{"id":"W4394504940","doi":"10.6084/m9.figshare.5258545","title":"Tree Canopy Change, 2009-2014, Prince George's County, MD","year":2017,"lang":"en","type":"dataset","venue":"Figshare","topic":"Forest ecology and management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"George (robot); Canopy; Tree (set theory); Forestry; Geography; Tree canopy; Archaeology; History; Mathematics; Art history; Combinatorics","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":[0.0005007913,0.0008192784,0.0005533775,0.00210878,0.0005030243,0.001211878,0.001541282,0.0007316637,0.02209255],"category_scores_gemma":[0.002453452,0.0004151432,0.0006500732,0.003853388,0.0002061442,0.0009128537,0.001030967,0.0008042027,0.01765827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00113918,"about_ca_system_score_gemma":0.001746035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08692292,"about_ca_topic_score_gemma":0.1638346,"domain_scores_codex":[0.9997156,0.00003278752,0.00003600922,0.00008222682,0.00007302978,0.00006031376],"domain_scores_gemma":[0.9991069,0.0001323046,0.0001252119,0.0001578184,0.0003831959,0.0000946381],"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":[0.00005988981,0.00002359758,0.005318485,0.0004393044,0.00004184405,0.00003295606,0.00006122755,0.000321713,0.00008369126,0.0002982552,0.9902132,0.003105681],"study_design_scores_gemma":[0.00031927,0.00002731065,0.08477653,0.0005029705,0.0000544669,0.00008043124,0.0004808443,0.001173852,0.0005661118,0.0007815222,0.9111848,0.00005188131],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006569764,0.00002842164,0.00002949611,0.00005153636,0.00001230824,0.000009803372,0.9985776,0.0001509755,0.0004828759],"genre_scores_gemma":[0.001577674,0.00003973277,0.0002560134,0.00003199582,0.000007029213,0.00008327079,0.997318,0.00003692012,0.0006494878],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08692292,"threshold_uncertainty_score":0.1728339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03001803648274323,"score_gpt":0.2641786190797607,"score_spread":0.2341605825970175,"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."}}