{"id":"W7067693091","doi":"","title":"Multi-modal, mobile microscopy for visualization of biological agents","year":2021,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Advanced Research Projects Agency; Defense Advanced Research Projects Agency; McGill University","keywords":"Visualization; Mobile phone; Lens (geology); Biological imaging; Microscopy; Microfluidics; Resolution (logic); Phone; Interface (matter); Biological specimen","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.0003789258,0.00044592,0.0005755626,0.0002260374,0.0006108794,0.0001263466,0.001090388,0.0004272204,0.00004473377],"category_scores_gemma":[0.0001859495,0.0004794232,0.0004083472,0.0008408491,0.00003999956,0.0003992656,0.0002155386,0.0003233675,0.0000466673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001716856,"about_ca_system_score_gemma":0.0001141638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005258069,"about_ca_topic_score_gemma":0.00006251526,"domain_scores_codex":[0.9970848,0.0001649006,0.0008173016,0.001096387,0.0004541996,0.0003824376],"domain_scores_gemma":[0.9969867,0.0003477604,0.0006886317,0.0007614556,0.00104717,0.0001682676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005149934,0.001076497,0.00002828482,0.0003524778,0.0001781316,0.000004802093,0.00003435249,0.0006762017,0.3173305,0.6037465,0.00003380856,0.07648686],"study_design_scores_gemma":[0.003423863,0.0008340255,0.007155068,0.0007431147,0.0002300375,0.00001767759,0.0002777236,0.03477751,0.6388612,0.1822209,0.1285461,0.002912758],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787208,0.0005541454,0.008844127,0.00002029489,0.001971037,0.002895763,0.002537408,0.0003789112,0.004077481],"genre_scores_gemma":[0.9205568,0.0001861901,0.07008011,0.0002114223,0.0000530483,0.0009966408,0.006129524,0.00009336848,0.001692835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4215257,"threshold_uncertainty_score":0.9997658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03802394689216824,"score_gpt":0.3309504758439009,"score_spread":0.2929265289517327,"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."}}