{"id":"W6899344792","doi":"10.5883/ds-cangibel","title":"Using DNA barcoding to identify Prussian carp (Carassius gibelio, Bloch, 1782) in southern Alberta, Canada","year":2023,"lang":"en","type":"dataset","venue":"Barcode of Life Data Systems","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"DNA barcoding; Barcode; Mitochondrial DNA; Sequence (biology); DNA sequencing; DNA","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.003155659,0.001692116,0.003347999,0.002228529,0.0003019364,0.0006128639,0.008274695,0.0009279357,0.0001481139],"category_scores_gemma":[0.003501057,0.001835101,0.0001981793,0.003350367,0.0001768812,0.0006785268,0.004228624,0.00139332,0.008917151],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002084703,"about_ca_system_score_gemma":0.007537515,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9979764,"about_ca_topic_score_gemma":0.9954394,"domain_scores_codex":[0.9855983,0.0015306,0.003939877,0.003120848,0.003550759,0.002259658],"domain_scores_gemma":[0.9860188,0.0009114541,0.002256457,0.009131282,0.0002981221,0.001383926],"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":[0.0001031276,0.0001043098,0.0003279812,0.002846768,0.000515646,0.0006125926,0.0002067541,0.002525941,0.001121351,0.000009047401,0.9916162,0.00001026308],"study_design_scores_gemma":[0.001376108,0.00004604087,0.0004248344,0.00780465,0.0006135816,0.0001118574,0.005128391,0.004892995,0.00002649288,0.000001472745,0.977109,0.002464612],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006616711,0.000816277,0.00002217852,0.00008403348,0.004030166,0.002750821,0.9854698,0.0001329777,0.00007701569],"genre_scores_gemma":[0.009233552,0.00002621094,0.00009980352,0.0001064887,0.001419116,0.0001889718,0.987624,0.0007416556,0.0005601493],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01450725,"threshold_uncertainty_score":0.9995825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09899601282433622,"score_gpt":0.3412950179235631,"score_spread":0.2422990050992269,"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."}}