{"id":"W2965654035","doi":"10.7490/f1000research.1115921.1","title":"Unraveling the neural correlates of dream recall: Novel insights from deep convolutional nets","year":2018,"lang":"en","type":"article","venue":"Faculty of 1000 Research Ltd","topic":"Sleep and Wakefulness Research","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Open peer review; Plant biology; Recall; Neuroscience; Convolutional neural network; Dream; Cognitive science; Physiology; Artificial intelligence; Biology; Psychology; Cognitive psychology; Computer science; Botany","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":[0.000335998,0.0003057212,0.000193931,0.0003102021,0.0001169,0.0006731222,0.0003637423,0.0003216116,0.001487678],"category_scores_gemma":[0.002086272,0.000236969,0.0001775671,0.0003785262,0.0002408343,0.0007162297,0.0004592231,0.0007428256,0.0001832149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002017611,"about_ca_system_score_gemma":0.0002674071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002428779,"about_ca_topic_score_gemma":0.005762467,"domain_scores_codex":[0.9999347,0.00001568617,0.000003968952,0.00001714734,0.0000117171,0.00001692363],"domain_scores_gemma":[0.9995657,0.000219462,0.00008939827,0.0000426334,0.00004527978,0.00003754775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001855143,0.0004547963,0.2905399,0.0004787567,0.0007213749,0.0005861169,0.001009625,0.0557351,0.1316865,0.02319239,0.006035311,0.487705],"study_design_scores_gemma":[0.00005402082,0.0002842166,0.5511605,0.0001355065,0.0002160057,0.0006576974,0.0004722379,0.3790794,0.0163057,0.04726772,0.004295183,0.00007188912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358222,0.001822929,0.0550915,0.001039734,0.00007179371,0.00002243697,0.001147568,0.0001191307,0.004862661],"genre_scores_gemma":[0.9946719,0.0003609022,0.003259383,0.00004767187,0.00002635557,0.000006320465,0.0003151103,0.00001501114,0.001297296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002428779,"threshold_uncertainty_score":0.004976749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1029838847166876,"score_gpt":0.3730131932864818,"score_spread":0.2700293085697942,"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."}}