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//
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.042 |
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".