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Record W4251387265 · doi:10.3395/reciis.v3i4.236pt

Tecnologias de Internet em uma plataforma de colaboração para a pesquisa médica

2009· article· pt· W4251387265 on OpenAlexaff
Miriam A. M. Capretz, Maria Beatriz Felgar de Toledo, Marcelo Fantinato, Diego Zuquim Guimarães Garcia, Shuying Wang, David S. Allison, Olga Nabuco, Marcos Rodrigues, Rodrigo Bonacin, Emma Chen Sasse, Itana Maria de Souza Gimenes, Americo Brigido Cunha

Bibliographic record

VenueReciis · 2009
Typearticle
Languagept
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsWestern University
Fundersnot available
KeywordsWorkflowReuseWorld Wide WebMetadataThe InternetProtocol (science)Computer scienceDomain (mathematical analysis)Knowledge managementEngineeringDatabaseMedicine

Abstract

fetched live from OpenAlex

Web technologies have changed software development. The changes affect a full range of applications as well as the way users interact with computers. In the health domain, clinical research demands a lot of investment, effort and information in order to safely commercialize a new drug. The WebInVivo project aims at providing automated support for clinical research based on Web technologies. It includes mechanisms for sharing and reusing clinical trial information, such as protocols, protocol data, workflows and workflow metadata and for controlling the protocol life cycle, from modeling to execution. In this project, knowledge from the biomedical area permeates three segments of Brazilian society: (a) research and development, (b) health agents, and (c) the population. This knowledge will be made available through social networks for these segments of Brazilian society.

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 imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.006
Science and technology studies0.0030.006
Scholarly communication0.0110.016
Open science0.0020.006
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.003

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 head0.032
GPT teacher head0.287
Teacher spread0.255 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2009
Admission routes1
Has abstractyes

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