{"id":"W2973156027","doi":"10.48550/arxiv.1908.06868","title":"Comparing linear structure-based and data-driven latent spatial\\n representations for sequence prediction","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Publics; Library science; Political science; Humanities; Computer science; Philosophy; Law; Politics","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":[0.002413649,0.0006913089,0.000668428,0.001918567,0.0002874912,0.001253032,0.001291838,0.001281273,0.001879962],"category_scores_gemma":[0.007829449,0.000237534,0.0008972774,0.001895948,0.0005775589,0.003086467,0.001110521,0.001580032,0.0006510541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072456,"about_ca_system_score_gemma":0.001004991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006375367,"about_ca_topic_score_gemma":0.007989673,"domain_scores_codex":[0.9992644,0.0003356006,0.00005245888,0.000172328,0.00008593961,0.00008930376],"domain_scores_gemma":[0.9952059,0.003292967,0.0003638977,0.0006548403,0.0003074936,0.0001748751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008642187,0.0006567031,0.01827177,0.0002330757,0.0003467174,0.00009810374,0.0002352717,0.5881196,0.002640285,0.02481944,0.0086315,0.3550833],"study_design_scores_gemma":[0.00001199295,0.00004601635,0.000869329,0.00001213167,0.00001361118,0.000009017174,0.00002432699,0.9879541,0.0002877438,0.01047237,0.0002903174,0.000008891077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.408385,0.003404128,0.5759987,0.002713652,0.0002372933,0.0001254508,0.003546246,0.002119158,0.003470456],"genre_scores_gemma":[0.9257756,0.0009472349,0.06503733,0.0002821833,0.0001870826,0.0001139171,0.006008888,0.00009971194,0.001547873],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006375367,"threshold_uncertainty_score":0.01276475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2225676661500261,"score_gpt":0.2656496710594689,"score_spread":0.04308200490944278,"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."}}