{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000367559,0.0001562203,0.0002176523,0.00005981831,0.0001656367,0.00001872723,0.00006060314,0.000152016,0.00001298815],"category_scores_gemma":[0.0000612189,0.0001413169,0.00005069399,0.0001488668,0.0001934424,0.0001703872,0.00009460122,0.0001576805,0.00000540779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001993249,"about_ca_system_score_gemma":0.00001282501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004518456,"about_ca_topic_score_gemma":0.00004138708,"domain_scores_codex":[0.9987705,0.0001260171,0.0002401852,0.0002672087,0.0001927517,0.0004033099],"domain_scores_gemma":[0.9994393,0.0000879525,0.000155311,0.000182131,0.00001541885,0.0001198658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001555027,0.0000949476,0.6348673,0.00003377528,0.00002184292,0.0000226608,0.0001199547,0.007253303,0.3172182,0.00001184367,0.00001628316,0.04018435],"study_design_scores_gemma":[0.0003183997,0.00002939795,0.3829815,0.00002527414,0.0000243753,0.00003521072,0.00001698216,0.6155094,0.0009173722,0.0000166863,0.00002193111,0.000103471],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9748463,0.00008350433,0.02443889,0.0001121067,0.0001919523,0.00009781156,0.000004951521,0.00003224178,0.0001922276],"genre_scores_gemma":[0.964118,0.00003224671,0.03558581,0.00002566733,0.0001524742,4.651664e-8,0.00004646572,0.00001446697,0.00002480795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6082561,"threshold_uncertainty_score":0.5762736,"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."}}