{"id":"W6907460012","doi":"10.23687/add1346b-f632-4eb9-a83d-a662b38655ad","title":"Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30-m resolution product of fire and harvest detection and attribution since 1984","year":2017,"lang":"en","type":"dataset","venue":"GEOSCAN","topic":"","field":"","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Disturbance (geology); Attribution; Pixel; Change detection; Product (mathematics); Land cover; Data set","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004545097,0.0008403591,0.000483353,0.004471255,0.001548535,0.001751782,0.0008978046,0.0002430747,0.01332147],"category_scores_gemma":[0.001991789,0.0003484061,0.0005053862,0.008221547,0.0003377532,0.0006685918,0.0008203174,0.0006233471,0.003618174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0152647,"about_ca_system_score_gemma":0.02481854,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9850492,"about_ca_topic_score_gemma":0.9926895,"domain_scores_codex":[0.9990735,0.00002832549,0.00003154294,0.0001025973,0.0006499572,0.0001140675],"domain_scores_gemma":[0.9963415,0.00007563749,0.0002254372,0.0001572244,0.00295354,0.0002465731],"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":[0.0003998355,0.00009575054,0.1170804,0.0006402932,0.000230513,0.0001657883,0.0005541016,0.002463918,0.002103328,0.002320861,0.7760494,0.09789586],"study_design_scores_gemma":[0.00009480083,0.00002410396,0.457887,0.0003020627,0.00009850839,0.0001236141,0.0007163832,0.004819361,0.002420237,0.0005570495,0.5328344,0.0001224892],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02560838,0.0008397944,0.003369607,0.0002486935,0.000120687,0.0003109543,0.9371673,0.00190844,0.0304262],"genre_scores_gemma":[0.1079746,0.001030892,0.01786051,0.0002135388,0.00003558141,0.0003445741,0.8489436,0.0006989278,0.02289768],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0152647,"threshold_uncertainty_score":0.1107537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009331917926962034,"score_gpt":0.2161657246700388,"score_spread":0.2068338067430768,"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."}}