{"id":"W3093978280","doi":"10.1080/15481603.2020.1829377","title":"Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing","year":2020,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Academy of Finland; Helsingin Yliopisto","keywords":"Hyperspectral imaging; Remote sensing; Leaf area index; Environmental science; Lidar; Biomass (ecology); Vegetation (pathology); Spatial ecology; Geography; Ecology","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":[0.0003672692,0.0003467341,0.0001758455,0.0007461118,0.0001090301,0.000261201,0.0002914034,0.0001793374,0.0004094907],"category_scores_gemma":[0.0006339593,0.0001647108,0.0003753733,0.000480262,0.00009004078,0.0004648465,0.0001995453,0.0001204295,0.0001609763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003472,"about_ca_system_score_gemma":0.0002100869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01816931,"about_ca_topic_score_gemma":0.0288328,"domain_scores_codex":[0.9998882,0.00002657527,0.000006101997,0.00004280125,0.00002245109,0.00001392716],"domain_scores_gemma":[0.9998187,0.00006243924,0.00004338226,0.00002660741,0.00003668598,0.00001212192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003351056,0.0002533824,0.6100388,0.0001106175,0.0002203161,0.0002560542,0.0001482843,0.1656549,0.04556464,0.0004506028,0.0004397967,0.1765274],"study_design_scores_gemma":[0.00001506951,0.00005958116,0.3792966,0.00001095911,0.00004191203,0.0001150234,0.00007303101,0.6132224,0.006375338,0.0003916959,0.0003727589,0.00002577136],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978851,0.00009773265,0.0200128,0.00001450984,0.000003516687,0.00001038318,0.0002594314,0.0001512272,0.0005994804],"genre_scores_gemma":[0.983001,0.00003073208,0.01641759,0.000003961202,0.000001986472,0.000009385831,0.0002990129,0.000005763933,0.0002305638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01816931,"threshold_uncertainty_score":0.03612709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213373386621725,"score_gpt":0.209354130118789,"score_spread":0.1972203962525718,"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."}}