{"id":"W6939856199","doi":"10.6084/m9.figshare.c.3882952.v1","title":"Applications of altmetrics for Canadian institutions","year":2017,"lang":"en","type":"other","venue":"Figshare","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Altmetrics; Social media; Citizen science; Field (mathematics)","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":["metaresearch","bibliometrics"],"domain":"evaluation","study_design":"not_applicable","genre":"other","about_ca_system":true,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":true,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00002324139,0.000163484,0.0002158087,0.001190202,0.0001856676,0.00003179676,0.0006695486,0.000327521,0.1990401],"category_scores_gemma":[0.001547534,0.0001845292,0.0001053913,0.0003254003,0.00001757012,0.00003260936,0.00003972183,0.0001023787,0.02727555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000193112,"about_ca_system_score_gemma":0.002057672,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04953623,"about_ca_topic_score_gemma":0.6418434,"domain_scores_codex":[0.999284,0.000005897233,0.0001089298,0.0002302016,0.0001291972,0.0002418056],"domain_scores_gemma":[0.998248,0.00002868891,0.0003950298,0.0009144227,0.000199419,0.0002144638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[2.695299e-7,0.00001065127,0.000001245582,0.0003526825,0.00003395964,6.067917e-7,0.000003356964,8.674583e-7,5.384711e-7,0.0003881711,0.9972072,0.002000435],"study_design_scores_gemma":[0.0001170784,0.000005517264,0.00002208132,0.001712242,0.00002877992,9.960619e-7,0.000001097657,0.000005578773,0.000009940076,0.00001781284,0.9979014,0.0001774897],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[9.770441e-10,0.0008905519,0.00001144715,0.000005936374,0.00001776498,0.0009512756,0.5191561,0.00006204507,0.4789049],"genre_scores_gemma":[0.00006870028,0.000002954746,0.000320741,0.00001796698,0.0002505028,0.002912675,0.554251,0.000418537,0.4417569],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5923072,"threshold_uncertainty_score":0.9734818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1615220311732184,"score_gpt":0.3609200532399597,"score_spread":0.1993980220667413,"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."}}