Association of Applied Geochemists
Bibliographic record
Abstract
Aaron is a multifaceted geoscientist and technologist with 25 years of international experience, spanning technology development and research, global business development and strategy, and mining operations and mineral exploration.He is also a strong advocate and promoter of sustainability, diversity, and STEM pathways for the next generations entering the resources industry.Aaron's passion is centred around deep engagement and collaboration with the Mining Equipment, Technology, and Services (METS), Environmental, Social, and Governance (ESG), and research sectors for the delivery of Mining Industry 4.0 across the entire mine life cycle and value chain.This draws on his experience in material characterization, sensors, IIoT, analytics, robotics, and automation, as well as over a decade of operational experience.This aligns well with the emerging landscape of decarbonization, digital transformation, automation, and the future of work in the resources industry.Aaron is currently employed by Eurasian Resources Group (ERG) as the Head of Smart Exploration Technologies, concentrating on new opportunities in Saudi Arabia.Prior to this, he spent 15 years focusing on a wide range of mining and exploration technologies, including the adaption of systems used by NASA on the Mars Curiosity Rover.He was also an embedded researcher and project manager at MinEx CRC and Deep Exploration Technologies CRC, where he was a co-inventor of the Lab-At-Rig® system.Aaron spent his first decade working in a variety of management, operational, and exploration roles across a range of gold, nickel, and copper projects.Professionally, Aaron is a proud graduate of the Western Australian School of Mines, Kalgoorlie (WASM), and holds a Bachelor of Science in mineral exploration and mining geology with 1 st class honours.He is a registered professional geoscientist (AIG -RPGeo #10255 for Mining and Geochemistry) as well as a Fellow of the AusIMM, AIG, SEG, and AAG.Aaron is also a qualified snowboard instructor, and you can find him hitting the slopes during the winter in the USA, Canada, NZ, and Australia, as well as 4WD-ing, boating, and fishing with his family and friends throughout the warmer months.
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 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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.140 | 0.136 |
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".