{"id":"W2287528554","doi":"10.1111/ddi.12427","title":"Metabarcoding reveals strong spatial structure and temporal turnover of zooplankton communities among marine and freshwater ports","year":2016,"lang":"en","type":"article","venue":"Diversity and Distributions","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; McGill University","funders":"Ministerio de Economía y Competitividad; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Abbott Canada","keywords":"Biodiversity; Environmental DNA; Taxonomic rank; Ecology; Zooplankton; Arctic; Habitat; Beta diversity; Identification (biology); Biology; Geography; Taxon","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006830101,0.0001036439,0.0001360452,0.00001856062,0.0008936067,0.00001188996,0.00007405464,0.0000478442,0.0006944644],"category_scores_gemma":[0.00001043409,0.00007891697,0.00002135923,0.00003135856,0.001119497,0.0002657707,0.002946435,0.00006096035,0.000002184529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004681223,"about_ca_system_score_gemma":6.464757e-7,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007470999,"about_ca_topic_score_gemma":0.003500207,"domain_scores_codex":[0.9994683,0.0000316399,0.00008807929,0.0001415602,0.0001320286,0.000138384],"domain_scores_gemma":[0.9997196,0.0000435139,0.00005921182,0.0000998013,0.000004580135,0.00007335479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001195847,0.00001581263,0.9977963,0.000008222116,0.00002417629,0.0000030702,0.0005332369,6.135951e-7,0.0008143802,0.0001107946,0.0002804882,0.0004009561],"study_design_scores_gemma":[0.0003401571,0.00003243063,0.9974533,0.00001361759,0.00005698615,0.000004203496,0.0006473387,0.000007832144,0.0007448734,0.0003385579,0.0002558269,0.000104854],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968949,0.00002970266,0.000191495,0.00009907104,0.00002487888,0.000087049,0.0025106,0.000009973312,0.0001523077],"genre_scores_gemma":[0.9993781,0.0001055354,0.0003112137,0.00001253282,0.000005732321,4.948149e-7,0.00009223025,0.000001785649,0.0000923519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003970792,"threshold_uncertainty_score":0.9991384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01413086218914672,"score_gpt":0.190874787915035,"score_spread":0.1767439257258882,"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."}}