{"id":"W3211329810","doi":"10.3389/fenvs.2021.757871","title":"Mapping the Extent of Invasive Phragmites australis subsp. australis From Airborne Hyperspectral Imagery","year":2021,"lang":"en","type":"article","venue":"Frontiers in Environmental Science","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Phragmites; Hyperspectral imaging; Vegetation (pathology); Remote sensing; Ground truth; Environmental science; Thematic Mapper; Invasive species; Biodiversity; National park; Geography; Cartography; Satellite imagery; Ecology; Wetland; Biology; Computer science; Artificial intelligence","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":[0.0001965852,0.0002858352,0.00009558065,0.0006761811,0.0001513876,0.0003174195,0.0001462933,0.0001302563,0.0003685947],"category_scores_gemma":[0.0003435176,0.0001391363,0.0001400625,0.000328952,0.0001384241,0.0002181952,0.0002133828,0.0001753455,0.0001046601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003229105,"about_ca_system_score_gemma":0.0004783105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.116784,"about_ca_topic_score_gemma":0.2871557,"domain_scores_codex":[0.9999139,0.000008342072,0.000004140477,0.00003180307,0.00002754204,0.00001420616],"domain_scores_gemma":[0.9998096,0.00004242286,0.00003770495,0.00001295002,0.0000782395,0.00001924247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002109011,0.0001810896,0.5191675,0.0002252351,0.0001146049,0.0005608549,0.0011992,0.03941571,0.2477495,0.0003306684,0.001867982,0.1889767],"study_design_scores_gemma":[0.00000738555,0.00003746838,0.8990213,0.00002307572,0.000030056,0.0001330827,0.0004139544,0.09012136,0.008806432,0.00009624103,0.001293456,0.00001603445],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932673,0.0000726838,0.004418248,0.00002371377,0.000003094686,0.00002682795,0.0006248961,0.0001428983,0.001420243],"genre_scores_gemma":[0.9893301,0.00007675207,0.009217666,0.000008851156,0.000002443541,0.00001125577,0.0007322058,0.00001015324,0.0006106154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.116784,"threshold_uncertainty_score":0.2322085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01041316918151627,"score_gpt":0.1935985089283896,"score_spread":0.1831853397468733,"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."}}