{"id":"W3089462285","doi":"10.3390/rs12193165","title":"Toronto Water Vapor Lidar Inter-Comparison Campaign","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Environment and Climate Change Canada","funders":"Environment and Climate Change Canada; York University","keywords":"Lidar; Water vapor; Dial; Environmental science; Remote sensing; Planetary boundary layer; Meteorology; Backscatter (email); Atmospheric sciences; Geology; Turbulence; Physics; Telecommunications","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":[0.0005903907,0.0004592341,0.0003819094,0.0008608658,0.00142568,0.0005219019,0.0008133774,0.0004156798,0.003115382],"category_scores_gemma":[0.0005689388,0.0002114325,0.000270423,0.001050937,0.0003092301,0.000546836,0.0006791963,0.0006145955,0.0009200895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003621696,"about_ca_system_score_gemma":0.002538626,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3534547,"about_ca_topic_score_gemma":0.5599578,"domain_scores_codex":[0.998948,0.0000786713,0.00002236223,0.000256936,0.0005513293,0.0001426482],"domain_scores_gemma":[0.9992686,0.00004761016,0.0000539851,0.00007191217,0.0005033968,0.00005453692],"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":[0.003393477,0.001254178,0.2999285,0.0006076358,0.0005162437,0.001592913,0.004213464,0.0167287,0.4342288,0.003049792,0.05545513,0.1790312],"study_design_scores_gemma":[0.0001829626,0.0008182113,0.7948985,0.00004351967,0.0001311596,0.0002618095,0.001500877,0.02099868,0.1097624,0.0002270512,0.07103115,0.0001435988],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9472688,0.0002980297,0.007189821,0.0001833187,0.0000971695,0.0003508559,0.0153116,0.0008682724,0.02843216],"genre_scores_gemma":[0.9708027,0.0001322926,0.006533623,0.0001381824,0.00003240247,0.0002692693,0.01472813,0.0001218928,0.007241492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6465453,"threshold_uncertainty_score":0.7027947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01167732274394099,"score_gpt":0.217345575257205,"score_spread":0.205668252513264,"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."}}