{"id":"W3128156827","doi":"10.1002/biot.202100076","title":"Designing microfluidic devices for behavioral screening of multiple zebrafish larvae","year":2021,"lang":"en","type":"article","venue":"Biotechnology Journal","topic":"Zebrafish Biomedical Research Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Trillium Foundation","keywords":"Microfluidics; Zebrafish; Throughput; Computer science; Ichthyoplankton; Larva; Biology; Biomedical engineering; Nanotechnology; Materials science; Engineering; Ecology; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0003807433,0.0001166837,0.0001765572,0.0001213083,0.0001920169,0.00002892434,0.0003843493,0.0004565487,0.0000281669],"category_scores_gemma":[0.0007170998,0.0001156939,0.0001442038,0.000218794,0.0002841503,0.000006103226,0.0001975833,0.0002968948,0.000002984073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000158001,"about_ca_system_score_gemma":0.0002482486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005721372,"about_ca_topic_score_gemma":0.00002655523,"domain_scores_codex":[0.9988172,0.00005513846,0.0003189601,0.0002780182,0.0001605975,0.0003700618],"domain_scores_gemma":[0.9990774,0.00004370978,0.0001570052,0.0002787376,0.0003116986,0.0001313806],"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":[0.00004051359,0.0001088177,0.002296043,0.0000130297,0.00006456701,0.0000109685,0.000006433227,9.347913e-7,0.9776499,0.00006824813,0.005607136,0.01413346],"study_design_scores_gemma":[0.0007755572,0.0002746088,0.000963393,0.00002740197,0.00002422194,0.0002557207,0.0002057923,0.00002712071,0.9150876,0.000114479,0.08213642,0.0001076474],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6731228,0.004500387,0.3198605,0.002154143,0.00007580393,0.000188197,0.00005417065,0.00002283284,0.00002122802],"genre_scores_gemma":[0.9077775,0.0007073231,0.09094007,0.0001680865,0.000165415,0.00003521548,0.00009679452,0.00002443975,0.00008515598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2346547,"threshold_uncertainty_score":0.4717861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03027587614278247,"score_gpt":0.3159087622131382,"score_spread":0.2856328860703557,"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."}}