{"id":"W4287958791","doi":"10.1002/edn3.335","title":"Comparative analysis of zooplankton diversity in freshwaters: What can we gain from metagenomic analysis?","year":2022,"lang":"en","type":"article","venue":"Environmental DNA","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Montréal; Concordia University; McGill University; Bureau de Coopération Interuniversitaire","funders":"Fonds de recherche du Québec – Nature et technologies; Groupe de recherche interuniversitaire en limnologie; Concordia University; Ministerio de Economía y Competitividad; Natural Sciences and Engineering Research Council of Canada; Liber Ero Foundation; Université du Québec à Montréal; Canada Research Chairs; McGill University","keywords":"Metagenomics; Biology; Biodiversity; Zooplankton; Environmental DNA; Genetic diversity; Ecology; Computational biology; Archaea; Community structure; Evolutionary biology; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003669251,0.0008012165,0.001280447,0.003531573,0.0005630589,0.002141941,0.0006597998,0.0008219738,0.0005540219],"category_scores_gemma":[0.004615624,0.0003176729,0.0008097563,0.002473667,0.0009659073,0.002225989,0.001273906,0.0006119997,0.0001471446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007426232,"about_ca_system_score_gemma":0.0008997332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006245632,"about_ca_topic_score_gemma":0.01197119,"domain_scores_codex":[0.9989968,0.0003784497,0.00008615766,0.0002046706,0.0002264948,0.0001074332],"domain_scores_gemma":[0.9975058,0.0008934623,0.0004189425,0.0001940823,0.0006940391,0.0002936746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004601781,0.00009994174,0.7698634,0.00183327,0.001845494,0.00050242,0.001752988,0.0014606,0.08533303,0.0006213338,0.0006237454,0.1356037],"study_design_scores_gemma":[0.00001386645,0.0002760121,0.9804421,0.0005897537,0.0005368207,0.0003713069,0.003329317,0.003039647,0.004373661,0.003479577,0.003488285,0.00005966392],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.942539,0.03956416,0.01163258,0.003347643,0.00007646784,0.00004996781,0.00108828,0.00008436239,0.001617393],"genre_scores_gemma":[0.9806439,0.008729025,0.009255475,0.0005155783,0.0001323689,0.00002220506,0.0005125473,0.00001336525,0.0001757402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006245632,"threshold_uncertainty_score":0.01940507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02270313338928139,"score_gpt":0.2157851355949157,"score_spread":0.1930820022056343,"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."}}