{"id":"W6927271081","doi":"10.25919/n2v8-e946","title":"Parkes observations for project P885 semester 2020OCTS_14","year":2020,"lang":"en","type":"dataset","venue":"CSIRO","topic":"Botanical Research and Applications","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Magnetar; Swift; Flux (metallurgy); Electromagnetic spectrum; Active listening","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006691311,0.001507319,0.001019553,0.002121319,0.0007748337,0.002094321,0.00188658,0.001288419,0.07649599],"category_scores_gemma":[0.002353671,0.0004957405,0.0009166201,0.00339012,0.0002523226,0.001369779,0.001812207,0.001202916,0.1116885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011499,"about_ca_system_score_gemma":0.001604354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03866734,"about_ca_topic_score_gemma":0.06429841,"domain_scores_codex":[0.9994765,0.00006453744,0.00004536482,0.0001683705,0.0001231369,0.0001221381],"domain_scores_gemma":[0.9991007,0.00009516806,0.0001193085,0.0002284745,0.0003021139,0.0001541572],"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":[0.00006273911,0.00001489434,0.00110034,0.0002233786,0.00002283992,0.00001727108,0.00001111011,0.0001448901,0.00009563188,0.0003132743,0.9957041,0.002289508],"study_design_scores_gemma":[0.0001474807,0.00001231081,0.00605755,0.0001050718,0.00001766732,0.00002314324,0.00008218313,0.0003086972,0.0002205335,0.0007491001,0.9922599,0.00001630116],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001648721,0.00004125055,0.00003282088,0.00005828149,0.00003340068,0.000006733916,0.9985859,0.0001987312,0.0008779718],"genre_scores_gemma":[0.000385584,0.00003167305,0.0001595127,0.00003308726,0.00001190576,0.0000248517,0.9980615,0.00005861628,0.001233275],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07649599,"threshold_uncertainty_score":0.2559046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1850064548709295,"score_gpt":0.3342886484750517,"score_spread":0.1492821936041222,"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."}}