{"id":"W4281400275","doi":"10.3389/frwa.2022.870453","title":"It Takes a Village: Using a Crowdsourced Approach to Investigate Organic Matter Composition in Global Rivers Through the Lens of Ecological Theory","year":2022,"lang":"en","type":"article","venue":"Frontiers in Water","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Pacific Northwest National Laboratory; Biological and Environmental Research; Office of Science; Battelle; U.S. Department of Energy","keywords":"Globe; Data science; Leverage (statistics); Computer science; Citizen science; Ecology; Psychology; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03782824,0.0008908279,0.0006809144,0.005734611,0.007169478,0.009640702,0.003179501,0.002975385,0.005975513],"category_scores_gemma":[0.06624331,0.0005775592,0.001691155,0.004404347,0.007335511,0.009303009,0.01563752,0.002871019,0.002121941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002509913,"about_ca_system_score_gemma":0.006044015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003577364,"about_ca_topic_score_gemma":0.008541124,"domain_scores_codex":[0.9652182,0.02658073,0.0008378589,0.003358675,0.003049971,0.0009545931],"domain_scores_gemma":[0.901928,0.07313222,0.003929105,0.01159346,0.006135663,0.003281507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0007906297,0.001000908,0.0402563,0.003414418,0.0003587163,0.0028709,0.5474295,0.003472784,0.01244197,0.02807204,0.04065922,0.3192326],"study_design_scores_gemma":[0.0005500662,0.0008536735,0.01882833,0.002385962,0.0003641963,0.0007051695,0.4107948,0.009881387,0.007848817,0.1990221,0.3482757,0.0004899164],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5552732,0.003328769,0.2720214,0.04844334,0.00265515,0.006940559,0.008236513,0.002312084,0.100789],"genre_scores_gemma":[0.6476207,0.001710505,0.3086512,0.0107217,0.0009426343,0.007685542,0.003217072,0.00136995,0.01808065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03782824,"threshold_uncertainty_score":0.2000573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01583818156438386,"score_gpt":0.200966573181758,"score_spread":0.1851283916173741,"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."}}