{"id":"W4244535566","doi":"10.22215/etd/2003-05673","title":"Monitoring Northern Atlantic Canadian wetlands using remote sensing techniques","year":2003,"lang":"en","type":"dissertation","venue":"","topic":"Ecology and biodiversity studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Wetland; Remote sensing; Geography; Environmental resource management; Environmental science; Ecology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000500851,0.0002846067,0.0001924353,0.0009392299,0.001347902,0.0008161208,0.0005196515,0.0002512704,0.0008505381],"category_scores_gemma":[0.001004894,0.0001733741,0.000176639,0.001745264,0.0002832359,0.0004431234,0.000257883,0.0002779133,0.0002338204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007841912,"about_ca_system_score_gemma":0.0124427,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9813026,"about_ca_topic_score_gemma":0.9952713,"domain_scores_codex":[0.9996713,0.00001339572,0.000008658692,0.0000547947,0.0001658111,0.00008598019],"domain_scores_gemma":[0.9992804,0.00005107145,0.00006667302,0.0000267072,0.0005330584,0.00004202818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001640313,0.0002318791,0.4715001,0.0002398913,0.0001591438,0.0002441542,0.00274394,0.01067011,0.04560312,0.000684404,0.01611205,0.4516472],"study_design_scores_gemma":[0.00002231369,0.00004261435,0.9700893,0.00004675439,0.00009754131,0.00005750493,0.001933206,0.005841187,0.005986726,0.0001484625,0.01570786,0.00002643223],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9643978,0.0009855711,0.003547139,0.0006289166,0.00004146462,0.0001888701,0.002563953,0.0001645849,0.02748165],"genre_scores_gemma":[0.9599295,0.00212913,0.01362183,0.0001589582,0.0000320259,0.00009963026,0.002067645,0.00003563995,0.02192559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01869744,"threshold_uncertainty_score":0.05689734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01552418607507511,"score_gpt":0.2324594392406568,"score_spread":0.2169352531655817,"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."}}