{"id":"W6927600055","doi":"10.34943/7893a521-3728-4c54-b9ce-77150250369d","title":"Barkley Canyon Mid-East Accelerometer Deployed 2016-06-15","year":2016,"lang":"en","type":"dataset","venue":"Ocean Networks Canada Society","topic":"Microbial metabolism and enzyme function","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Canyon; Accelerometer; Software deployment; Benthic zone; Acceleration; Sediment; Measure (data warehouse)","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":[0.0007586437,0.001921071,0.001056302,0.00236135,0.0009428015,0.001536292,0.002318616,0.001120986,0.01284433],"category_scores_gemma":[0.003626148,0.000439241,0.0005929848,0.004545831,0.0005310215,0.001051653,0.00154358,0.001339622,0.0299236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002736379,"about_ca_system_score_gemma":0.00523907,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3113555,"about_ca_topic_score_gemma":0.5372787,"domain_scores_codex":[0.9991068,0.00007796262,0.00007027207,0.0002293469,0.0003544903,0.0001610697],"domain_scores_gemma":[0.9982541,0.0001391668,0.0001116704,0.0003760167,0.0009128997,0.000206243],"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":[0.00008364914,0.00002073479,0.002474396,0.0002981318,0.00002619071,0.00004467537,0.00004458127,0.0006615349,0.0002210005,0.0005197922,0.9920963,0.003509048],"study_design_scores_gemma":[0.0000935633,0.000015429,0.01316012,0.000220032,0.00001906914,0.00005481127,0.0002070494,0.001152691,0.0006861921,0.0008896498,0.9834646,0.00003679869],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005309713,0.00008450004,0.000121769,0.00007265308,0.00003027118,0.00001228612,0.9971897,0.0005767255,0.001381206],"genre_scores_gemma":[0.0008973493,0.00005549374,0.0003100099,0.00002080718,0.000004664566,0.00002498919,0.9977203,0.00004870435,0.0009176967],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6886445,"threshold_uncertainty_score":0.6190864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005858495849063703,"score_gpt":0.1875232999177747,"score_spread":0.181664804068711,"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."}}