{"id":"W4393461521","doi":"10.5281/zenodo.10334778","title":"Caméra 35 mm Mitchell SS","year":2017,"lang":"fr","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Cinema and Media Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Art","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004407915,0.001422684,0.0007298715,0.002133174,0.0009901144,0.002461077,0.001157561,0.001198297,0.736691],"category_scores_gemma":[0.001606883,0.0008289038,0.0008273188,0.001375614,0.0003857207,0.002341146,0.002121689,0.001381511,0.4454249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005203444,"about_ca_system_score_gemma":0.0009444278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002891791,"about_ca_topic_score_gemma":0.004768992,"domain_scores_codex":[0.9996065,0.00003189679,0.00002461453,0.000121836,0.0001649899,0.0000500098],"domain_scores_gemma":[0.9992871,0.0001267252,0.00002724366,0.0001506079,0.0002905054,0.000117786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000209614,0.00004560932,0.0005126876,0.0004948463,0.00001239444,0.0003317546,0.0002558705,0.0008593946,0.00933893,0.007838328,0.7024093,0.2776912],"study_design_scores_gemma":[0.00002682682,0.00004957952,0.001245902,0.0001217161,0.00001019381,0.0005423944,0.0001418298,0.0007260029,0.002169139,0.001163067,0.9937836,0.00001979973],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"dataset","genre_scores_codex":[0.003115035,0.001619254,0.05412101,0.001142024,0.002831882,0.000749062,0.0180474,0.01414459,0.9042298],"genre_scores_gemma":[0.01876756,0.001364772,0.03494909,0.0005133378,0.0003122533,0.0003941719,0.01348667,0.004888799,0.9253234],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.736691,"threshold_uncertainty_score":0.3755783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05863387192748534,"score_gpt":0.252819471863733,"score_spread":0.1941855999362476,"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."}}