{"id":"W3134267451","doi":"10.3897/aca.4.e65156","title":"There's always a better way: The application of eDNA to effectively assess biodiversity","year":2021,"lang":"en","type":"article","venue":"ARPHA Conference Abstracts","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Parks Canada","funders":"Parks Canada","keywords":"Biodiversity; Environmental resource management; Ecosystem; Environmental DNA; Population; Abundance (ecology); Trophic level; Psychological resilience; Scale (ratio); Ecology; Environmental change; Resilience (materials science); Geography; Environmental science; Biology; Climate change; Cartography","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":[0.07460366,0.001630162,0.002354454,0.00414766,0.002320042,0.0103788,0.003847117,0.004944121,0.01062184],"category_scores_gemma":[0.1098743,0.001099583,0.002137341,0.00360585,0.005270755,0.02054633,0.008127572,0.01092581,0.004476866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002174869,"about_ca_system_score_gemma":0.005727435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006438755,"about_ca_topic_score_gemma":0.01340076,"domain_scores_codex":[0.9740227,0.01463973,0.0022001,0.003674314,0.004685056,0.0007780663],"domain_scores_gemma":[0.8375492,0.09019668,0.008633466,0.02208668,0.0371519,0.004382084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003801999,0.0002711404,0.06781731,0.004807523,0.000887275,0.0004630677,0.004732721,0.00277363,0.01728107,0.03879955,0.1638027,0.6979839],"study_design_scores_gemma":[0.0001263005,0.0005085127,0.03976438,0.01288603,0.0007771612,0.001723418,0.008943327,0.009111855,0.02119704,0.2070218,0.6970377,0.0009024196],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02887779,0.04558635,0.5630658,0.3034256,0.01538552,0.001003215,0.007120132,0.004061516,0.03147414],"genre_scores_gemma":[0.05454976,0.01134014,0.8749729,0.04967608,0.002156721,0.0006769276,0.001421026,0.00133754,0.003868839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07460366,"threshold_uncertainty_score":0.3945466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02507878051001865,"score_gpt":0.2309799900300382,"score_spread":0.2059012095200196,"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."}}