{"id":"W4382449738","doi":"10.1002/edn3.444","title":"How <scp>eDNA</scp> data filtration, sequence coverage, and primer selection influence assessment of fish communities in northern temperate lakes","year":2023,"lang":"en","type":"article","venue":"Environmental DNA","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs; Ministère des Ressources naturelles et des Forêts; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Species richness; Environmental DNA; Primer (cosmetics); Biology; Fish <Actinopterygii>; Temperate climate; Biodiversity; Ecology; Sample (material); Range (aeronautics); Fishery; Geography; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003650252,0.0002687421,0.000251384,0.00008067103,0.0003231572,0.00006521757,0.0005386145,0.0000938898,0.00009581014],"category_scores_gemma":[0.00004408977,0.0002845186,0.00003257205,0.0002865155,0.000955157,0.001098286,0.001408464,0.0002407514,0.00009295246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000371242,"about_ca_system_score_gemma":0.000006326154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004381294,"about_ca_topic_score_gemma":0.003441136,"domain_scores_codex":[0.9981441,0.0001614604,0.0002894744,0.0005003958,0.0005341357,0.0003704404],"domain_scores_gemma":[0.9990068,0.0001858093,0.0001693338,0.0005518568,0.00000224805,0.00008397123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002405941,0.0001020074,0.9311302,0.00001623329,0.00002786867,0.000004272191,0.0009489367,0.002013364,0.06440309,0.000004268857,0.0008535534,0.0004937644],"study_design_scores_gemma":[0.0003808899,0.0001445592,0.985872,0.00002334591,0.00002017527,0.00000653653,0.003326669,0.0009748612,0.005061299,0.00004524804,0.004000327,0.0001441303],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982302,0.00003539375,0.00002019896,0.0001995222,0.00004782414,0.0003826083,0.0006260372,0.00005159195,0.0004066013],"genre_scores_gemma":[0.9967629,0.001122649,0.0006236637,0.0002030787,0.0000121019,0.00002756593,0.0005907264,0.0000206974,0.0006366278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05934179,"threshold_uncertainty_score":0.9999607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02667286432502379,"score_gpt":0.2415519248212381,"score_spread":0.2148790604962144,"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."}}