{"id":"W7128790004","doi":"10.15468/9qrhqj","title":"ACER: Marine Resource Inventory of the Seaside Adjunct, Kejimkujik National Park","year":2017,"lang":"en","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"National park; Resource (disambiguation); Estuary; Distribution (mathematics); Marine conservation; Ecosystem; Coastal zone","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007446425,0.001386147,0.001492309,0.004190637,0.0008147854,0.001974006,0.002525373,0.001083616,0.03794668],"category_scores_gemma":[0.003869167,0.0007760016,0.0007437514,0.0114866,0.0003280137,0.001322094,0.001551465,0.001461955,0.03763111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002065163,"about_ca_system_score_gemma":0.00423253,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1224802,"about_ca_topic_score_gemma":0.200254,"domain_scores_codex":[0.9992622,0.00005902773,0.0001214015,0.0002251026,0.0001830628,0.0001492116],"domain_scores_gemma":[0.9979526,0.0003623095,0.0003586679,0.0003361649,0.0007224326,0.0002677787],"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":[0.00007429484,0.00002271507,0.005106818,0.0007800253,0.00005002707,0.00004983521,0.00005175123,0.0003044157,0.0001014182,0.0004845129,0.9904653,0.002508973],"study_design_scores_gemma":[0.0002030644,0.00001254039,0.03679072,0.0006127569,0.00008044895,0.00009390638,0.0003145025,0.0005258716,0.0003961281,0.0007741476,0.9601376,0.00005834256],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001352776,0.00001860702,0.0000160644,0.00001101227,0.000004301305,0.000002838624,0.9995558,0.00003017624,0.000225905],"genre_scores_gemma":[0.0003552894,0.00002394508,0.00008707409,0.000006277489,0.00000157062,0.00002821302,0.9992755,0.00001459095,0.0002075624],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8775198,"threshold_uncertainty_score":0.2435345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06567477013572134,"score_gpt":0.3465982823060048,"score_spread":0.2809235121702834,"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."}}