{"id":"W4405822708","doi":"10.1101/2024.12.23.629923","title":"eDNA provides accurate population abundance estimates with bioenergetics and particle mass-balance modelling","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Concordia University; Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Ministerio de Economía y Competitividad; Parks Canada","keywords":"Bioenergetics; Abundance (ecology); Balance (ability); Particle (ecology); Environmental science; Energy balance; Econometrics; Ecology; Economics; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001320181,0.000763261,0.0006314603,0.0007593456,0.0002786105,0.001020799,0.0009129033,0.0006701024,0.0009444894],"category_scores_gemma":[0.003026635,0.0004895966,0.0007001084,0.0006375724,0.0003932892,0.001217222,0.00106053,0.0005134123,0.0001939382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008829525,"about_ca_system_score_gemma":0.0007108105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01337199,"about_ca_topic_score_gemma":0.01458728,"domain_scores_codex":[0.9995868,0.0001183594,0.00002653637,0.0001711136,0.00007623623,0.00002096478],"domain_scores_gemma":[0.9991838,0.000346522,0.0002021174,0.00009527986,0.0001348596,0.00003734243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008778289,0.00009049127,0.03848073,0.000093298,0.0002714962,0.0000520827,0.000105684,0.9092755,0.01287963,0.002550148,0.0003766125,0.03573647],"study_design_scores_gemma":[0.000007904274,0.00001652952,0.004930718,0.000008494958,0.00002222055,0.00001015244,0.00001578538,0.9918916,0.001088287,0.001519035,0.0004768983,0.00001244473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2862445,0.0002690175,0.7101379,0.0002258704,0.00003459219,0.00004037847,0.0005636515,0.0007904414,0.001693659],"genre_scores_gemma":[0.8162228,0.000122197,0.1816158,0.00008179533,0.00002555146,0.00009792748,0.0006190435,0.0001026984,0.001112117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01337199,"threshold_uncertainty_score":0.02658826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01380194425698883,"score_gpt":0.202109874912018,"score_spread":0.1883079306550292,"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."}}