Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
- There are six folders corresponding to 6 types of BPPV disorders.- Each folder has one sample. Each class is specified by the typical movement of the eye. +) Lt_Geo_BPPV: eye beats toward the ground, beats stronger to the left side (turn head left).+) Rt_Geo_BPPV: eye beats toward the ground, beats stronger to the right side (turn head right).+) Lt_Apo_BPPV: eye beats toward the sky, beats stronger to the left side (turn head right).+) Rt_Apo_BPPV: eye beats toward the sky, beats stronger to the right side (turn head left).+) Lt_PC_BPPV: eye beats slightly up and rotates clockwise (often at hanging left position).+) Rt_PC_BPPV: eye beats slightly up and rotates counter-clockwise (often at hanging right position). - Each folder contains a video (*.avi format) and a temporal labeled file (*.mat format).- The '*.mat' file can be read by matlab, that stores the 'fr' and 'label' variable.- Each action of the patient is labeled with the start and end frame ID. - Actions are marked as follows: +) 'sitting' is labeled as '1' +) 'head turns left' is labeled as '2' +) 'head turns right' is labeled as '3' +) 'head hanging left' is labeled as '4' +) 'head hanging right' is labeled as '5' +) 'lying down' is labeled as '6' +) 'head bending' is labeled as '7' An example: postures of patient are labeled in the 'Class2_100030.mat' (Lt_Geo_BPPV folder) as follows:'fr''label'22211035116142166321781318373220622271227751319013208533135378013947139644406044496178051103061186651947525776330764067 TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.330 | 0.056 |
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 it