Exploring for graphite using a new ground-based time-domain electromagnetic system
Bibliographic record
Abstract
With the proliferation of battery-powered technology, such as cell phones, laptops, and electric cars, the need for graphite is expected to increase as it plays a crucial role in batteries and fuel cells.The mineral is already used for many applications where its unique properties make it difficult or sometimes impossible to replace.As the value of graphite increases, small graphite deposits that were not economically feasible in the past could become viable.Their small size requires exploration methods with high spatial resolution able to accurately detect and delineate the deposits.We present the IMAGEM system, a high-resolution ground-based time-domain electromagnetic system, whose high spatial resolution allows it to accurately delineate small deposits, on the order of a few metres thick.We perform forward modelling and inversion using a 1D modelling software program, AarhusInv, and present the results of case studies where the system was used for graphite exploration.The IMAGEM system was able to differentiate multiple distinct anomalies where the classic MaxMin system could only outline one anomaly.The IMAGEM system is still being developed and shows great potential for detecting small near-surface (depth less than ~20 m) highly-conductive bodies, such as graphite deposits.AarhusInv provides great flexibility in system configuration, and proves to be an effective program to model the IMAGEM system.iii
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".