{"id":"W1639305740","doi":"10.22230/cjc.2013v38n3a2735","title":"Logistical Media: Fragments from Radar’s Prehistory","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Intelligence, Security, War Strategy","field":"Social Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Prehistory; Radar; French horn; Gesture; Computer science; Sociology; Telecommunications; History; Media studies; Archaeology; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007199177,0.0003670681,0.0002681561,0.003360423,0.004470134,0.005108717,0.0005732794,0.0009164586,0.01089992],"category_scores_gemma":[0.002992483,0.0002519421,0.0001541151,0.003759253,0.005483296,0.00370627,0.002916868,0.002189555,0.001861884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004181829,"about_ca_system_score_gemma":0.001078046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01132024,"about_ca_topic_score_gemma":0.01859723,"domain_scores_codex":[0.9993612,0.0001852111,0.0000272289,0.00008628877,0.0002007616,0.0001393183],"domain_scores_gemma":[0.9987831,0.0005673252,0.0002870828,0.0001071201,0.0001332145,0.0001222118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003002143,0.0001039634,0.007907505,0.0005097208,0.00002594557,0.004630479,0.2123763,0.0005364777,0.001813503,0.5100377,0.02295096,0.2388072],"study_design_scores_gemma":[0.00001871027,0.00008066861,0.03521205,0.0007242886,0.00002428698,0.002971912,0.03880918,0.0002633999,0.0008083823,0.02300802,0.8980357,0.00004335391],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.20591,0.0299359,0.002723097,0.00602364,0.0004132803,0.00007083351,0.0003948011,0.00008796411,0.7544405],"genre_scores_gemma":[0.9556051,0.01222728,0.0009478758,0.0006157543,0.0003929979,0.00002924286,0.0002866294,0.00007874982,0.02981644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9955299,"threshold_uncertainty_score":0.03646392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04075439333470807,"score_gpt":0.2895463509708709,"score_spread":0.2487919576361629,"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."}}