{"id":"W27720022","doi":"10.1016/j.prevetmed.2016.09.011","title":"Microfluidics as a tool for micro-manipulation","year":2008,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Food Inspection Agency","keywords":"Microfluidics; Computer science; Nanotechnology; Materials science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007115811,0.0004389673,0.0004549992,0.0005059771,0.0003862653,0.0008979205,0.0009250463,0.0006618163,0.002314397],"category_scores_gemma":[0.001019499,0.0003818539,0.0004345182,0.0002605623,0.0005910058,0.0006377912,0.001248745,0.0008442919,0.00130439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005402533,"about_ca_system_score_gemma":0.0004445958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006697748,"about_ca_topic_score_gemma":0.0009574444,"domain_scores_codex":[0.9994186,0.00007730909,0.00004487186,0.0001408852,0.0002455213,0.0000728045],"domain_scores_gemma":[0.9994442,0.0002120236,0.0001066794,0.0001108197,0.00007139789,0.00005496992],"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":[0.00006397088,0.00009005317,0.0006948097,0.0004106906,0.00004008173,0.0002360779,0.0001535101,0.002564105,0.9245878,0.00713981,0.003427784,0.06059129],"study_design_scores_gemma":[0.00006085974,0.0006356012,0.002669714,0.0001125184,0.00006803117,0.0006264233,0.00009813609,0.02657591,0.8292911,0.003374609,0.1363802,0.0001067796],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2269875,0.02074968,0.6909693,0.003669091,0.003901367,0.001211939,0.00287361,0.01085595,0.03878141],"genre_scores_gemma":[0.6029575,0.009478102,0.3675909,0.001615549,0.0003994881,0.001864652,0.0009775534,0.000329954,0.01478624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002314397,"threshold_uncertainty_score":0.007742405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02917703236350097,"score_gpt":0.2680589276216382,"score_spread":0.2388818952581373,"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."}}