{"id":"W4385369073","doi":"10.1002/edn3.459","title":"Beyond species detection<b>—</b>leveraging environmental DNA and environmental RNA to push beyond presence/absence applications","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":"Université Laval; University of Windsor","funders":"","keywords":"Environmental DNA; Ecology; Identification (biology); Biology; Biodiversity","routes":{"ca_aff":true,"ca_fund":false,"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","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003945464,0.000908862,0.0005527388,0.0002592109,0.001686573,0.000141627,0.0009078579,0.0002501784,0.004870268],"category_scores_gemma":[0.00002019745,0.001020663,0.0002333274,0.0004746273,0.002032068,0.000855859,0.003467633,0.00048818,0.01504841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431069,"about_ca_system_score_gemma":0.000003930993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000611526,"about_ca_topic_score_gemma":0.00002798432,"domain_scores_codex":[0.9943336,0.0001400557,0.0006919156,0.001979716,0.001489777,0.001364961],"domain_scores_gemma":[0.9977706,0.000197626,0.0002400462,0.001021712,8.588071e-7,0.0007691634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004676701,0.0003679217,0.2528666,0.00001482976,0.00008695471,0.00005719467,0.002303655,0.0004010013,0.7188721,0.00001518011,0.004211308,0.02075652],"study_design_scores_gemma":[0.0009622583,0.0002837534,0.8155618,0.00001756675,0.0001026429,0.00006899093,0.006070148,0.0003832631,0.1081136,0.0006395489,0.06634266,0.001453752],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906297,0.0002412879,0.0003433046,0.0005347689,0.0003978068,0.001790019,0.0009703478,0.0003103458,0.004782449],"genre_scores_gemma":[0.9881335,0.001324306,0.001738942,0.0007840593,0.0002090053,0.0004582525,0.0003777704,0.0001157847,0.006858415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6107584,"threshold_uncertainty_score":0.9996131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009046726181211464,"score_gpt":0.1905828927926111,"score_spread":0.1815361666113997,"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."}}