{"id":"W6921870015","doi":"10.7910/dvn/litfvn","title":"Penguatan local value chain: analisis pembiayaan hijau terhadap comparative trade CPO di Malaysia","year":2023,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Distributed lag; Consumption (sociology); Quarter (Canadian coin); Production (economics); Value (mathematics); Variable (mathematics); Variables","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":[],"consensus_categories":[],"category_scores_codex":[0.0003647154,0.0003269123,0.000197924,0.003904929,0.0002504787,0.001162753,0.0002497018,0.0001508771,0.007784734],"category_scores_gemma":[0.001269012,0.0001314426,0.0002943116,0.009966651,0.0002225867,0.001001872,0.0007200849,0.0003186903,0.001718894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006563597,"about_ca_system_score_gemma":0.0005916585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02670558,"about_ca_topic_score_gemma":0.02815108,"domain_scores_codex":[0.9997851,0.00003321773,0.00002435811,0.00004298119,0.00006724976,0.00004719111],"domain_scores_gemma":[0.9991336,0.0002120813,0.0002799046,0.00007202929,0.0002283724,0.000073991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002399519,0.00009165804,0.8817965,0.001029206,0.0001951398,0.0007783574,0.002845122,0.001613317,0.0007110618,0.004718224,0.03020553,0.07577591],"study_design_scores_gemma":[0.000008747425,0.0000365375,0.9167377,0.0002887083,0.00007568226,0.0002872811,0.008933825,0.001784667,0.0008271646,0.0003496403,0.07065123,0.00001875262],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9018216,0.002175955,0.0005648001,0.0002349514,0.0000241609,0.00004187245,0.07417594,0.00009065225,0.02087007],"genre_scores_gemma":[0.9111273,0.002437209,0.000853344,0.0000293369,0.00001973637,0.00005910713,0.07765102,0.00003922036,0.007783736],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.02670558,"threshold_uncertainty_score":0.05310029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03902878798581567,"score_gpt":0.2891341462940512,"score_spread":0.2501053583082355,"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."}}