{"id":"W2088191870","doi":"10.3390/f5051053","title":"Using VEGNET In-Situ Monitoring LiDAR (IML) to Capture Dynamics of Plant Area Index, Structure and Phenology in Aspen Parkland Forests in Alberta, Canada","year":2014,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Phenology; Environmental science; Canopy; Leaf area index; Lidar; Remote sensing; Forestry; Physical geography; Geography; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007772083,0.0001163738,0.0001852932,0.00008103896,0.00002886883,0.00001062704,0.0001259655,0.000102141,0.000006831292],"category_scores_gemma":[0.00004446361,0.000111093,0.000008190817,0.0002700186,0.00005040801,0.0000471637,0.00008756603,0.0001679282,9.171445e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003076912,"about_ca_system_score_gemma":0.00004077817,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8297615,"about_ca_topic_score_gemma":0.999293,"domain_scores_codex":[0.9991331,0.00003426775,0.0002010685,0.0002419157,0.0001376471,0.0002519837],"domain_scores_gemma":[0.9996029,0.00007115751,0.0000576408,0.0001883885,0.000004384622,0.00007550637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001477538,0.000009234546,0.9682997,0.000006891546,0.000001699112,0.000007778618,0.0005636012,0.02871669,0.0008314525,0.00005231477,0.00002037255,0.001475463],"study_design_scores_gemma":[0.0002424856,0.00001428697,0.9682872,0.00005371067,0.000002433168,0.00001362502,0.00007725117,0.02967479,0.0003393874,0.001027,0.0001555223,0.0001123317],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99885,0.0000123919,0.0001344667,0.0001301542,0.00006889011,0.0001858138,0.00001424348,0.00000305236,0.0006009528],"genre_scores_gemma":[0.999445,0.000001690948,0.0004609103,0.00002413259,0.00001774501,0.000001544725,0.00001901095,0.00001048439,0.00001950937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1695315,"threshold_uncertainty_score":0.453024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007824414644874576,"score_gpt":0.2148202372940365,"score_spread":0.206995822649162,"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."}}