{"id":"W4389092941","doi":"10.53555/sfs.v10i1.1823","title":"Artificial Intelligence, Robotics And Its Applications In Green Libraries","year":2023,"lang":"en","type":"article","venue":"Journal of Survey in Fisheries Sciences","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Robotics; Robot; Artificial intelligence; Information and Communications Technology; Field (mathematics); Service (business); Computer science; Digital library; Big data; Information technology; Data science; Engineering management; Knowledge management; Engineering ethics; Engineering; World Wide Web; Business; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002082638,0.00008911455,0.0001816703,0.0005813492,0.0001724352,0.0003861455,0.001065927,0.00004548797,0.00001026461],"category_scores_gemma":[0.0003669573,0.00007484492,0.00002692895,0.003576274,0.0002307831,0.002530773,0.0002584634,0.0002219145,0.0000110835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002794541,"about_ca_system_score_gemma":0.0001603678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003202539,"about_ca_topic_score_gemma":0.00403427,"domain_scores_codex":[0.9985169,0.0001687504,0.0005686863,0.000188919,0.0003328423,0.0002238648],"domain_scores_gemma":[0.9981729,0.001270857,0.0002261853,0.0001288387,0.0001370685,0.00006414614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000141097,0.0001014813,0.9166156,0.0000228293,0.000008510062,0.00003360887,0.004521931,0.00930139,0.00009741094,0.0526441,0.0003436122,0.0162954],"study_design_scores_gemma":[0.00005396543,0.0002373534,0.8337397,0.00008603148,0.000002996324,0.0000565441,0.003169419,0.04154333,0.0007121323,0.1189236,0.001214641,0.0002602904],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9233815,0.0004486215,0.05438618,0.01869494,0.001376416,0.0002854438,0.00001191179,0.00008371771,0.001331276],"genre_scores_gemma":[0.9907026,0.0001727397,0.008914509,0.000081659,0.00005517703,0.000006219462,9.188687e-7,0.000004211195,0.0000619507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08287589,"threshold_uncertainty_score":0.372361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2520796812274846,"score_gpt":0.3220725048062436,"score_spread":0.06999282357875891,"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."}}