Bibliographic record
Abstract
Thank you for your kind words, Rob. I will come back to you and the University of Alberta shortly. First, I must emphasize that this award is about the scientific team responsible for the success of the AIRIE Program. I could not have accomplished this without the support of Richard Markey and John Morgan, who came to Colorado State University with me after the U.S. Geological Survey terminated our developmental work on Re-Os and the employment of the persons leading that effort. But we had a place to land, a place to bring our excitement and our early success, thanks to Judy Hannah, at that time the new department head of Geo-sciences at Colorado State in Fort Collins. In addition, we could not have managed without the vision and creative support provided by Paul Sims, an SEG legacy and here with us today. Just as a concerto requires a willing orchestra, it is rare that this kind of recognition can be attributed to solo work carried out by an individual. Thus, it was the collective effort of these persons—Rich, John, Judy, and Paul, coupled with my own dogged determination, that built the AIRIE Program. We all knew that Re-Os would change the field of economic geology. Two wonderful post-docs, Anders Schersten and Gang Yang, plus excellent students such as Aaron Zimmerman, sitting with us today, also contributed enormously to AIRIE. This month the AIRIE Program celebrates 10 years of relevant science—from technique development to industry applications and far beyond. The most important ingredient for success is people—the right people, broad and visionary thinkers, complementary in ability, and excited about working together for a common goal. In our profession, that goal is discovery, from the atomic to geologic scale. The second ingredient for success is funding to realize goals. I can …
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.129 | 0.088 |
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".