{"id":"W6887926359","doi":"10.18452/24373","title":"Learning physical descriptors for materials science by compressed sensing","year":2017,"lang":"en","type":"other","venue":"edoc Publication server (Humboldt University of Berlin)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Banting and Best Diabetes Centre, University of Toronto; Einstein Stiftung Berlin; Neuron Nadační Fond Na Podporu Vědy; European Commission; National Science Foundation","keywords":"Compressed sensing; Binary number; Feature (linguistics); Big data; Physical system; Binary data; Statistical learning; SIGNAL (programming language)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009740268,0.0004504616,0.0008563947,0.0008569038,0.0009623463,0.0004867289,0.001958057,0.0003376916,0.0007927709],"category_scores_gemma":[0.0003712987,0.0005865647,0.0002220142,0.0005083644,0.001473112,0.001423004,0.0004709878,0.0002848106,0.0005694771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003287437,"about_ca_system_score_gemma":0.0005652902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002165669,"about_ca_topic_score_gemma":0.0001507449,"domain_scores_codex":[0.9969687,0.0001963571,0.0002687656,0.001054958,0.0008895102,0.0006216881],"domain_scores_gemma":[0.9950101,0.00008993598,0.002082994,0.001325735,0.00121851,0.0002726719],"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":[0.00007313284,0.0001771214,0.00008554079,0.0001938974,0.0001499935,0.000001329958,0.0006338292,0.00001029306,0.1121744,0.0010297,0.8841013,0.001369465],"study_design_scores_gemma":[0.001323188,0.00007279919,0.0003401698,0.0002810567,0.0001951075,0.000001943875,0.000294937,0.0008677162,0.0152419,0.00004596211,0.980716,0.000619158],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1913268,0.00042641,0.01612619,0.001781815,0.004159086,0.00824905,0.009156818,0.005029834,0.763744],"genre_scores_gemma":[0.1277459,0.00003419973,0.005305739,0.00004027556,0.0005119031,0.000001606801,0.00261461,0.0006984997,0.8630472],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.09930322,"threshold_uncertainty_score":0.9996586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01956971415889157,"score_gpt":0.253326580635606,"score_spread":0.2337568664767144,"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."}}