{"id":"W7067183294","doi":"","title":"Merging data streams and remote sensing change detection routines for stand-replacing disturbances in Canada","year":2022,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Fungal Plant Pathogen Control","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Change detection; STREAMS; Field (mathematics); Data processing; Climate change","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":[0.000786128,0.000406703,0.0004734739,0.001492348,0.001832693,0.002200979,0.001107204,0.000421908,0.002806705],"category_scores_gemma":[0.002439154,0.0003643675,0.0004585872,0.004027035,0.000457585,0.0008291798,0.0005784621,0.0005396802,0.0007412505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01296747,"about_ca_system_score_gemma":0.02217748,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9802449,"about_ca_topic_score_gemma":0.9879624,"domain_scores_codex":[0.9993235,0.00002700519,0.0000326027,0.0001465684,0.0003155128,0.0001547332],"domain_scores_gemma":[0.9983754,0.0001984643,0.00006683854,0.00006372434,0.001127577,0.0001678948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002076105,0.0005199751,0.2896631,0.0003782337,0.0002402076,0.000689993,0.003901717,0.08035161,0.02691178,0.001474702,0.04118267,0.5526099],"study_design_scores_gemma":[0.0002236018,0.0001306794,0.6066908,0.00009603969,0.0001991591,0.0000844889,0.006640221,0.3190933,0.02265736,0.0007322795,0.04329395,0.0001580869],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9517881,0.0004448495,0.01651331,0.0007125194,0.0000989348,0.0004070383,0.01212557,0.004980496,0.01292923],"genre_scores_gemma":[0.9060691,0.0003867729,0.06543692,0.0001048781,0.00002302064,0.0001122283,0.01505743,0.0005321704,0.01227748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01975507,"threshold_uncertainty_score":0.09408605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06140956672919224,"score_gpt":0.2771923670421866,"score_spread":0.2157828003129943,"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."}}