{"id":"W2955887570","doi":"10.33539/consensus.2016.v21n1.981","title":"De la televisión a la televisión extendida a partir de Marshall Mcluhan","year":2016,"lang":"es","type":"article","venue":"Consensus","topic":"Media and Digital Communication","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Art; Humanities; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001229862,0.0003351425,0.0003506392,0.0001558579,0.0002280984,0.001224126,0.001419815,0.000286815,0.00007176923],"category_scores_gemma":[0.002031158,0.0002643305,0.0001754545,0.0003477108,0.0009544639,0.0002200586,0.0006561204,0.0003082156,0.0005001288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001618156,"about_ca_system_score_gemma":0.0008042408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006378106,"about_ca_topic_score_gemma":0.000007349941,"domain_scores_codex":[0.9964433,0.001372404,0.0004656217,0.0005631192,0.0003462703,0.0008093087],"domain_scores_gemma":[0.9922564,0.005184858,0.0002119795,0.001705305,0.0001530691,0.0004883709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009638901,0.0003733141,0.02194504,0.0001830165,0.00009330375,0.0006579594,0.00181559,0.000002907208,0.03158198,0.1750061,0.009851481,0.7583929],"study_design_scores_gemma":[0.00269801,0.0003761844,0.1172754,0.001691254,0.000115897,0.001845634,0.0001563252,0.004940093,0.01313722,0.0414501,0.8150459,0.001268044],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8787132,0.005128579,0.02280117,0.01077622,0.0003942308,0.00046142,0.00005947947,0.0003969009,0.08126878],"genre_scores_gemma":[0.990869,0.0006383549,0.006439134,0.0005808329,0.0001667369,0.00003695117,0.000002635628,0.00003668278,0.001229703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8051944,"threshold_uncertainty_score":0.9999809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01997796198189017,"score_gpt":0.2886061875991821,"score_spread":0.2686282256172919,"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."}}