{"id":"W6983231137","doi":"","title":"<sup>45</sup>Ti extraction using hydroxamate resin","year":2012,"lang":"en","type":"article","venue":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","topic":"Titanium Alloys Microstructure and Properties","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Extraction (chemistry); Fraction (chemistry); Yield (engineering); Column (typography); Component (thermodynamics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001594612,0.000745413,0.001368386,0.0006296608,0.0008938147,0.00006496526,0.002328506,0.00132589,0.005631048],"category_scores_gemma":[0.0002722678,0.0008600389,0.0007208757,0.001147193,0.002157132,0.001868406,0.001616483,0.001053044,0.0006839675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008403598,"about_ca_system_score_gemma":0.0003296526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008538715,"about_ca_topic_score_gemma":0.000605169,"domain_scores_codex":[0.9948204,0.0005740986,0.0008166526,0.001143557,0.001260014,0.00138528],"domain_scores_gemma":[0.9959186,0.000490137,0.0009147195,0.001449701,0.0005236131,0.0007032654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001666067,0.0005542386,0.001294025,0.0001995268,0.00009842305,0.0001693558,0.0003728651,0.0001942037,0.9847577,0.002177848,0.00783313,0.000682586],"study_design_scores_gemma":[0.01275844,0.002905694,0.082084,0.002500368,0.003873836,0.002410619,0.005933153,0.002096692,0.5219179,0.002734033,0.353326,0.007459353],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9720536,0.001081016,0.008275153,0.0004839211,0.0003678921,0.0008359867,0.0002203707,0.0004285882,0.01625342],"genre_scores_gemma":[0.9696558,0.0004818438,0.02646151,0.00007112795,0.0001395403,3.089074e-7,0.0000278126,0.00006893314,0.003093099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4628399,"threshold_uncertainty_score":0.9999706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02021729746115495,"score_gpt":0.2239937927403933,"score_spread":0.2037764952792384,"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."}}