{"id":"W3009495551","doi":"","title":"LIGHTCURVE PHOTOMETRY OPPORTUNITIES: 2019 APRIL-JUNE.","year":2019,"lang":"en","type":"article","venue":"PubMed","topic":"Astro and Planetary Science","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Takeda (Canada)","funders":"","keywords":"Photometry (optics); Astronomy; Geography; Geology; Physics","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":[0.0006699724,0.0009516598,0.0003817807,0.006871248,0.0006602231,0.0016819,0.000575175,0.0004475105,0.1521029],"category_scores_gemma":[0.001969588,0.0003072436,0.0003949804,0.005703187,0.0001758757,0.001754698,0.001241572,0.0007054782,0.09633065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005261111,"about_ca_system_score_gemma":0.0007216756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005178718,"about_ca_topic_score_gemma":0.01358202,"domain_scores_codex":[0.9996717,0.00001765617,0.00004114971,0.00004639333,0.0001317952,0.00009137232],"domain_scores_gemma":[0.997173,0.0001802904,0.0006222127,0.0002493825,0.00116729,0.0006079024],"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":[0.0003804906,0.00004907377,0.01308396,0.001009545,0.00003479921,0.0002451663,0.0001188012,0.00009777167,0.002178793,0.0008681595,0.8790414,0.1028919],"study_design_scores_gemma":[0.00001776548,0.00002615321,0.03158876,0.0001328578,0.000007807426,0.0001786677,0.00006349479,0.00002776047,0.0004602068,0.0002948821,0.967189,0.00001264409],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0160546,0.004893787,0.001852122,0.001498635,0.001437678,0.0002956297,0.8039542,0.006818712,0.1631946],"genre_scores_gemma":[0.03090161,0.003348831,0.004366429,0.0005724751,0.001166205,0.0001609487,0.8711511,0.002045785,0.08628661],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1521029,"threshold_uncertainty_score":0.5088349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02307754202009459,"score_gpt":0.1955351307463783,"score_spread":0.1724575887262837,"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."}}