{"id":"W2884352191","doi":"10.1016/j.isprsjprs.2018.07.005","title":"Spectral analysis of wetlands using multi-source optical satellite imagery","year":2018,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":134,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Environment and Climate Change Canada; Department of Environment and Conservation, Government of Newfoundland and Labrador; Government of Canada","keywords":"Wetland; Remote sensing; Spectral bands; Environmental science; Advanced Spaceborne Thermal Emission and Reflection Radiometer; Bog; Swamp; Satellite imagery; Geology; Geography; Peat; Ecology; Digital elevation model","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001002911,0.0001941239,0.00009278725,0.001519289,0.0001719256,0.0003269556,0.0001776892,0.0001411125,0.0008131582],"category_scores_gemma":[0.0002168182,0.0001002353,0.0002641046,0.0006515317,0.00009499141,0.0004043103,0.000156892,0.0001104914,0.0002246735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001630603,"about_ca_system_score_gemma":0.0002025686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007276695,"about_ca_topic_score_gemma":0.0138408,"domain_scores_codex":[0.9999475,0.000006049733,0.000003102551,0.0000116253,0.00002109554,0.00001055635],"domain_scores_gemma":[0.9998959,0.00002081047,0.00001400941,0.00001140889,0.000047285,0.00001045603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000584768,0.0003485198,0.1022528,0.0002811726,0.0001460377,0.0002708318,0.0003286022,0.05266557,0.3504839,0.001162805,0.002929847,0.4885452],"study_design_scores_gemma":[0.00003019688,0.00008025155,0.5628977,0.00002643522,0.0001157617,0.0002182641,0.000457564,0.3725471,0.05926675,0.0008489654,0.003467016,0.00004402184],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.963755,0.0001693269,0.03130578,0.00008012172,0.00001494605,0.00003005456,0.00107892,0.0004127069,0.003153273],"genre_scores_gemma":[0.9755372,0.0001188617,0.02269277,0.00001207145,0.000009727605,0.00001117834,0.0007581802,0.00003525527,0.0008247587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007276695,"threshold_uncertainty_score":0.01446867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508989182660366,"score_gpt":0.2605264642940482,"score_spread":0.2454365724674445,"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."}}