{"id":"W6958250661","doi":"10.6084/m9.figshare.16779322.v1","title":"Additional file 1 of A 40-year evaluation of drivers of African rainforest change","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Rainforest; Climate change; Data collection; Government (linguistics); Vegetation (pathology)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001607971,0.0005416075,0.0005873263,0.002484822,0.0008872671,0.0008904647,0.001226569,0.0005711967,0.7464886],"category_scores_gemma":[0.02101256,0.0004565341,0.0007244108,0.005538846,0.0002118736,0.001880887,0.001053514,0.0006032432,0.07063384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005263,"about_ca_system_score_gemma":0.002152815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04151771,"about_ca_topic_score_gemma":0.06321896,"domain_scores_codex":[0.9993637,0.0001654663,0.00009475834,0.00010643,0.0001543892,0.0001152675],"domain_scores_gemma":[0.9820624,0.0128628,0.001326893,0.0007794744,0.002442805,0.0005256172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002396414,0.0001081478,0.01195946,0.001249296,0.00007277739,0.00004962902,0.0001778852,0.001033855,0.00004637103,0.0007378067,0.9764696,0.007855627],"study_design_scores_gemma":[0.005488729,0.0004363583,0.2552038,0.002039076,0.0004356804,0.0004150174,0.002930524,0.004505129,0.000798659,0.005726588,0.7218323,0.0001882072],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0006612338,0.000007663506,0.00009295515,0.00004955041,0.000007150814,0.00007542584,0.9980064,0.00005442219,0.001045295],"genre_scores_gemma":[0.04771257,0.0001244803,0.003420848,0.0002323075,0.00005498959,0.003830461,0.9281501,0.0004657922,0.01600838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7464886,"threshold_uncertainty_score":0.3616033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0685887272864559,"score_gpt":0.2223762165275927,"score_spread":0.1537874892411368,"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."}}