Mineralogical Society of Great Britain and Ireland
Bibliographic record
Abstract
At this time of year, we are busy planning for several meetings, including our participation in the EMC2020 meeting in Poland.We will also hold the first in our new series of meetings, entitled New Topics in Mineralogy, at the suggestion of President Bruce Yardley.These events are our main opportunity to interface with our members.Society staff will have a presence at several of the meetings below, so please come along to our booth to say Hello.Since this content was prepared, in January 2020, the impact of the coronavirus has been felt around the world, including by Societies and their personnel.Most of the meetings listed below have been postponed for obvious reasons.Normal service will resume as soon as possible.Council is currently looking at ways to improve our service to members, particularly early career members.We encourage you to send us your ideas.What would help you to get more from your membership?Let us know (kevin@minersoc.org),whether it be for publications, meetings, bursaries, online services, anything.And, in the spirit of "Ask not what your society can do for you … ", but rather what skills and talents could you bring to the society?We would especially appreciate input in terms of technical offerings, e.g.podcasts, webinars, video editing, etc.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.239 | 0.125 |
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".