{"id":"W7090705738","doi":"10.5281/zenodo.17352563","title":"CanPests V1","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Genome Canada","keywords":"Barcode; DNA barcoding; Identifier; Host (biology); Distribution (mathematics)","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":[0.0008584911,0.002399764,0.001394208,0.005321173,0.001502215,0.002244906,0.002959779,0.002128678,0.1225314],"category_scores_gemma":[0.00722882,0.0007899394,0.001569401,0.01055446,0.0005384044,0.001114724,0.0016223,0.001485878,0.07280767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004139272,"about_ca_system_score_gemma":0.009496574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.489296,"about_ca_topic_score_gemma":0.6290548,"domain_scores_codex":[0.9990915,0.00009700911,0.0000807316,0.0002388934,0.0002759577,0.0002159262],"domain_scores_gemma":[0.9975196,0.0005574659,0.0001729645,0.0004344492,0.0009453853,0.0003701299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005243268,0.00001223289,0.0005731822,0.0005265738,0.00003272815,0.00001656488,0.00002463045,0.0003991912,0.00009228261,0.0004588001,0.9961493,0.001662206],"study_design_scores_gemma":[0.0002104959,0.00001179822,0.003474946,0.0003273992,0.00005530798,0.00003301085,0.00007962374,0.000666554,0.0002067533,0.001305873,0.9935871,0.0000411232],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007109249,0.00003322946,0.00002277103,0.00002387715,0.00000708648,0.000007785158,0.9991888,0.0001682391,0.0004770689],"genre_scores_gemma":[0.0002609611,0.00004394878,0.0002015699,0.00002971949,0.000002235181,0.00004230982,0.9989272,0.00006618239,0.0004258194],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.489296,"threshold_uncertainty_score":0.9728959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01999276170835279,"score_gpt":0.2229697093475264,"score_spread":0.2029769476391736,"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."}}