{"id":"W4412571954","doi":"10.3390/electronics14152922","title":"Towards Reliable and Efficient Natural Language Processing in Emergent Technologies","year":2025,"lang":"en","type":"article","venue":"Electronics","topic":"Robotics and Automated Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Computer science; Natural (archaeology); Natural language processing; Artificial intelligence; Geography; Archaeology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003575395,0.0004868936,0.0007658219,0.0009619697,0.0007397911,0.003357348,0.001409905,0.001635575,0.002700243],"category_scores_gemma":[0.01302761,0.0008028116,0.0007637073,0.000651403,0.003988081,0.007653451,0.003106857,0.00268985,0.001671329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001315516,"about_ca_system_score_gemma":0.001391463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009799359,"about_ca_topic_score_gemma":0.0009797781,"domain_scores_codex":[0.9977634,0.001101662,0.000147233,0.0002266088,0.0005977186,0.0001633343],"domain_scores_gemma":[0.9914078,0.00474247,0.0004472012,0.002399575,0.0008323634,0.0001705195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008765617,0.0000846827,0.000870006,0.0004010443,0.00004954567,0.0002796765,0.001265803,0.04726576,0.0266402,0.8461733,0.004785125,0.0720972],"study_design_scores_gemma":[0.00002803736,0.00003959875,0.0002136136,0.00004789576,0.00001391464,0.00009409651,0.0002327175,0.2168413,0.0133453,0.7503682,0.01874895,0.0000262685],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02029181,0.0003991293,0.970497,0.002289599,0.00005796947,0.00007849061,0.0001005152,0.001586716,0.004698785],"genre_scores_gemma":[0.2894351,0.0008167994,0.70453,0.000533213,0.0001326159,0.0002991515,0.0002379569,0.0005582652,0.003456885],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003575395,"threshold_uncertainty_score":0.01890874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00272277712211613,"score_gpt":0.2164019137102039,"score_spread":0.2136791365880878,"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."}}