{"id":"W3208387531","doi":"10.5281/zenodo.3406508","title":"AWARE characterization factor samples","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Factor (programming language); Characterization (materials science); Computer science; Materials science; Nanotechnology; Programming language","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":[0.001833568,0.001344839,0.0007567039,0.002304371,0.0006835884,0.001750233,0.001529485,0.001246918,0.1135899],"category_scores_gemma":[0.01997315,0.0008455982,0.001174082,0.004129041,0.0003848467,0.001804865,0.001319507,0.001475567,0.08358754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001098902,"about_ca_system_score_gemma":0.001587725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005506495,"about_ca_topic_score_gemma":0.005962304,"domain_scores_codex":[0.9982042,0.0002283524,0.0001777273,0.0004895139,0.0007271795,0.0001729149],"domain_scores_gemma":[0.9913266,0.003222122,0.0003322424,0.002058798,0.002902949,0.0001573232],"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":[0.000457921,0.0001413706,0.007793108,0.0005938354,0.00005830044,0.00009906709,0.0001305347,0.006019049,0.001860418,0.003255216,0.9235346,0.05605654],"study_design_scores_gemma":[0.0004507347,0.0001208808,0.01647236,0.0002219071,0.00005802589,0.000214785,0.0002811441,0.01879768,0.01028838,0.01007671,0.9429048,0.0001127487],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005902319,0.00008217496,0.01162738,0.0003146599,0.0001187208,0.0003302528,0.9505149,0.02220174,0.008907889],"genre_scores_gemma":[0.01495438,0.00008271197,0.02798992,0.000236701,0.00004859702,0.001387709,0.9438874,0.005659541,0.005753029],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1135899,"threshold_uncertainty_score":0.3799961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07997840928112612,"score_gpt":0.2582508778266486,"score_spread":0.1782724685455225,"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."}}