{"id":"W6902105260","doi":"10.6084/m9.figshare.13642706.v1","title":"Analyzing Canadian ecological restoration literature with bibliometric analysis and a systematic map. Presented at ESMARConf2021","year":2021,"lang":"en","type":"other","venue":"Figshare","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scope (computer science); Systematic review; Selection (genetic algorithm); Bibliometrics; Presentation (obstetrics); Process (computing)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"other","about_ca_system":false,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"other","about_ca_system":false,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","bibliometrics","insufficient_payload"],"consensus_categories":["bibliometrics","insufficient_payload"],"category_scores_codex":[0.0001765162,0.0006237677,0.001364193,0.1741807,0.0001791287,0.0009005248,0.0003612463,0.0008859546,0.3166523],"category_scores_gemma":[0.002142797,0.0004929177,0.0002430387,0.2585793,0.00001857447,0.0001239859,0.0002006504,0.0004817516,0.002617153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007681209,"about_ca_system_score_gemma":0.0004243148,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0118104,"about_ca_topic_score_gemma":0.674634,"domain_scores_codex":[0.9966388,0.0006041379,0.0004464093,0.001041957,0.0006506363,0.0006180683],"domain_scores_gemma":[0.9969692,0.0002036286,0.0007287093,0.0009499436,0.0004995023,0.0006490231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000009641585,0.00003075703,0.003259678,0.03981782,0.003155865,0.001700742,0.00006171664,0.00001983381,0.000008171783,0.00000142851,0.9519267,0.000007610214],"study_design_scores_gemma":[0.001497868,0.000235828,0.1414639,0.4447627,0.009234161,0.0004178636,0.0001351004,0.003185757,0.00002484977,0.000002462518,0.3957317,0.003307736],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002929067,0.1433652,0.000002151669,0.00008803524,0.00006456667,0.002981987,0.8021346,0.0004169638,0.05065354],"genre_scores_gemma":[0.01277333,0.00003503905,0.0001542239,0.00003976036,0.000185727,0.0006656007,0.7005771,0.000648021,0.2849211],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6628236,"threshold_uncertainty_score":0.9997522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03169186853094417,"score_gpt":0.2716390840561715,"score_spread":0.2399472155252274,"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."}}