{"id":"W4225417045","doi":"10.1101/2022.05.01.490210","title":"Metabarcoding metacommunities: time, space, and land use interact to structure aquatic macroinvertebrate communities in streams","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metacommunity; Ecology; Biological dispersal; STREAMS; Riparian zone; Community structure; Bioindicator; Habitat; Biodiversity; Spatial ecology; Aquatic ecosystem; Invertebrate; Distance decay; Occupancy; Environmental science; Biology; Population","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":[0.0003417574,0.000140724,0.0002531069,0.0009678265,0.0004354421,0.0006586447,0.0002149884,0.0001748448,0.0006345012],"category_scores_gemma":[0.001254436,0.0001214961,0.0001671407,0.001027863,0.0003764028,0.0004016227,0.0003792924,0.0001666772,0.00007928315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005148008,"about_ca_system_score_gemma":0.0004828276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06626128,"about_ca_topic_score_gemma":0.2264636,"domain_scores_codex":[0.9997205,0.00005716948,0.00002511819,0.00008677143,0.00006326973,0.00004709464],"domain_scores_gemma":[0.9989459,0.0002327288,0.0004621316,0.00004976165,0.0001945904,0.0001148401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005284198,0.0000101704,0.9768296,0.00003938441,0.00006773059,0.00002996886,0.0003626567,0.0001991493,0.0147666,0.00003164283,0.0000576243,0.007552666],"study_design_scores_gemma":[6.660467e-7,0.00001321387,0.9989694,0.000003454056,0.00001097723,0.00001833216,0.0001602067,0.0004477375,0.0002445353,0.00002182481,0.0001075532,0.00000216214],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987987,0.0001599351,0.0005231259,0.00001831611,8.975048e-7,0.000006819543,0.0002037918,0.000007269924,0.0002811659],"genre_scores_gemma":[0.9987856,0.00006512964,0.0008124061,0.000008839878,0.000001510306,0.000006327399,0.0001703456,0.00000281026,0.0001470924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06626128,"threshold_uncertainty_score":0.1317512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0166050811693381,"score_gpt":0.2057081839007015,"score_spread":0.1891031027313634,"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."}}