{"id":"W6949619847","doi":"10.5281/zenodo.14277812","title":"Insect DNA Barcode and Image Dataset","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Barcode; DNA barcoding; Pattern recognition (psychology); DNA; Vector (molecular biology); DNA sequencing; Image (mathematics); Genus","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000611248,0.001679607,0.00102357,0.0023112,0.0007328413,0.001112343,0.002254642,0.001605924,0.02800618],"category_scores_gemma":[0.00271464,0.0003350581,0.001053289,0.002788597,0.0003833607,0.001336398,0.001519696,0.001586159,0.04961215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001094654,"about_ca_system_score_gemma":0.001119048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01090434,"about_ca_topic_score_gemma":0.01916539,"domain_scores_codex":[0.9988897,0.00007616317,0.0001068449,0.0003177536,0.0004769509,0.0001325944],"domain_scores_gemma":[0.9986987,0.0001518193,0.000132625,0.0003472871,0.0005675032,0.0001020272],"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":[0.000432862,0.0003120691,0.004372279,0.001604444,0.00006314881,0.0001657077,0.00005609474,0.001967798,0.006642309,0.0009458777,0.9326956,0.05074174],"study_design_scores_gemma":[0.0001829452,0.0002792552,0.02547868,0.000377649,0.00006538953,0.0007521417,0.0002028423,0.01122835,0.01439903,0.002292909,0.9445847,0.0001563198],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004203512,0.0005261012,0.002709808,0.00018122,0.0001756349,0.0002121625,0.9830108,0.005505071,0.003475854],"genre_scores_gemma":[0.002581948,0.0001179029,0.004644855,0.0001069337,0.00001403502,0.0002229359,0.9909767,0.0001565636,0.001178049],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02800618,"threshold_uncertainty_score":0.09369004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03881349540082198,"score_gpt":0.2795241234325042,"score_spread":0.2407106280316822,"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."}}