{"id":"W2616591008","doi":"10.48550/arxiv.1705.05278","title":"Unimodal probability distributions for deep ordinal classification","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Ordinal data; Probability distribution; Binomial (polynomial); Poisson distribution; Binomial distribution; Mathematics; Ordinal regression; Entropy (arrow of time); Context (archaeology); Poisson binomial distribution; Statistics; Ordinal optimization; Principle of maximum entropy; Negative binomial distribution; Artificial intelligence; Computer science; Pattern recognition (psychology); Beta-binomial distribution; Geography","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":[0.002979212,0.0006258439,0.0008415579,0.001547661,0.0005047827,0.00166976,0.001369846,0.00117055,0.003178969],"category_scores_gemma":[0.01407383,0.0004380043,0.0005741759,0.00125315,0.001753278,0.003748509,0.001914954,0.003083629,0.00054148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001762345,"about_ca_system_score_gemma":0.0008016275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001341916,"about_ca_topic_score_gemma":0.001452068,"domain_scores_codex":[0.9988101,0.0005063018,0.00005708049,0.0002085392,0.0003195585,0.0000985001],"domain_scores_gemma":[0.9934575,0.004801957,0.0005912961,0.000542955,0.0003689547,0.000237431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001803369,0.0001511488,0.004252418,0.0002069953,0.00008009947,0.0001844431,0.0001887246,0.5032495,0.003869333,0.3537463,0.004791637,0.1290991],"study_design_scores_gemma":[0.000005696025,0.00001284791,0.0004738131,0.00001496181,0.000003833584,0.00002639969,0.00001147912,0.8173663,0.0006493499,0.1808845,0.000542026,0.000008904394],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03360406,0.0005621322,0.9623676,0.0006939898,0.00002814266,0.00002818074,0.0002579145,0.0004725734,0.001985418],"genre_scores_gemma":[0.857582,0.0006956994,0.1368838,0.000423559,0.0001789407,0.0002118242,0.0008684635,0.0001834349,0.002972306],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003178969,"threshold_uncertainty_score":0.01575583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2068615062896483,"score_gpt":0.2480125443743021,"score_spread":0.04115103808465384,"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."}}