{"id":"W3201026981","doi":"10.14710/tpwk.2021.31879","title":"Estimasi Cadangan Karbon Akibat Perubahan Tutupan Lahan di Kabupaten Kendal","year":2021,"lang":"en","type":"article","venue":"Teknik PWK (Perencanaan Wilayah Kota)","topic":"Geological and Geophysical Studies","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Environmental science; Greenhouse gas; Land cover; Stock (firearms); Carbon stock; Carbon sequestration; Land use; Climate change; Forestry; Carbon dioxide; Geography; Ecology","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":[0.0003793046,0.0005776698,0.0003782416,0.00100887,0.0004918705,0.001348306,0.0004039539,0.0002312918,0.00783725],"category_scores_gemma":[0.0008163032,0.0001950179,0.0003915301,0.00113747,0.0002514885,0.0007421248,0.0006284506,0.0004352802,0.001735021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009629467,"about_ca_system_score_gemma":0.001646427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02862555,"about_ca_topic_score_gemma":0.04202669,"domain_scores_codex":[0.9998166,0.00003894653,0.00001026756,0.00005046314,0.00004658535,0.00003718893],"domain_scores_gemma":[0.9996951,0.00007973988,0.00003957618,0.000018279,0.0001423989,0.00002498077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003619553,0.0002581445,0.5319415,0.0009441106,0.0005275925,0.003041816,0.002543826,0.01232707,0.007695696,0.007981756,0.03190812,0.4004684],"study_design_scores_gemma":[0.00003339798,0.0001769998,0.7887865,0.0003128103,0.0003128082,0.001367831,0.007303424,0.04493401,0.006583898,0.003332458,0.1467594,0.00009644341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9237465,0.004873477,0.01106782,0.001372951,0.000138629,0.0001176535,0.009044323,0.0005580267,0.0490807],"genre_scores_gemma":[0.948736,0.002985521,0.01149637,0.00009857374,0.00006113652,0.0001358123,0.008425957,0.00008728303,0.02797322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02862555,"threshold_uncertainty_score":0.05691785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01827990655993248,"score_gpt":0.2056257190185281,"score_spread":0.1873458124585956,"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."}}