{"id":"W4247277890","doi":"10.1111/gwmr.12180","title":"Table of Contents","year":2017,"lang":"en","type":"article","venue":"Groundwater Monitoring & Remediation","topic":"Microbial bioremediation and biosurfactants","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Table (database); Citation; Computer science; Information retrieval; Table of contents; World Wide Web; Database","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001916154,0.00008642871,0.0001054413,0.0000191325,0.0002044413,0.000075374,0.0002775083,0.00006190946,0.0003475344],"category_scores_gemma":[0.00008242032,0.00007451107,0.00003131563,0.00003892799,0.00009492879,0.0004613197,0.0001274792,0.00005586224,0.0003254333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006863256,"about_ca_system_score_gemma":0.000004277722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007849199,"about_ca_topic_score_gemma":0.00004399607,"domain_scores_codex":[0.9991503,0.00001824829,0.0002025428,0.0001728362,0.0002803014,0.0001757338],"domain_scores_gemma":[0.9993327,0.00001038688,0.0002480387,0.000328992,0.00002158766,0.00005827798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007672016,0.00002327736,0.5423395,0.000003341232,0.00000342161,6.063802e-7,0.0001151763,0.000004156676,0.453852,0.000003173209,0.0002783531,0.003369365],"study_design_scores_gemma":[0.0002286986,0.00002233105,0.5879294,0.000009609655,0.000004939536,4.171981e-7,0.00001673851,0.00001788401,0.4084688,0.0000315232,0.003203025,0.00006659015],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995966,0.000007244767,0.0001348926,0.0001573606,0.001366595,0.000115679,0.000003661919,0.00002229518,0.002226329],"genre_scores_gemma":[0.9981209,0.00002936306,0.0004751827,0.00001401311,0.000209401,0.000003068401,0.000007726892,0.000007849176,0.001132501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04558992,"threshold_uncertainty_score":0.4182895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02673332612031639,"score_gpt":0.251315756966424,"score_spread":0.2245824308461076,"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."}}