{"id":"W6948256856","doi":"10.5067/lidar/ozone/tolnet/eccc","title":"TOLNet Environment and Climate Change Canada Data","year":2022,"lang":"en","type":"dataset","venue":"Earth Observing System Data and Information System","topic":"Wood and Agarwood Research","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Climate change; Effects of global warming; Climate system; Work (physics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001229612,0.0004301092,0.0006288926,0.0001197989,0.0006292474,0.0005423415,0.002544662,0.0002280459,0.001576123],"category_scores_gemma":[0.00005018914,0.0003959168,0.00002196801,0.000113654,0.00003622197,0.002689269,0.008512035,0.0005459286,0.00006425106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000222047,"about_ca_system_score_gemma":0.000272611,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1268273,"about_ca_topic_score_gemma":0.02731551,"domain_scores_codex":[0.9964343,0.0001328233,0.00101732,0.0006837235,0.001177059,0.0005547789],"domain_scores_gemma":[0.9944628,0.0001055267,0.0006703581,0.004449502,0.00003582341,0.0002759873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001464414,0.000006095409,0.0001887079,0.02429469,0.0000839487,0.00004124501,0.00008467105,0.000002111286,0.000002215027,0.00002767393,0.9738297,0.001424226],"study_design_scores_gemma":[0.0003977151,0.00001502037,0.0001453473,0.001235205,0.00007599989,0.0002042076,0.005221088,0.004167008,0.000003536965,1.922897e-8,0.9881313,0.0004035165],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004628217,0.001254162,0.000002592101,0.00009070994,0.0004820377,0.0004385845,0.9965088,0.00007945567,0.0006808641],"genre_scores_gemma":[0.004371391,0.0009618012,0.00002210901,0.00010088,0.0003833278,0.0001009291,0.9939857,0.00002193928,0.00005191342],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09951179,"threshold_uncertainty_score":0.9998493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05575384095052682,"score_gpt":0.2407112634769487,"score_spread":0.1849574225264219,"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."}}