{"id":"W3164269982","doi":"10.1111/mec.15942","title":"Trade‐offs between reducing complex terminology and producing accurate interpretations from environmental DNA: Comment on “Environmental DNA: What's behind the term?” by Pawlowski et al., (2020)","year":2021,"lang":"en","type":"letter","venue":"Molecular Ecology","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Government of Canada; Fisheries and Oceans Canada; Ministère des Ressources naturelles et des Forêts","funders":"H2020 European Research Council; Ministerio de Ciencia e Innovación","keywords":"Terminology; Environmental DNA; Biology; Term (time); Biomonitoring; Evolutionary biology; Ecology; Environmental resource management; Biodiversity; Linguistics","routes":{"ca_aff":true,"ca_fund":false,"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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003005516,0.0009940671,0.0009580753,0.00008352274,0.0008238279,0.0002422688,0.001236419,0.0007169343,0.002412973],"category_scores_gemma":[0.00004682301,0.0009379264,0.0002768243,0.00007866079,0.002153402,0.0003988169,0.003306604,0.002090127,0.0004840137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001245834,"about_ca_system_score_gemma":0.00001170114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001845069,"about_ca_topic_score_gemma":0.00004136973,"domain_scores_codex":[0.994148,0.001119083,0.0007454993,0.002240296,0.0007020188,0.001045127],"domain_scores_gemma":[0.9971993,0.0006641183,0.0005301749,0.001438415,0.000001496987,0.0001665251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001881573,0.0002777542,0.06953246,0.00001475325,0.0006369295,0.001012659,0.00290347,0.00007146351,0.04273761,5.726575e-7,0.8795859,0.003207595],"study_design_scores_gemma":[0.000963515,0.000507251,0.4885667,0.00008177499,0.0005310508,0.0001006612,0.00134125,0.00004341351,0.006027223,0.0000703156,0.5005958,0.001171038],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"commentary","genre_scores_codex":[0.5415177,0.0004814842,0.00001910753,0.4553842,0.0003815366,0.0008867491,0.001182233,0.00003578827,0.0001112448],"genre_scores_gemma":[0.4314929,0.001187091,0.0005350942,0.56047,0.0001562463,0.0001579123,0.005723043,0.00009733924,0.0001803581],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.4190343,"threshold_uncertainty_score":0.9993072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01583998661772289,"score_gpt":0.2255960671866768,"score_spread":0.209756080568954,"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."}}