{"id":"W4402227887","doi":"10.1016/j.rse.2024.114377","title":"New insights into distinguishing temperate deciduous swamps from upland forests and shrublands with SAR","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Environment and Climate Change Canada","funders":"","keywords":"Shrubland; Swamp; Remote sensing; Deciduous; Temperate rainforest; Temperate deciduous forest; Temperate forest; Environmental science; Temperate climate; Geography; Agroforestry; Ecology; Ecosystem","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006001504,0.0003845237,0.0001955577,0.0007659745,0.0001375648,0.0006681427,0.0001775134,0.0002141461,0.0009634747],"category_scores_gemma":[0.000723072,0.0001796924,0.0002436743,0.0006904747,0.0003509011,0.0008644909,0.0002348063,0.0002703088,0.0001475897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001411466,"about_ca_system_score_gemma":0.0001924742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00213269,"about_ca_topic_score_gemma":0.007101037,"domain_scores_codex":[0.9998869,0.00002379643,0.00001077257,0.0000364279,0.00001921185,0.00002299125],"domain_scores_gemma":[0.9995955,0.0001635631,0.0001160365,0.00002947404,0.00006298851,0.00003245693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002080358,0.0001651582,0.6857657,0.0002794479,0.0001369775,0.001176354,0.001570875,0.002983472,0.1561881,0.002556629,0.0008891201,0.1480801],"study_design_scores_gemma":[0.00000447243,0.00007523081,0.9818736,0.00004611376,0.00004498574,0.0005537111,0.001264273,0.008257085,0.004268934,0.001475182,0.002121316,0.00001509311],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833211,0.001740302,0.008986258,0.0004558279,0.00002138325,0.00002291838,0.000286051,0.0000569897,0.005109243],"genre_scores_gemma":[0.9872928,0.001693992,0.00903672,0.0002037466,0.00005501093,0.000009870482,0.0003488991,0.00001438242,0.001344625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00213269,"threshold_uncertainty_score":0.004240572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005266935954264891,"score_gpt":0.1948532422724356,"score_spread":0.1895863063181707,"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."}}