{"id":"W4399893917","doi":"10.1111/mms.13148","title":"Best practices for collecting and preserving marine mammal biological samples in the ‘omics era","year":2024,"lang":"en","type":"article","venue":"Marine Mammal Science","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Marine mammal; Mammal; Biology; Omics; Computational biology; Ecology; Geography; Zoology; Bioinformatics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001548682,0.000161366,0.0001320354,0.000063862,0.0007170067,0.0003190289,0.0007014691,0.00004692671,0.0003672879],"category_scores_gemma":[0.0007139667,0.0001102406,0.00003567656,0.0006774496,0.001214478,0.0005522169,0.003573571,0.0002103539,0.00005266127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002271338,"about_ca_system_score_gemma":0.00001120406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004472619,"about_ca_topic_score_gemma":0.001554244,"domain_scores_codex":[0.9982843,0.00005396039,0.0001802164,0.0006289697,0.0003881238,0.0004644199],"domain_scores_gemma":[0.9989901,0.0006609024,0.00008282322,0.0001932096,0.000005328094,0.0000676066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002873887,0.00005521143,0.9675227,0.00002216588,0.000004864295,0.00002012983,0.0005461678,0.0000567679,0.003993073,0.0003216717,0.0003218916,0.02710666],"study_design_scores_gemma":[0.0001888871,0.0002167658,0.9713663,0.00001207779,0.00001530808,0.00004074578,0.001336486,0.002533763,0.0004505392,0.001186785,0.02245309,0.0001992678],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805409,0.00006415035,0.0000417499,0.00195552,0.0001319011,0.0004843982,0.00001564306,0.00003325228,0.01673248],"genre_scores_gemma":[0.975329,0.0002378039,0.0226783,0.0002459951,0.00006820787,0.00005509312,0.00000636858,0.000007348864,0.001371872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02690739,"threshold_uncertainty_score":0.6761293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07434841323214432,"score_gpt":0.2934794953459273,"score_spread":0.219131082113783,"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."}}