ILD Software Working Group Phone Meeting
Bibliographic record
Abstract
**** You can forward this email invitation to attendees **** \nHello ,\n \nAkiya Miyamoto changed the meeting information.\n \nTopic: ild-detector-optimization\nDate: Wednesday, March 2, 2011\nTime: 10:00 pm, Japan Time (Tokyo, GMT+09:00)\nMeeting Number: 755 602 859\nMeeting Password: ildopt\n \n-------------------------------------------------------\nTo join the online meeting (Now from mobile devices!)\n-------------------------------------------------------\n1. Go to https://ilc.webex.com/ilc/j.php?ED=129257777&UID=0&PW=NZmYzZmE0MGZk&RT=MiM0OQ%3D%3D \n 2. If requested, enter your name and email address.\n3. If a password is required, enter the meeting password: ildopt\n4. Click "Join".\n5. Follow the instructions that appear on your screen.\n \nTo view in other time zones or languages, please click the link: \n https://ilc.webex.com/ilc/j.php?ED=129257777&UID=0&PW=NZmYzZmE0MGZk&ORT=MiM0OQ%3D%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=129257777&tollFree=1 \n Toll-free dialing restrictions: http://www.webex.com/pdf/tollfree_restrictions.pdf \n Access code:755 602 859\n-------------------------------------------------------\nFor assistance\n-------------------------------------------------------\n1. Go to https://ilc.webex.com/ilc/mc \n 2. On the left navigation bar, click "Support".\nYou can contact me at: \n akiya.miyamoto@kek.jp \n 81-29-864-5371\nTo update this meeting to your calendar program (for example Microsoft Outlook), click this link: \n https://ilc.webex.com/ilc/j.php?ED=129257777&UID=0&ICS=UMI&LD=1&RD=2&ST=1&SHA2=LNDjK3OFGUK2RF354Uk2wpHB2xrmo-B1ca-eFj78R8U=&RT=MiM0OQ%3D%3D \n WebEx will automatically setup Meeting Manager for Windows the first time you join a meeting. To save time, you can setup prior to the meeting by clicking this link: \n https://ilc.webex.com/ilc/meetingcenter/mcsetup.php \n The playback of UCF (Universal Communications Format) rich media files requires appropriate players. To view this type of rich media files in the meeting, please check whether you have the players installed on your computer by going to https://ilc.webex.com/ilc/systemdiagnosis.php \n http://www.webex.com \n CCP:+14087926300x755602859#\nIMPORTANT NOTICE: This WebEx service includes a feature that allows audio and any documents and other materials exchanged or viewed during the session to be recorded. By joining this session, you automatically consent to such recordings. If you do not consent to the recording, discuss your concerns with the meeting host prior to the start of the recording or do not join the session. Please note that any such recordings may be subject to discovery in the event of litigation. \nPlease contact Akiya or Frank if you have contributions
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.680 | 0.811 |
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".