ILD Detector Optimization WG Phone Meeting
Bibliographic record
Abstract
Topic: ILD Detector Optimization\n o discussion of optimization section in LoI\n\nDate: Wednesday, 25 February 2009\nTime: 13:00, GMT Standard Time (GMT -00:00, London)\nMeeting Number: 750 963 199\nMeeting Password: ildopt\n\nPlease click the link below to see more information, or to join the meeting.\n\n-------------------------------------------------------\nTo join the online meeting\n-------------------------------------------------------\n1. Go to https://ilc.webex.com/ilc/j.php?ED=112862432&UID=0&PW=56763aa15822503c313d\n2. Enter your name and email address.\n3. Enter the meeting password: ildopt\n4. Click "Join Now".\n\n-------------------------------------------------------\nTo join the meeting on iPhone\n-------------------------------------------------------\nGo to\nwbx://ilc.webex.com/ilc?MK=750963199&MPW=e5d2e901b4217824d1984d90dad15e1a1c7a254625fea06a0ee925c03d38c2bc\n\nDon't have the iPhone WebEx application yet?\nGo to http://itunes.apple.com/WebObjects/MZStore.woa/wa/viewSoftware?id=298844386\n\n\n-------------------------------------------------------\nTo join the teleconference only\n-------------------------------------------------------\nProvide your phone number when you join the meeting to receive a call back. Or, call the number below and\nenter the meeting number.\nCall-in toll-free number (US/Canada): 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=112862432&tollFree=1\nToll-free dialing restrictions: http://www.webex.com/pdf/tollfree_restrictions.pdf\n\n-------------------------------------------------------\nFor assistance\n-------------------------------------------------------\n1. Go to https://ilc.webex.com/ilc/mc\n2. On the left navigation bar, click "Support".\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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.314 | 0.196 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".