{"id":"W1996869006","doi":"10.4028/www.scientific.net/amr.760-762.1703","title":"Semantic Inner Product Based Web Service Matchmaking Method","year":2013,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Computer science; Semantic Web Stack; Social Semantic Web; World Wide Web; Web service; Semantic Web; Service (business); Information retrieval; Product (mathematics); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003009605,0.0009178459,0.0008838009,0.003185221,0.00144387,0.002699349,0.001902834,0.001268481,0.006035264],"category_scores_gemma":[0.003978333,0.0006524527,0.001690521,0.002448939,0.001234525,0.004443861,0.002137659,0.001259165,0.002650803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001024292,"about_ca_system_score_gemma":0.001951721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001848917,"about_ca_topic_score_gemma":0.001078055,"domain_scores_codex":[0.9939627,0.001168843,0.0005164801,0.001245454,0.002877289,0.0002292109],"domain_scores_gemma":[0.9981706,0.0004534303,0.0001145452,0.0004794134,0.0006925538,0.00008945358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004721039,0.0003694583,0.001750828,0.0007087639,0.0002553446,0.0008479808,0.001336421,0.02514559,0.0421561,0.251306,0.01093482,0.6647167],"study_design_scores_gemma":[0.0001563636,0.00025455,0.0009296517,0.0001128248,0.0002835868,0.002158685,0.0004061742,0.6131666,0.1245006,0.133598,0.1242161,0.0002168259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004306776,0.0001077147,0.9880583,0.00009464421,0.00005424045,0.0001540091,0.000078531,0.00181977,0.005326073],"genre_scores_gemma":[0.1535755,0.0003745566,0.8331285,0.0001371996,0.00007886953,0.0003887177,0.0006525338,0.0004332195,0.01123101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006035264,"threshold_uncertainty_score":0.02019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03294728403008663,"score_gpt":0.3526913630102609,"score_spread":0.3197440789801743,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}