{"id":"W6912355380","doi":"10.5281/zenodo.16780332","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); Identification (biology)","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":[0.0008631138,0.002070579,0.001323733,0.004691275,0.001362722,0.002169688,0.002780977,0.001794097,0.1121249],"category_scores_gemma":[0.006597455,0.0007256198,0.001355453,0.009809877,0.0004776703,0.001117789,0.001669826,0.001353317,0.06567383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00379378,"about_ca_system_score_gemma":0.008614647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4262184,"about_ca_topic_score_gemma":0.5686501,"domain_scores_codex":[0.9991488,0.00009306697,0.00008275503,0.0002372802,0.0002446415,0.0001933429],"domain_scores_gemma":[0.9978531,0.000437859,0.0001559844,0.0004010543,0.0008394743,0.0003125682],"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":[0.00005163365,0.00001133045,0.0006613496,0.0005096112,0.00003455011,0.00001692982,0.00002569711,0.0003664362,0.000101823,0.0004976606,0.9960454,0.001677672],"study_design_scores_gemma":[0.0001898619,0.00001055492,0.00369159,0.000303516,0.00005278232,0.00003397897,0.00008297064,0.0005748598,0.0002077092,0.001272916,0.9935403,0.00003891308],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007022061,0.00002735761,0.0000218675,0.00002164265,0.000006523684,0.000008216306,0.9992506,0.0001519339,0.000441524],"genre_scores_gemma":[0.0002645725,0.00004078241,0.0002041851,0.00002942048,0.000002182139,0.00004790659,0.9989279,0.00006263226,0.0004204047],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4262184,"threshold_uncertainty_score":0.8474751,"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."}}