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