Bibliographic record
Abstract
The ICCST conference series had its origin sometime during 1969 or 1970, in a phone call from Professor Yoshiyuki Okamoto of New York University that I received at my Xerox Corporation laboratory. He had heard that 1 was engaged in editing a book on selenium chemistry[1]. In the course of the discussion, it also became apparent that we had acommon interest in selenium polymers. Okamoto then made the suggestion that we might jointly organize a symposium on the matter. As venue he suggested New York City where he had good contacts at the New York Academy of Sciences. My co-editor Dan Klayman and I had already assembled much of the necessary scientific contacts via the book project. Also, we (actually the publisher, Wiley Interscience) had received a $10,000 grant for the book from the Selenium and Tellurium Development Association (STDA). We again contacted that organization for a grant to defray some expenses. I can't remember details but, in exchange for half a promise of receiving financial consideration, we acquired a third co-chairman in the person of Eugene M. Elkin of Canadian Copper refineries, who would represent the interests of the STDA.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.587 | 0.379 |
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".