{"id":"W6891643086","doi":"10.48448/dbkm-te76","title":"Weakly Supervised Pre-Training for Multi-Hop Retriever","year":2021,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Task (project management); Scalability; Process (computing); Labrador Retriever; Iterative and incremental development; Question answering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001743685,0.00150459,0.001359922,0.0007758522,0.0005161212,0.0007714932,0.002281544,0.001937632,0.008411976],"category_scores_gemma":[0.005329328,0.0005701382,0.001111373,0.0005903252,0.0008366721,0.002294054,0.001579711,0.002428876,0.008532733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006462845,"about_ca_system_score_gemma":0.0009443725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003390827,"about_ca_topic_score_gemma":0.005540301,"domain_scores_codex":[0.9989748,0.0003250765,0.00005934894,0.0003534843,0.0001898702,0.00009752938],"domain_scores_gemma":[0.9976239,0.001214272,0.0001081222,0.0005624047,0.0004125967,0.00007877296],"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":[0.0007283946,0.0005791953,0.001601279,0.0005092342,0.0001398332,0.000368226,0.0004071438,0.1003699,0.05803302,0.00383906,0.0254012,0.8080234],"study_design_scores_gemma":[0.00007124129,0.0003586597,0.0007398941,0.00002791345,0.00004953076,0.000247603,0.0001050733,0.9544402,0.03182728,0.005919926,0.006174543,0.00003817525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03301862,0.00100974,0.9427528,0.0003149064,0.0001023776,0.0002031987,0.0006076623,0.01874514,0.003245534],"genre_scores_gemma":[0.427329,0.0005423585,0.5376936,0.001216606,0.0002066044,0.0006720413,0.007646715,0.001447849,0.02324515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008411976,"threshold_uncertainty_score":0.02814084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1191606949599811,"score_gpt":0.3669893717318903,"score_spread":0.2478286767719092,"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."}}