{"id":"W1606084391","doi":"10.1371/journal.pbio.1002151","title":"Applying the ARRIVE Guidelines to an In Vivo Database","year":2015,"lang":"en","type":"article","venue":"PLoS Biology","topic":"Animal testing and alternatives","field":"Veterinary","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Centre for Phenogenomics; Mount Sinai Hospital","funders":"National Cancer Institute; National Bioscience Database Center; RIKEN; Japan Science and Technology Agency; National Institutes of Health; Centre National de la Recherche Scientifique; Bundesministerium für Bildung und Forschung; Université de Strasbourg; Institut National de la Santé et de la Recherche Médicale; Ministry of Education, Culture, Sports, Science and Technology; CHIST-ERA; National Human Genome Research Institute; Wellcome Trust; Agence Nationale de la Recherche; PHENOMIN; European Commission; INFRAFRONTIER; Genome Canada","keywords":"Transparency (behavior); Biology; Context (archaeology); Resource (disambiguation); Data science; Bioinformatics; Computational biology; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2290567,0.001576747,0.003970504,0.01095957,0.003776813,0.01602846,0.01203894,0.006415786,0.0212268],"category_scores_gemma":[0.2222351,0.002598376,0.003635783,0.007981268,0.004058925,0.005717991,0.008092892,0.009954885,0.02769622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004053495,"about_ca_system_score_gemma":0.04037161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006213935,"about_ca_topic_score_gemma":0.01091294,"domain_scores_codex":[0.783808,0.1095493,0.05809756,0.005615925,0.03920413,0.003725179],"domain_scores_gemma":[0.6333462,0.08113423,0.02539904,0.09020169,0.1587068,0.01121203],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0014704,0.000782898,0.003074333,0.01009411,0.0004863137,0.001823336,0.004257239,0.001308275,0.01822058,0.08454771,0.6568111,0.2171238],"study_design_scores_gemma":[0.0001086752,0.0002143922,0.001137413,0.004782375,0.0001306652,0.0004076473,0.0004779979,0.0004731177,0.004378031,0.009602127,0.978182,0.0001056393],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004236247,0.008822725,0.6971574,0.03224536,0.008537223,0.05832992,0.06491005,0.0142531,0.1115079],"genre_scores_gemma":[0.01077539,0.0093189,0.8149388,0.01550086,0.001482141,0.05777325,0.06700111,0.004328123,0.01888137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7709433,"threshold_uncertainty_score":0.9507103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5746571148505991,"score_gpt":0.4894639404915394,"score_spread":0.08519317435905976,"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."}}