ILD Detector Optimisation/Software WG Phone Meeting
Bibliographic record
Abstract
Topic: ILD Optimisation \nDate: Wednesday, 26 May 2010 \nTime: 12:45, GMT Summer Time (London, GMT+01:00) \nMeeting Number: 750 685 916 \nMeeting Password: ildopt \n\n\n------------------------------------------------------- \nTo join the online meeting (Now from iPhones and other Smartphones too!) \n------------------------------------------------------- \n1. Go to https://ilc.webex.com/ilc/j.php?ED=123402727&UID=0&PW=NYWZlNDU1OWI3&RT=MTgjMjE%3D \n2. Enter your name and email address. \n3. Enter the meeting password: ildopt \n4. Click "Join Now". \n\nTo view in other time zones or languages, please click the link: \nhttps://ilc.webex.com/ilc/j.php?ED=123402727&UID=0&PW=NYWZlNDU1OWI3&ORT=MTgjMjE%3D \n\n------------------------------------------------------- \nTo join the audio conference only \n------------------------------------------------------- \nTo receive a call back, provide your phone number when you join the meeting, or call the number below and enter the access code. \nCall-in toll-free number (US/Canada): 1-866-699-3239 \nCall-in toll number (US/Canada): 1-408-792-6300 \nGlobal call-in numbers: https://ilc.webex.com/ilc/globalcallin.php?serviceType=MC&ED=123402727&tollFree=1 \nToll-free dialing restrictions: http://www.webex.com/pdf/tollfree_restrictions.pdf \n\nAccess code:750 685 916 \n\n------------------------------------------------------- \nFor assistance \n------------------------------------------------------- \n1. Go to https://ilc.webex.com/ilc/mc \n2. On the left navigation bar, click "Support". \n\nYou can contact me at: \nthomson@hep.phy.cam.ac.uk \n44-1223-765122 \n
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.417 | 0.447 |
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".