{"id":"W6930628405","doi":"10.5281/zenodo.13208080","title":"Cropland dataset for Canada for AD1000-2015","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Action Observation and Synchronization","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Technology of the People's Republic of China","keywords":"Census; Land use; Population; Per capita; 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":[0.0005266076,0.002167575,0.001264473,0.004614237,0.001642763,0.002374073,0.002920364,0.001289109,0.04341074],"category_scores_gemma":[0.004088701,0.0005927241,0.00109112,0.01117426,0.0005717872,0.0008826307,0.001347176,0.001674862,0.05085666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008371954,"about_ca_system_score_gemma":0.01535861,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.867024,"about_ca_topic_score_gemma":0.9308433,"domain_scores_codex":[0.9992529,0.00005725664,0.00005794034,0.000191977,0.0002619719,0.0001780521],"domain_scores_gemma":[0.9977427,0.0002171697,0.0001534826,0.0002692895,0.001371141,0.0002463495],"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":[0.00002615706,0.00001070079,0.0008224249,0.0002083132,0.00002002792,0.00001932867,0.00002259872,0.0002360281,0.00003707152,0.0005289841,0.9964502,0.001618116],"study_design_scores_gemma":[0.0001159418,0.000005964712,0.008409231,0.0002238504,0.0000298224,0.00004710341,0.00009166745,0.0008723107,0.0002318106,0.0009704833,0.9889616,0.00004013532],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009369737,0.00005430483,0.00004427124,0.00003457024,0.000009034145,0.000006273269,0.9990839,0.0001432268,0.0005307204],"genre_scores_gemma":[0.0003314845,0.00004842892,0.0002167701,0.00002045119,0.000003080627,0.00002756953,0.9987105,0.00003339083,0.0006082347],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.132976,"threshold_uncertainty_score":0.2675182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07140248335580654,"score_gpt":0.3216336023176569,"score_spread":0.2502311189618504,"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."}}