{"id":"W6904427737","doi":"10.1371/journal.pone.0245533.s001","title":"S1 Appendix -","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Task (project management); Set (abstract data type); Software; Control (management); Gender gap; Social media; Data set; News media","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004211938,0.001219477,0.001158844,0.005768497,0.002281758,0.003025572,0.002077825,0.0017882,0.852396],"category_scores_gemma":[0.08265799,0.001053408,0.0008518513,0.006800368,0.0006753404,0.002906111,0.002108599,0.001936159,0.4291653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001834213,"about_ca_system_score_gemma":0.0042359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0109271,"about_ca_topic_score_gemma":0.01566273,"domain_scores_codex":[0.9967042,0.000863926,0.0006888528,0.0005996609,0.0009426071,0.0002007597],"domain_scores_gemma":[0.9323211,0.03973069,0.00296197,0.006316346,0.01715791,0.001512055],"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":[0.00009347656,0.00007219541,0.001681518,0.0008213094,0.00001433024,0.00004420239,0.0001612063,0.0001644504,0.0001009328,0.001334203,0.9807114,0.0148008],"study_design_scores_gemma":[0.0003165212,0.00009932331,0.01071354,0.001481403,0.00003825419,0.0002620944,0.0008290824,0.0005311032,0.0003512076,0.009558998,0.9757471,0.00007142966],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.001010799,0.0001595581,0.004110296,0.000759025,0.0006518161,0.001559909,0.9631892,0.00123971,0.0273197],"genre_scores_gemma":[0.01282226,0.0007522356,0.02505929,0.003568423,0.0004981573,0.01768264,0.8480183,0.003010433,0.08858828],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.147604,"threshold_uncertainty_score":0.2105391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1066756793904044,"score_gpt":0.4050596373774522,"score_spread":0.2983839579870478,"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."}}