{"id":"W6958889580","doi":"10.7479/hxh6-0109","title":"Impact erfassen und darstellen: Leitfaden zur Ermittlung von Indikatoren musealer Wissenstransferleistung","year":2022,"lang":"de","type":"dataset","venue":"Museum für Naturkunde Berlin - Leibniz-Institut für Evolutions- und Biodiversitätsforschung","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Minnow Environmental (Canada)","funders":"","keywords":"Cost analysis; Context (archaeology); Performance indicator; Cost effectiveness","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":[0.002102225,0.001362772,0.0007286057,0.006103943,0.0008840141,0.002275439,0.001844531,0.001734146,0.02252566],"category_scores_gemma":[0.01242923,0.0005280647,0.001378116,0.008292088,0.0004213768,0.001634276,0.002533883,0.001521435,0.02127197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002725773,"about_ca_system_score_gemma":0.002955037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05301836,"about_ca_topic_score_gemma":0.143456,"domain_scores_codex":[0.9981894,0.0003903997,0.0002299643,0.0003872443,0.000534612,0.0002682932],"domain_scores_gemma":[0.9954513,0.001743452,0.0004727668,0.0007321718,0.001347371,0.0002530151],"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":[0.0002676007,0.00005340476,0.008203507,0.003128283,0.0001062281,0.00008573858,0.000360877,0.001168724,0.0005071137,0.002013463,0.9692348,0.01487039],"study_design_scores_gemma":[0.0001413993,0.0000275774,0.0295969,0.0008853746,0.00006854298,0.00008909509,0.0008576043,0.0009263686,0.0008333522,0.002213251,0.9643,0.00006053993],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001457631,0.0003509255,0.0003225677,0.0002603607,0.00005119752,0.00003619799,0.994437,0.0003382949,0.002745776],"genre_scores_gemma":[0.003463501,0.0002303495,0.001375311,0.00006443405,0.00001257238,0.0002141709,0.9923066,0.0000917933,0.002241232],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05301836,"threshold_uncertainty_score":0.1054195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0224942586462359,"score_gpt":0.3229258199593584,"score_spread":0.3004315613131225,"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."}}