{"id":"W2100125369","doi":"10.1080/01431160500181812","title":"Satellite‐derived ecosystems classification: image segmentation by ecological region for improved classification accuracy, a boreal case study","year":2006,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Parks Canada","keywords":"Thematic Mapper; Segmentation; Context (archaeology); Remote sensing; Computer science; Image segmentation; Pattern recognition (psychology); Boreal; Contextual image classification; Statistic; Artificial intelligence; Satellite imagery; Ecology; Geography; Mathematics; Image (mathematics); Statistics; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006011029,0.0002377526,0.0002766454,0.0001057485,0.0001891678,0.0002606111,0.0002859662,0.0001442653,0.00001406268],"category_scores_gemma":[0.0002278964,0.0001914633,0.0001773171,0.0001852372,0.0001038127,0.0005383833,0.00006281649,0.0002549964,0.00001384297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009559574,"about_ca_system_score_gemma":0.00003304294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001130755,"about_ca_topic_score_gemma":0.0009260731,"domain_scores_codex":[0.9975963,0.0002175469,0.000981092,0.0003794079,0.0005787396,0.0002469461],"domain_scores_gemma":[0.9976014,0.0002830848,0.001368851,0.0002026464,0.0004442564,0.00009978669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002005234,0.0003113658,0.0008879597,0.000006145764,0.00009067867,0.000948266,0.0005786762,0.0001477055,0.6844373,0.00000629746,0.008122239,0.3042628],"study_design_scores_gemma":[0.01043251,0.001812719,0.1168397,0.0002738678,0.0004827773,0.08096025,0.01877888,0.6706718,0.06139663,0.002455706,0.03426323,0.001631936],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9029682,0.00002978749,0.09226325,0.00214275,0.0009292197,0.0007420171,0.00001021467,0.00003959778,0.0008749618],"genre_scores_gemma":[0.9404677,0.00003319746,0.05852891,0.0001146076,0.000622818,2.921475e-7,0.00008177821,0.00002428691,0.0001264561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6705241,"threshold_uncertainty_score":0.7807645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02297034704511825,"score_gpt":0.285286007216536,"score_spread":0.2623156601714178,"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."}}