Society of Economic Geologists Silver Medal for 2008: Citation of Mark D. Hannington
Bibliographic record
Abstract
Dear SEG friends and guests: Every now and then a university professor has the good fortune to work with an exceptional student. I am such a professor and the exceptional student in the mid- to late 1980s was Mark Hannington, this year’s SEG Silver Medalist. I somehow convinced Mark to come to Toronto for graduate studies (actually, I bribed him by offering submersible dives). Mark was a member of a small team I had put together to explore the deep sea floor for hydrothermal sulfide deposits as natural laboratories for better understanding volcanic-hosted massive sulfide (VMS) ores that formed on ancient sea beds and are now on land. I supervised Mark’s M.Sc. and Ph.D. theses but use the word “supervised” lightly because Mark was a self-starter. He knew what needed to be done, figured out how to do it, and got on with the task at hand. In addition, he was an unusually talented writer, a skill that would serve him well for what was to come in his career. While at Toronto, Mark collaborated with Peter Herzig. Peter, a cosponsor of Mark’s Silver Medal nomination, was one of my postdocs and is now the director of IFM-GEO-MAR, the large German oceanographic institute in Kiel. Putting Mark and Peter together was one of the best decisions of my academic life because they did exceptional research and have …
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.062 |
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".