Bibliographic record
Abstract
[1]Albanese,A.,Blomer,J.,Edmonds,J.,Luby,M.,Sudan,M.,1996.Priority encoding transmission.IEEE Trans.on Information Theory,42(6):1737-1744.[doi:10.1109/18.556670] [2]Bloemer,J.,Kalfane,M.,Karpinski,M.,Karp,R.,Luby,M.,Zuckerman,D.,1995.An XOR-Based Erasure-Resilient Coding Scheme.Technical Report ICSI TR-95-048. [3]Chou,P.A.,Wang,H.J.,Padmanabhan,V.N.,2003.Layered Multiple Description Coding.Proc.Int'l Packet Video Workshop.Nantes,France. [4]Klaue,J.,Rathke,B.,Wolisz,A.,2003.EvalVid-A Framework for Video Transmission and Quality Evaluation.Proc.of the 13th Internationnnal Conference on Modelling Techniques and Tools for Computer Performance Evaluation.Urbana Illinois,USA. [5]Lacan,J.,Fimes,J.,2004.Systematic MDS erasure codes based on vandermonde matrices.IEEE Communications Letters,8(9):570-572.[doi:10.1109/LCOMM.2004.833807] [6]Lacan,J.,Roca,V.,Peltotalo,J.,Peltotalo,S.,2005.Reed Solomon Error Correction Scheme.Work in Progress:〈draft-lacan-rmt-fec-bb-rs-00〉. [7]Leicher,C.,1994.Hierarchical Encoding of MPEG Sequences Using Priority Encoding Transmission (PET).Technical Report ISCI Lehrstuhl fur Kommunikationsnetze Technische Universitat Munchen,TR-94-058. [8]Liebl,G.,Wagner,M.,Pandel,J.,Weng,W.,2004.An RTP Payload Format for Erasure-Resilient Transmission of Progressive Multimedia Streams.Work in Progress:〈draft-iet f-avt-uxp-07〉. [9]Mohr,A.E.,Ladner,R.E.,Riskin,E.A.,2000.Approximately Optimal Assignment for Unequal Loss Protection.Conf.Image Processing.Vancouver,BC. [10]Ohm,J.R.,1999.Picture Signal Processing for MultimediaSystem.Script,Institute for Communications and Theoretical Electrical Engineering (in German). [11]Rizzo,L.,1997.Effective erasure codes for relaible computer communication protocols.ACMReview,27:24-36. [12]Roca,V.,Khallouf,Z.,Laboure,J.,2003.Design and Evaluation of a Low Density Generator Matrix (LDGM) Large Block FEC Codec.Fifth International Workshop on Networked Group Communication (NGC'03).Munich,Germany. [13]Shokrollahi,A.,2003.Raptor Codes.Digital Fountain Inc.,Te
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".