{"id":"W2995433651","doi":"","title":"The CAnadian NIRISS Unbiased Cluster Survey (CANUCS)","year":2017,"lang":"en","type":"article","venue":"American Astronomical Society Meeting Abstracts #230","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Cluster (spacecraft); Computer science","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","sts"],"consensus_categories":[],"category_scores_codex":[0.001168655,0.0003727892,0.0003680974,0.00002813142,0.001932277,0.0008414279,0.001014562,0.0001270689,0.00001019763],"category_scores_gemma":[0.0006145654,0.0003410396,0.0002639141,0.0000988253,0.0009022416,0.0002287516,0.0001436299,0.0007406462,0.0001756343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009456708,"about_ca_system_score_gemma":0.000221492,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4145163,"about_ca_topic_score_gemma":0.2840813,"domain_scores_codex":[0.9975492,0.00009088469,0.0004774645,0.0004109352,0.0002693938,0.001202132],"domain_scores_gemma":[0.9972406,0.0008340062,0.0003001053,0.0008707566,0.00008558809,0.0006689472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003305197,0.00003192146,0.8623148,0.00002474288,0.0004134521,0.000005259409,0.0003871383,0.0807837,0.006103315,0.00001032276,0.01727956,0.03261276],"study_design_scores_gemma":[0.0003478388,0.00003614235,0.9864825,0.00004349399,0.00002277736,0.000001181,0.0003896788,0.003226785,0.003444023,0.00001064279,0.005510497,0.0004844169],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927091,0.00005138091,0.000122374,0.000604656,0.000853247,0.0002307629,0.00005877243,0.0001831724,0.00518656],"genre_scores_gemma":[0.9962273,0.00001552553,0.002797053,0.00008078866,0.0006390172,0.00002707712,0.00002455085,0.0001034399,0.00008523755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1304349,"threshold_uncertainty_score":0.9999042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01505429779358487,"score_gpt":0.2388148492254731,"score_spread":0.2237605514318882,"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."}}