{"id":"W2581315808","doi":"10.5539/mas.v11n4p1","title":"The Study of Semantic Analysis on Intelligence Research under the Environment of Big Data","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data science; Semantic technology; Intelligence analysis; Semantic analysis (machine learning); Semantic computing; Visualization; Meaning (existential); Business intelligence; Big data; Information retrieval; Strengths and weaknesses; Semantic Web; Knowledge management; Artificial intelligence; Data mining; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","open_science"],"consensus_categories":["sts"],"category_scores_codex":[0.01478278,0.00008773895,0.0001993811,0.0002850009,0.002983713,0.0005327549,0.02026295,0.00003329294,0.00001101217],"category_scores_gemma":[0.001093161,0.00003987247,0.00003503133,0.00190295,0.005346589,0.000147692,0.005881051,0.0002427629,0.00005562726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002671234,"about_ca_system_score_gemma":0.00008178812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000222653,"about_ca_topic_score_gemma":0.0004749762,"domain_scores_codex":[0.9949879,0.00007740365,0.0004786932,0.0008523431,0.003295772,0.0003078651],"domain_scores_gemma":[0.9859095,0.001554145,0.0004244077,0.01194865,0.0001185024,0.00004485908],"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":[0.0000244025,0.0005984422,0.002972102,0.000001381305,0.00007331675,4.276833e-7,0.001123375,0.004894952,0.02691225,0.06163673,0.0003599552,0.9014027],"study_design_scores_gemma":[0.0001995268,0.0002474683,0.164665,0.000008066184,0.0001214243,4.474215e-7,0.04916111,0.3945293,0.01549498,0.3727152,0.002619423,0.0002381068],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7387347,0.00005405952,0.2525,0.003724294,0.00007061196,0.0008903126,0.00005262567,0.00001480168,0.003958553],"genre_scores_gemma":[0.9996105,0.00005045308,0.0001759643,0.00001478742,0.000009278637,0.00004040189,9.660768e-7,0.000003011229,0.00009465338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9011645,"threshold_uncertainty_score":0.9983143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6982545098271963,"score_gpt":0.4996123582502159,"score_spread":0.1986421515769803,"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."}}