{"id":"W2751979613","doi":"10.1080/07038992.2017.1370367","title":"Mapping Arctic Coastal Ecosystems with High Resolution Optical Satellite Imagery Using a Hybrid Classification Approach","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Tundra; Remote sensing; Land cover; Arctic; Satellite imagery; Pixel; Wetland; Random forest; Terrain; Geography; Cartography; Environmental science; Computer science; Land use; Geology; Artificial intelligence; Ecology; Oceanography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005382613,0.0001387311,0.0002430608,0.0002473032,0.0007658689,0.0004926901,0.0001690649,0.0000589893,0.0000517977],"category_scores_gemma":[0.00009396796,0.0001167825,0.000065041,0.00008449675,0.0001780783,0.0004337009,0.000004898926,0.0002653761,0.00001175361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006077959,"about_ca_system_score_gemma":0.0004650081,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1351167,"about_ca_topic_score_gemma":0.418463,"domain_scores_codex":[0.9988194,0.00006953916,0.0003187686,0.0001732017,0.0002163632,0.0004027352],"domain_scores_gemma":[0.9985182,0.00004684754,0.0004531268,0.0002606518,0.0002066336,0.0005145412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002963833,0.00001162596,0.2686936,0.0004032964,0.0001947535,0.005078523,0.00277198,0.002224633,0.008486293,0.00002961025,0.0003509108,0.7114584],"study_design_scores_gemma":[0.0006175218,0.0001237394,0.3546866,0.001006626,0.0000747102,0.01256968,0.002065255,0.6236177,0.0002109955,0.0001504665,0.004470472,0.0004061767],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9832917,0.0003787091,0.01286515,0.0004242276,0.0005911335,0.0001002591,0.0001470583,0.000005180122,0.002196535],"genre_scores_gemma":[0.9724527,0.00004557312,0.02669093,0.00006042512,0.0005742659,2.918276e-9,0.0001474403,0.000008617715,0.00002005631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7110522,"threshold_uncertainty_score":0.8706426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06699418352352655,"score_gpt":0.2326477009496956,"score_spread":0.1656535174261691,"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."}}