{"id":"W4252428567","doi":"10.31227/osf.io/t7pds","title":"PENGARUH GEOMETRI JARINGAN TERHADAP KETELITIAN SURVEY GPS","year":2018,"lang":"id","type":"preprint","venue":"","topic":"Computer Science and Engineering","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.004749659,0.001155025,0.001104312,0.001152168,0.0005780926,0.003319722,0.008501405,0.0006100423,0.0004837535],"category_scores_gemma":[0.0005295099,0.00115135,0.0004798901,0.002762814,0.0003127937,0.001149254,0.01126541,0.001525631,0.001674551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003179506,"about_ca_system_score_gemma":0.0006496312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002538656,"about_ca_topic_score_gemma":0.0007580881,"domain_scores_codex":[0.9918185,0.0004019321,0.001242197,0.003061103,0.00149404,0.001982201],"domain_scores_gemma":[0.9931504,0.0008379846,0.0003581332,0.004020021,0.0007136021,0.0009198817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000121331,0.002292018,0.08767419,0.002700658,0.001805029,0.001182933,0.02599251,0.04815595,0.001542525,0.0293336,0.1566679,0.6425313],"study_design_scores_gemma":[0.0005051354,0.0004607124,0.2695609,0.000460748,0.00003178512,0.0001275225,0.00004956026,0.6808963,0.00194019,0.0008248221,0.04253342,0.002608895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04911046,0.0003270232,0.9171829,0.0006175499,0.01301556,0.0005620307,0.00001388102,0.0008525442,0.01831804],"genre_scores_gemma":[0.8972647,0.0002762282,0.08578561,0.000988322,0.001949834,0.00004462185,0.00007413361,0.000108514,0.01350808],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8481542,"threshold_uncertainty_score":0.9991028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0483431765028733,"score_gpt":0.257386697317797,"score_spread":0.2090435208149237,"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."}}