2025 OIC Manufacturing “Road Runner” Challenge [Invited]
Bibliographic record
Abstract
Measurements of the fabricated optical filters submitted to the Manufacturing Challenge (MC) organized for Optica’s Topical Meeting on Optical Interference Coatings held in Tucson, AZ, in May 2025, are presented. For this ninth MC, participants were asked to design and deposit, on a provided substrate, a filter with transmittance ( T ) and front- and back-reflectance ( R ) spectra (with light incident from the front and back, respectively) matching target values specified in the 400 nm to 1100 nm spectral interval. The challenge problem was selected to require at least one absorbing layer in the design in order to ensure a good performance. Six teams from three countries participated and submitted a total of 10 samples, all coated on both sides, with total thicknesses ranging from 3589 nm to 8088 nm and comprised of 31 to 135 layers. The entries were measured at four independent laboratories; the resulting merit function values obtained when comparing the measured spectra to the targets were used to rank the filters and determine a winner. In the analysis of the results, observations will be made about coating design, fabrication, and measurement.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.023 |
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".