{"id":"W4394120926","doi":"10.6084/m9.figshare.1210773.v1","title":"Metadata: Fish Mercury Datalayer for Canada (FIDMAC).","year":2014,"lang":"en","type":"dataset","venue":"Figshare","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mercury (programming language); Metadata; Fish <Actinopterygii>; Fishery; World Wide Web; Computer science; Biology; Programming language","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001589117,0.001384271,0.001091509,0.008100402,0.002443927,0.003191233,0.003800365,0.001375126,0.3453804],"category_scores_gemma":[0.008362644,0.001064256,0.0008397668,0.01470918,0.0005913887,0.003333312,0.002709758,0.001187628,0.1750005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01602421,"about_ca_system_score_gemma":0.03220887,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.928302,"about_ca_topic_score_gemma":0.9240932,"domain_scores_codex":[0.9980072,0.00007584831,0.0002066058,0.0002636281,0.001169642,0.000277017],"domain_scores_gemma":[0.9860505,0.0006982824,0.0005629811,0.0008967762,0.01107762,0.0007139608],"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":[0.00004352072,0.000006814431,0.001527797,0.0003573673,0.000009150294,0.00001735717,0.00004130401,0.0001735632,0.0001274997,0.0007138174,0.9852471,0.0117346],"study_design_scores_gemma":[0.00003310563,0.000004821287,0.003974057,0.000249592,0.00001175892,0.00002655182,0.0001031808,0.0002239762,0.0003332742,0.0007908411,0.9942053,0.00004347736],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001738087,0.00009336954,0.0004284209,0.0001767342,0.00004493492,0.00005757144,0.9820403,0.001380526,0.01560432],"genre_scores_gemma":[0.002503916,0.0003260225,0.003455037,0.0003336978,0.00002836143,0.0002171832,0.969364,0.001845246,0.02192647],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3453804,"threshold_uncertainty_score":0.9337355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02647075862268946,"score_gpt":0.2214859327874473,"score_spread":0.1950151741647579,"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."}}