{"id":"W3131704567","doi":"10.21203/rs.3.rs-98574/v1","title":"Quantifying productivity at landscape scale using remotely-sensed foliar traits and canopy structure","year":2020,"lang":"en","type":"preprint","venue":"Research Square (Research Square)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Division of Environmental Biology; Pontificia Universidad Católica del Perú; Carnegie Institution for Science; European Commission; Carnegie Institution of Washington; Natural Environment Research Council; Gordon and Betty Moore Foundation; National Science Foundation","keywords":"Canopy; Scale (ratio); Productivity; Environmental science; Remote sensing; Geography; Agroforestry; Environmental resource management; Ecology; Biology; Cartography; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["sts","research_integrity"],"category_scores_codex":[0.006658914,0.00114392,0.001339101,0.0007987805,0.002691559,0.001118127,0.001979419,0.001511455,0.001145767],"category_scores_gemma":[0.003596053,0.0009734608,0.0004514768,0.003410199,0.002816616,0.0005020571,0.01199316,0.009804321,0.000420661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003233969,"about_ca_system_score_gemma":0.0007441225,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00705102,"about_ca_topic_score_gemma":0.01042471,"domain_scores_codex":[0.976455,0.005941758,0.0009313854,0.00433939,0.008601136,0.00373128],"domain_scores_gemma":[0.9938084,0.001473008,0.0003097485,0.001803633,0.0008531993,0.001751937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001112835,0.0004274796,0.05618494,0.005156462,0.0003323835,0.001668865,0.01224177,0.01672666,0.8329871,0.0001533603,0.04457688,0.02843129],"study_design_scores_gemma":[0.003828806,0.001912344,0.6781102,0.005691379,0.0002371523,0.001320158,0.005472234,0.1491768,0.0818157,0.0260839,0.04020264,0.006148736],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839262,0.001335954,0.0001407068,0.005526619,0.0003611314,0.004805565,0.0004292586,0.0002916131,0.003182957],"genre_scores_gemma":[0.9906471,0.0005537679,0.005776557,0.00004575092,0.001351814,0.00001896266,0.0001840487,0.000245324,0.001176674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7511714,"threshold_uncertainty_score":0.9999188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1376758665128387,"score_gpt":0.3901670801908602,"score_spread":0.2524912136780215,"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."}}