{"id":"W31709637","doi":"10.1111/cdoe.12506","title":"農地土壌の放射性物質除去技術(除染技術)について (東日本大震災における農業被害の実態と研究課題に関する研究会)","year":2011,"lang":"en","type":"article","venue":"Medical Entomology and Zoology","topic":"Dental Health and Care Utilization","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001672243,0.000227841,0.0002453564,0.001277047,0.001817613,0.001744157,0.0004090279,0.0003407461,0.01240476],"category_scores_gemma":[0.002225246,0.0002486524,0.0004591634,0.001096404,0.00204939,0.0006912295,0.0006334516,0.0005363086,0.002394332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004660336,"about_ca_system_score_gemma":0.00538607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3626388,"about_ca_topic_score_gemma":0.4627365,"domain_scores_codex":[0.9988432,0.0001075411,0.00006348415,0.0001242597,0.0006344786,0.0002269729],"domain_scores_gemma":[0.9981066,0.0002110651,0.0003754527,0.00009329675,0.0009928796,0.0002206579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005103312,0.000226206,0.3693643,0.001314924,0.0003613834,0.001217855,0.00545418,0.0008139917,0.033399,0.02562245,0.02001464,0.5417008],"study_design_scores_gemma":[0.00003131837,0.0002333212,0.8906227,0.0003409316,0.0001517518,0.001627993,0.005412248,0.0006928715,0.01407083,0.007625317,0.07909067,0.0001000362],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6921762,0.01550957,0.01118593,0.00582754,0.000315284,0.0003689415,0.003922217,0.0003292981,0.2703651],"genre_scores_gemma":[0.9585003,0.00603248,0.007502272,0.0004794682,0.0001308067,0.00006274139,0.001140219,0.00003237421,0.02611935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3626388,"threshold_uncertainty_score":0.721056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02537792603207894,"score_gpt":0.3070061356473815,"score_spread":0.2816282096153025,"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."}}