{"id":"W7001106730","doi":"","title":"On the Impact of Land Use & Landcover Change on GHG Emissions using Advanced Remote Sensing Technology","year":2024,"lang":"en","type":"other","venue":"York University Digital Library (York University)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Greenhouse gas; Land use; Land cover; Climate change; Land use, land-use change and forestry; Land information system; Global warming; Work (physics); Agriculture","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":[0.0006568622,0.0003150459,0.0001494355,0.0007277105,0.0003082525,0.0009104377,0.0002880245,0.0002723628,0.00117671],"category_scores_gemma":[0.001423295,0.0001192528,0.0003332829,0.001326106,0.0004249436,0.0007077172,0.000291111,0.0002209794,0.0001703527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002079507,"about_ca_system_score_gemma":0.0007790227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3495551,"about_ca_topic_score_gemma":0.5002814,"domain_scores_codex":[0.9996399,0.00006637934,0.00001081719,0.00005264165,0.0001987698,0.0000315284],"domain_scores_gemma":[0.9994899,0.0002643407,0.00005954688,0.00003133555,0.0001357927,0.00001894474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000316614,0.0002315209,0.5901018,0.0004675844,0.0003383716,0.0006330677,0.0007906267,0.09574667,0.03456793,0.003113816,0.002193704,0.2714983],"study_design_scores_gemma":[0.000008872042,0.0001191126,0.8845292,0.00006817937,0.0001229539,0.0001342652,0.0008772272,0.09877212,0.007606919,0.00154526,0.006175094,0.0000408277],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658307,0.002047903,0.009336142,0.0009410363,0.00004001231,0.00008222578,0.001335305,0.0001021581,0.02028428],"genre_scores_gemma":[0.9906378,0.001393693,0.00626354,0.00006622755,0.000015871,0.000008606507,0.0003912144,0.00001174566,0.00121126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3495551,"threshold_uncertainty_score":0.6950409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03122245832036447,"score_gpt":0.2154200038718062,"score_spread":0.1841975455514417,"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."}}