Bibliographic record
Abstract
This Workshop has been in the making for over two years from the first time that Prof. Silvano Donati asked me to host the 7thInternational Workshop on Fibre Optics and Passive Components (WFOPC2011) in Montreal, the first time ever in North America. It seemed far away at that time, but it was soon realised that the Conference calendar for the months of May–July were extremely crowded. After a couple of false starts and reiteration with organizing colleagues in its planning, we soon settled on the final dates of 13–15 July 2011, to be immediately followed by meeting on Nonlinear Optics in Hawaii (17–22 July 2011) and the IEEE Summer Topical on Mid-IR Fibres also in Montreal (18–20 July 2011). Although holding a meeting collocated with another seems attractive at first, however, neither benefit due to divided attention and conflicting programs, especially if the venues are at different locations in the city. It was thus decided that these would run sequentially and the same model has been applied to the program of WFOPC2011, with sequential deliveries of invited talks to give the presenters the undivided attention of the attendees.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.609 | 0.597 |
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".