{"id":"W6969581660","doi":"10.5683/sp/wkolt9","title":"KielstraArnottGunn_EcolApp","year":2017,"lang":"en","type":"dataset","venue":"Borealis","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Abundance (ecology); Ecosystem; Aquatic ecosystem; Period (music); Scale (ratio)","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":["metaresearch","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.006186754,0.0003017956,0.0005170566,0.0005231702,0.0006220713,0.003446126,0.00742563,0.0002212652,0.001252967],"category_scores_gemma":[0.00931041,0.0002245665,0.0002453436,0.0002469589,0.0002010237,0.0002743006,0.001774391,0.0003152486,0.003484462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004281034,"about_ca_system_score_gemma":0.0001436275,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01794219,"about_ca_topic_score_gemma":0.01994368,"domain_scores_codex":[0.9946782,0.0001539327,0.0007936745,0.001420742,0.002509134,0.000444318],"domain_scores_gemma":[0.9888269,0.0006160385,0.0008930278,0.009191857,0.0002606701,0.0002115639],"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":[0.000003910196,0.00003657593,0.00001354447,0.000007139743,0.0000183742,0.00005573622,0.00000752604,0.000005197279,7.132044e-8,0.00003555865,0.9784707,0.02134561],"study_design_scores_gemma":[0.0001220836,0.0000208601,0.001504531,0.00002940975,0.00003567488,0.000005986351,0.00002485814,0.00005765341,7.002796e-7,0.002708134,0.9952216,0.0002685136],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000002256172,0.0000539206,0.0001253612,0.0005409998,0.002570474,0.0001969025,0.9840813,0.00005050849,0.01237829],"genre_scores_gemma":[0.00000993067,0.00002759855,0.0001210774,0.0003027035,0.0005763222,0.00001307179,0.9904854,0.00001000353,0.008453922],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02107709,"threshold_uncertainty_score":0.99966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1609720505469295,"score_gpt":0.427563064521379,"score_spread":0.2665910139744496,"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."}}