Bibliographic record
Abstract
Super-conducting Quantum Interference Devices (SQUIDs) are tiny sensors that detect and measure very small magnetic fields. As part of an ATD-GRG research project, the Institut für Physikalische Hochtechnologie (IPHT) in Jena, Germany, have developed a Low Temperature SQUID (LTS) ground Transient ElectroMagnetic (TEM) system for Anglo to further strengthen the company’s mineral exploration capabilities. A brief history of the technology development is illustrated with results from various field tests. Early field trials conducted in Germany in 2002 showed good promise, but some system problems. Partial redesign and good applied science led to successful field testing and comparison of LTS, HTS and conventional coil receivers in Sweden in 2003. Further field tests on the Western Australian Nickel belt in 2003 and 2004 proved the system’s field-worthiness and that using liquid Helium as a coolant poses no serious logistical problems even in such a harsh environment. Undisputed proof of superior signal-to-noise capabilities over HTS, Fluxgate and coil sensors was again evident as well as the advantages of using LTS sensors for detecting conductive targets at depth or below conductive cover, hitherto a severely limiting constraint on exploration for conductive ore-bodies. Because of the better S/N stacking time is reduced and production is 4 to 10 times faster depending on the environment. Some spurious system response problems were more prominent during tests in resistive terrains in South Africa, but have subsequently been solved. The LTS TEM SQUID system has now been recognized as a major breakthrough with potential to give Anglo exploration teams a significant strategic advantage over competitors. An agreement has been signed with IPHT that provides Anglo with exclusive rights to the project technology for a ten year period following its development. Three systems are being deployed by our base metal exploration teams in Australia and Canada, while a fourth will be delivered later in 2005 for on-going exciting development work in Southern Africa.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".