{"id":"W3213496413","doi":"10.5281/zenodo.3558710","title":"Lynx D2.5 Report on Lynx acquired vocabularies","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canarie","funders":"European Commission","keywords":"Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02293715,0.001334989,0.001147251,0.008742545,0.002753411,0.008081397,0.00323973,0.001448313,0.08740374],"category_scores_gemma":[0.03785869,0.001347188,0.001115711,0.004930007,0.001183547,0.008625165,0.0114874,0.002761045,0.07566892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006684544,"about_ca_system_score_gemma":0.01866321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08209824,"about_ca_topic_score_gemma":0.04353276,"domain_scores_codex":[0.9846751,0.003017532,0.001941841,0.001536585,0.007930624,0.0008982393],"domain_scores_gemma":[0.9746695,0.004586242,0.001210163,0.003547977,0.01464281,0.001343425],"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":[0.0004090566,0.0002288112,0.00536367,0.002391102,0.0000708734,0.0004144913,0.006924008,0.002511247,0.00910333,0.06382819,0.6599404,0.2488148],"study_design_scores_gemma":[0.00003475017,0.00005600683,0.003033486,0.0005400879,0.00001591396,0.00007965529,0.001135581,0.0005963967,0.002423832,0.002618956,0.9894091,0.00005620775],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.03181882,0.003186241,0.07137033,0.005051638,0.001347522,0.005384342,0.5092648,0.01565772,0.3569186],"genre_scores_gemma":[0.02122839,0.001572454,0.06894659,0.0006057788,0.0001396122,0.004945172,0.7811137,0.007615569,0.1138327],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.08740374,"threshold_uncertainty_score":0.2923946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02064328875088597,"score_gpt":0.2534793829124178,"score_spread":0.2328360941615318,"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."}}