{"meta":{"query_hash":"9f861f8e8423","filters":{"venue":"Japanese Journal of JSCE"},"cohort_total":2,"direct_labels_cover":0,"predictions_cover":2,"exported":2,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/9f861f8e8423","api":"https://metacan.xera.ac/api/v1/cohort?venue=Japanese+Journal+of+JSCE"},"results":[{"id":"W4391666334","doi":"10.2208/jscejj.23-23190","title":"APPLICABILITY OF CROSS-SECTORAL MANAGEMENT OF INFRASTRUCTURES IN MUNICIPALITIES","year":2023,"lang":"en","type":"article","venue":"Japanese Journal of JSCE","topic":"Public Procurement and Policy","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Business; Environmental planning; Environmental science","score_opus":0.03347908709481606,"score_gpt":0.3155261460397654,"score_spread":0.28204705894494936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391666334","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94116706,0.00034501654,0.0017575341,0.0012809687,0.00001992128,0.00009178921,0.00026583427,0.00003975665,0.05503205],"genre_scores_gemma":[0.998747,0.000055288536,0.00021162574,0.000019652898,0.0000040019113,0.00001597972,0.00003781719,0.0000018347824,0.00090673065],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99533325,0.0019601402,0.00033957174,0.00049448945,0.0008028336,0.0010697196],"domain_scores_gemma":[0.98240364,0.006738124,0.0036224248,0.0016323959,0.0040592453,0.0015443139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041686804,0.00023556486,0.00036652212,0.0027169485,0.0016720814,0.0044356873,0.000995744,0.0009157987,0.008464736],"category_scores_gemma":[0.024454603,0.00028400004,0.0006235975,0.0047954447,0.0016508383,0.004329999,0.004646885,0.0005801047,0.00033891515],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044506727,0.00043278822,0.76084423,0.0004597669,0.00036403886,0.0014613445,0.013573787,0.022274567,0.0011326931,0.12330188,0.002933861,0.07277606],"study_design_scores_gemma":[0.000047998445,0.0004452089,0.8363873,0.0004090199,0.00028915078,0.00033956967,0.08589216,0.02551378,0.0014894515,0.029150771,0.019975288,0.00006032302],"about_ca_topic_score_codex":0.071287826,"about_ca_topic_score_gemma":0.068298295,"teacher_disagreement_score":0.071287826,"about_ca_system_score_codex":0.010448862,"about_ca_system_score_gemma":0.007206151,"threshold_uncertainty_score":0.14174575},"labels":[],"label_agreement":null},{"id":"W4391666339","doi":"10.2208/jscejj.23-23187","title":"COMPARATIVE ANALYSIS OF MARKOV CHAIN MODEL ESTIMATION METHODS BASED ON VISUAL INSPECTION DATA","year":2023,"lang":"en","type":"article","venue":"Japanese Journal of JSCE","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Markov chain; Computer science; Visual inspection; Markov chain Monte Carlo; Markov model; Artificial intelligence; Pattern recognition (psychology); Machine learning; Bayesian probability","score_opus":0.3236059678460392,"score_gpt":0.5795618829135618,"score_spread":0.25595591506752263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391666339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20927413,0.002762265,0.7824228,0.00040764044,0.0001331253,0.00016948831,0.00027540646,0.0013161788,0.0032389162],"genre_scores_gemma":[0.81804466,0.0009952326,0.17843887,0.00008246728,0.000050805942,0.00022974372,0.0008901533,0.0002854066,0.0009825483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99277085,0.0051414412,0.00032239288,0.00061318674,0.00091660593,0.00023551616],"domain_scores_gemma":[0.7806779,0.20545612,0.0028486305,0.002997401,0.007471043,0.0005489973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020521998,0.0009949205,0.0014461537,0.0036412457,0.00073487463,0.0016644639,0.0014137285,0.0015504829,0.0024490347],"category_scores_gemma":[0.093510725,0.0004768173,0.0015360344,0.0020315652,0.000686736,0.0032601897,0.00097373716,0.001409116,0.00034892393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027332976,0.00041209045,0.02712706,0.0009608961,0.00090031105,0.00021760503,0.00091113435,0.61559176,0.002468281,0.021316044,0.0024443893,0.32491714],"study_design_scores_gemma":[0.000041080497,0.00013395106,0.0046583167,0.000059456746,0.00010753113,0.00006225662,0.00015940201,0.98939973,0.00088739325,0.0041184253,0.00032960586,0.000042859574],"about_ca_topic_score_codex":0.01255953,"about_ca_topic_score_gemma":0.006910969,"teacher_disagreement_score":0.020521998,"about_ca_system_score_codex":0.001629197,"about_ca_system_score_gemma":0.0022243783,"threshold_uncertainty_score":0.10853201},"labels":[],"label_agreement":null}]}