A history of the CSIRO’s development of high temperature superconducting rf SQUIDs for TEM prospecting.
Bibliographic record
Abstract
Over the past 14 years, CSIRO Industrial Physics has developed High Temperature Superconducting (HTS) SQUID (Superconducting Quantum Interference Device) sensor systems for TEM prospecting. Initially this work was done in collaboration with BHP P/L, now BHP Billiton, and some early successes were achieved. Collaboration with BHP ceased in 1998 after completion of a series of airborne trials. Interest in the rf SQUID sensor was revived in 2000, when it was successfully used to delineate targets at Falconbridge Ltd.’s Raglan, Quebec, mine site. As a result, CSIRO entered into a contract to build a ruggedised version of the SQUID sensor system for Falconbridge’s use under a rental agreement. Since September 2001, a number of CSIRO SQUID systems have been built and deployed over three continents. A local Australian firm, Outer-Rim Developments, has been licensed by CSIRO to manufacture the rf SQUID systems now called LANDTEM™. Technology transfer from CSIRO to Outer-Rim Developments was facilitated by Outer-Rim having a sub-contractor work within CSIRO for a number of months.In this paper, the results from some of the seminal SQUID surveys are presented and discussed. Finally a direct comparison between the noise performance of CSIRO’s HTS SQUID sensors and a Bartington flux-gate sensor is presented.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".