM response. With an increase in strike length or conductance, the amplitude of the ZTEM responses increases. However, the amplitudes increase unevenly over the range of ZTEM frequencies. For low-conductance targets with short strike lengths the strongest responses are observed at the highest frequencies, whereas for high-conductance targets with long strike lengths the strongest responses are observed at the lowest frequencies. In the presence of a conductive overburden, responses are reduced by overburden blanking, but they can be boosted by current channeling if the conductor is in contact with the overburden. 2D and 3D inversion results of ZTEM survey data from Forrestania, Western Australia, show good agreement. The derivation of pseudo-profiles allowed for the 2D inversion of across-line data, which resulted in the modeling of structures not mapped by the in-line data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".