{"id":"W7145812816","doi":"","title":"コンバイン収穫と協調したロボットトラクタによる早期稲わら鋤き込み技術 : 労働時間削減効果と翌年産水稲の収量向上効果","year":2024,"lang":"ja","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Rice Cultivation and Yield Improvement","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hyperion Technologies (Canada)","funders":"","keywords":"Rice straw; Straw; Paddy field; Tractor; Economic shortage; Nitrogen; Rice plant","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":[0.0001096527,0.0002783612,0.0001482801,0.0001477097,0.0001532418,0.0001837679,0.0001750397,0.0001840803,0.0008449072],"category_scores_gemma":[0.00009147629,0.000109987,0.0002137773,0.0001028864,0.000245348,0.0002634093,0.0002555372,0.0001105634,0.0001905673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001581319,"about_ca_system_score_gemma":0.0002127548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001125333,"about_ca_topic_score_gemma":0.003336753,"domain_scores_codex":[0.999899,0.000008995004,0.000006213758,0.00003656486,0.00003223755,0.00001706763],"domain_scores_gemma":[0.999916,0.000007309274,0.00003152971,0.00001046322,0.00002055112,0.00001422102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005994955,0.00003342804,0.008055454,0.00009728693,0.00001300755,0.00009475238,0.00004457002,0.0007059099,0.9745745,0.00006756875,0.00004491641,0.01620874],"study_design_scores_gemma":[0.00002250866,0.002076526,0.2145356,0.00002341718,0.00008313885,0.0005983925,0.0004842615,0.01280776,0.7605845,0.0002441588,0.008492355,0.00004740806],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9807416,0.000209975,0.01769565,0.00002292335,0.00001154335,0.00003693287,0.00005388152,0.00008635801,0.001141033],"genre_scores_gemma":[0.982118,0.0001654427,0.01553738,0.0000262618,0.000004718924,0.0000284035,0.00009822152,0.000009127794,0.002012419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001125333,"threshold_uncertainty_score":0.002826452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02817249380220103,"score_gpt":0.2608919817236643,"score_spread":0.2327194879214633,"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."}}