{"id":"W6941842458","doi":"10.1371/journal.pone.0173465.t003","title":"Details of backward trajectories from London (Ontario).","year":2017,"lang":"en","type":"dataset","venue":"Figshare","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Trajectory; Field (mathematics); Tracking (education); Sequence (biology); Perspective (graphical)","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":[0.0006425018,0.002015495,0.001442079,0.003448846,0.001306986,0.002907541,0.002569272,0.001600217,0.1269461],"category_scores_gemma":[0.007692808,0.0007454545,0.001394368,0.00935917,0.000526152,0.001160352,0.001883829,0.001554406,0.1010882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004388374,"about_ca_system_score_gemma":0.009799586,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5064582,"about_ca_topic_score_gemma":0.749054,"domain_scores_codex":[0.9991424,0.00009753811,0.00009658673,0.000313055,0.000186206,0.000164259],"domain_scores_gemma":[0.997259,0.0007331458,0.0002413133,0.0006029574,0.0009192608,0.0002442556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005331251,0.000008179216,0.002080662,0.0007878875,0.00004605517,0.00002817461,0.00004564625,0.0004683499,0.00005173275,0.0006125226,0.9938495,0.001968065],"study_design_scores_gemma":[0.0001603958,0.000007543211,0.006715172,0.0005510467,0.00004860123,0.00004026011,0.000153049,0.0004425114,0.0001673655,0.0008827482,0.9908046,0.00002678183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001085688,0.00009911564,0.00003664447,0.00003788258,0.00001742528,0.000004606249,0.998696,0.0001174298,0.0008822929],"genre_scores_gemma":[0.0007631814,0.0001180606,0.0001770981,0.0000264271,0.000004606144,0.00003441747,0.9972942,0.00009555806,0.001486337],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4935418,"threshold_uncertainty_score":0.9928968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03492330407225394,"score_gpt":0.2410835422881339,"score_spread":0.2061602382158799,"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."}}