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Topic Segmentation : A First Stage to Dialog-Based Information Extraction.

2001· article· en· W21176108 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNLPRS · 2001
Typearticle
Languageen
FieldComputer Science
TopicSpeech and dialogue systems
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSegmentationComputer scienceDialog boxInformation extractionArtificial intelligenceNatural language processingHidden Markov modelSpeech recognitionWorld Wide Web

Abstract

fetched live from OpenAlex

This study was designed to investigate the role of opioid receptors, gamma-aminobutyric acid (GABA) receptors, mast cells and histamine receptors (H(1) subtype) in the seizurogenic effect of amisulpride on mice. A single injection of amisulpride (180 mg/kg) was employed to evaluate the seizurogenicity of the drug in mice. Seizures were assessed in terms of a composite seizure severity score (SSS), time of the onset of straub-like tail, onset of jerky movements of whole body, convulsions and death. Amisulpride administration (180 mg/kg) induced a significant pro-convulsant effect in mice as measured in terms of the SSS (21.12 ± 2.71) and a significant decrease in the time latency of the onset of straub-like tail (132.45 ± 12.31), jerky movements of whole body (153.28 ± 14.12), convulsions (184.97 ± 13.11) and death (100%). Moreover, prior administration of naloxone, cetrizine, sodium cromoglycate and gabapentin, respectively, attenuated this seizurogenic activity that amisulpride exerted on mice (p < 0.05). Therefore, it may be suggested that amisulpride exerts a seizurogenic effect on mice possibly via an opioid receptor activation-dependent release of histamine from the mast cells and a simultaneous inhibition of GABA release.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.016
GPT teacher head0.256
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it