{"id":"W2091210161","doi":"10.5589/m12-047","title":"Pixel-based image classification to map vegetation communities using SPOT5 and Landsat5 Thematic Mapper data in a tropical savanna, northern Australia","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Queensland","keywords":"Thematic Mapper; Geography; Thematic map; Vegetation (pathology); Cartography; Remote sensing; Multispectral image; Scale (ratio); Multispectral pattern recognition; Pixel; Field (mathematics); Computer science; Satellite imagery; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004482277,0.0001393362,0.0001976585,0.0001550211,0.0001403828,0.0000952468,0.0002024918,0.00008252898,0.00002002974],"category_scores_gemma":[0.0001125342,0.000117059,0.00002971344,0.0002038125,0.0001493967,0.0004039119,0.00003749372,0.0002919421,0.00003294477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004718031,"about_ca_system_score_gemma":0.00007754851,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01612006,"about_ca_topic_score_gemma":0.257466,"domain_scores_codex":[0.9988182,0.000169848,0.0003312071,0.00011849,0.0002112001,0.0003510975],"domain_scores_gemma":[0.9989432,0.00005953647,0.0001825483,0.0003352131,0.00003862057,0.0004408876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001037352,0.00007162519,0.590665,0.0002592849,0.00007964155,0.000352773,0.04194878,0.005514078,0.1361718,0.0000166609,0.00175472,0.2230619],"study_design_scores_gemma":[0.0005139991,0.00004891536,0.878177,0.0007834846,0.00006472415,0.0007449573,0.004474475,0.1103523,0.0004757757,0.000110532,0.003901786,0.0003520514],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987,0.00006275083,0.01145217,0.001014864,0.0001980327,0.0001254232,0.000003231771,0.00000437208,0.0001391557],"genre_scores_gemma":[0.8386581,0.0000012993,0.1610385,0.0001615999,0.0001001092,4.418913e-9,0.000008407869,0.00001312736,0.00001885803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2875119,"threshold_uncertainty_score":0.9904317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06964353237227328,"score_gpt":0.2789012918919794,"score_spread":0.2092577595197062,"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."}}