{"id":"W3100463562","doi":"","title":"Spatial or Temporal Pooling Solves Wild Goose Chase","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Goose; Pooling; Computer science; Geography; Artificial intelligence; Ecology; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.000232627,0.0001262524,0.0001316872,0.00006228169,0.0001088953,0.00003821484,0.0001290825,0.00005383178,0.0001323729],"category_scores_gemma":[0.00002942132,0.0001126567,0.00006942522,0.0002073785,0.00001915777,0.0001733144,0.000004371405,0.001114385,0.00003448079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000170709,"about_ca_system_score_gemma":0.0006000868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003337638,"about_ca_topic_score_gemma":0.00117814,"domain_scores_codex":[0.9985237,0.00001133734,0.0003043585,0.0001075411,0.0001477044,0.000905354],"domain_scores_gemma":[0.9997103,0.00001342977,0.00004328444,0.00007593318,0.00004574208,0.0001112989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001524777,0.0006781212,0.02905555,0.000420901,0.00288426,0.0002470155,0.01538055,0.1206653,0.04833792,0.3888909,0.005568619,0.3863461],"study_design_scores_gemma":[0.03170848,0.007150703,0.04238196,0.000455773,0.001194886,0.002884496,0.03316553,0.3068666,0.02269395,0.1668008,0.3771015,0.00759541],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6296729,0.0007339106,0.3645775,0.003461472,0.0003831034,0.0001843426,0.00001806462,0.0004393304,0.0005293886],"genre_scores_gemma":[0.9983341,0.000457164,0.0002901797,0.000314757,0.0004326347,0.000004717338,0.00002132104,0.00002897035,0.0001161839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3787507,"threshold_uncertainty_score":0.484151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01437708404004409,"score_gpt":0.2289596362334385,"score_spread":0.2145825521933944,"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."}}