{"id":"W7095405113","doi":"","title":"2002b. A Landsat7 ETM+ orthoimage coverage of Canada","year":2016,"lang":"en","type":"article","venue":"","topic":"Satellite Image Processing and Photogrammetry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Orthophoto; Geospatial analysis; General partnership; Government (linguistics); Satellite imagery; Land cover; Grey literature; Aerial photography; Cloud computing","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":[0.0003480089,0.0005771475,0.0002430808,0.002150976,0.00206462,0.001587199,0.000960422,0.0002859216,0.01404372],"category_scores_gemma":[0.0008642211,0.0003168306,0.0003298237,0.005009478,0.0002826618,0.0006323188,0.0003849899,0.0004790038,0.004187317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02741914,"about_ca_system_score_gemma":0.03800316,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9961463,"about_ca_topic_score_gemma":0.997422,"domain_scores_codex":[0.9994479,0.00001630838,0.00001547392,0.00004955463,0.0003785581,0.00009228441],"domain_scores_gemma":[0.9982153,0.00001941219,0.00003979701,0.00003543057,0.001595563,0.00009462256],"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":[0.0003088863,0.00009041672,0.04417147,0.0005685122,0.00008320662,0.0003288768,0.0005698673,0.004509219,0.00661303,0.005544279,0.7472999,0.1899123],"study_design_scores_gemma":[0.00004722055,0.00002165517,0.3834041,0.0001528056,0.00005581378,0.0001440058,0.0008719331,0.007156851,0.004272847,0.0005840151,0.6032072,0.00008151148],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06514703,0.002353331,0.008014477,0.003089727,0.0002575613,0.0006491017,0.7795424,0.002105004,0.1388414],"genre_scores_gemma":[0.2499685,0.002585937,0.02810321,0.001028694,0.00005769388,0.0003043362,0.5950554,0.000852579,0.1220437],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02741914,"threshold_uncertainty_score":0.1989408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003971510074707627,"score_gpt":0.1660928910301157,"score_spread":0.1621213809554081,"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."}}