{"meta":{"query_hash":"9e36ed382615","filters":{"venue":"Journal of Software Engineering Research and Development"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"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/9e36ed382615","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Software+Engineering+Research+and+Development"},"results":[{"id":"W3155762676","doi":"10.5753/jserd.2021.548","title":"Mining Experts from Source Code Analysis: An Empirical Evaluation","year":2021,"lang":"en","type":"article","venue":"Journal of Software Engineering Research and Development","topic":"Software Engineering Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Java; Source code; Quality (philosophy); Identification (biology); World Wide Web; Software; Software development; Microservices; Software quality; Code refactoring; Software engineering; Space (punctuation); Data science; Operating system; Cloud computing","score_opus":0.09872674669870646,"score_gpt":0.37527940236157586,"score_spread":0.2765526556628694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155762676","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92496556,0.006381978,0.056512326,0.00067561097,0.00014471066,0.0015874527,0.0034324036,0.0023086765,0.0039913543],"genre_scores_gemma":[0.9126058,0.0010740132,0.07429034,0.00020155952,0.0001238345,0.0007673595,0.0099531105,0.00018938216,0.00079457846],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.95455277,0.025949528,0.0041447706,0.004777524,0.009507877,0.0010675633],"domain_scores_gemma":[0.73813856,0.22127801,0.0087727355,0.011471129,0.017664883,0.002674754],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.042624943,0.0019545755,0.0018141208,0.010013041,0.0010736218,0.0024816333,0.0031338288,0.0028388465,0.0011434567],"category_scores_gemma":[0.14024417,0.0005703274,0.0013701656,0.004945789,0.001275811,0.0040617227,0.0027933072,0.0013221141,0.0007835295],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0057587163,0.005988089,0.40283647,0.004844,0.0017733442,0.00053403096,0.0024530096,0.03570002,0.006008957,0.0015084534,0.015252162,0.5173428],"study_design_scores_gemma":[0.001537199,0.004207343,0.21920973,0.0008479935,0.0014107268,0.0012807372,0.0025993602,0.7428896,0.012348544,0.0030988683,0.010405554,0.00016429402],"about_ca_topic_score_codex":0.0035857586,"about_ca_topic_score_gemma":0.0039029242,"teacher_disagreement_score":0.95737505,"about_ca_system_score_codex":0.0010393962,"about_ca_system_score_gemma":0.002110738,"threshold_uncertainty_score":0.22542489},"labels":[],"label_agreement":null}]}