{"id":"W2029655596","doi":"10.3390/rs4071887","title":"Monitoring Seasonal Hydrological Dynamics of Minerotrophic Peatlands Using Multi-Date GeoEye-1 Very High Resolution Imagery and Object-Based Classification","year":2012,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Peat; Environmental science; Watershed; Hydrology (agriculture); Remote sensing; Hydrometeorology; Wetland; Physical geography; Geology; Geography; Precipitation; Ecology; Computer science; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002630609,0.0001752182,0.0001862586,0.0008753247,0.000205944,0.0002495157,0.0002747136,0.0002313244,0.0002615407],"category_scores_gemma":[0.0001979521,0.0001094175,0.0001245235,0.0004453922,0.0001541893,0.0002085601,0.0001563721,0.0001017357,0.00007594827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004588836,"about_ca_system_score_gemma":0.0003601647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05695235,"about_ca_topic_score_gemma":0.2267746,"domain_scores_codex":[0.9999065,0.00001119998,0.000004179867,0.00003266739,0.00002577369,0.00001963507],"domain_scores_gemma":[0.9998518,0.0000237814,0.00004020602,0.00001054425,0.00004054633,0.00003314222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003785967,0.0004422283,0.8199896,0.00007872581,0.00006959219,0.0004097362,0.0005864452,0.00590398,0.1157048,0.00005899407,0.0004177543,0.05595963],"study_design_scores_gemma":[0.000006958844,0.00005764473,0.9860494,0.000004419819,0.000009526827,0.00005913122,0.0001660022,0.01117329,0.002262768,0.000008055788,0.0001946799,0.00000823232],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992494,0.00001794508,0.0003571058,0.000004116272,8.024958e-7,0.000009457088,0.0001885492,0.00001551664,0.0001570089],"genre_scores_gemma":[0.9953958,0.00003330154,0.003723443,0.000004813613,0.000002023786,0.00001637257,0.0006083547,0.000002576888,0.0002132003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05695235,"threshold_uncertainty_score":0.1132417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02903572399260855,"score_gpt":0.2546535921065055,"score_spread":0.2256178681138969,"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."}}