{"id":"W4220936866","doi":"10.18280/ts.390125","title":"Emotional Analysis and Annotation of Tourism Landscape Images Based on Tourist Experience","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Digital Media and Visual Art","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Jilin Office of Philosophy and Social Science","keywords":"Tourism; Annotation; Computer science; Feature (linguistics); Sentiment analysis; Image (mathematics); The Internet; Set (abstract data type); Information retrieval; Artificial intelligence; Data mining; Data science; Geography; World Wide Web; Linguistics","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":[0.0001680931,0.00007951524,0.0001203068,0.0002196704,0.0001169906,0.00006711137,0.0002330463,0.000009269087,0.0003533796],"category_scores_gemma":[0.000009720131,0.00007479909,0.00006216352,0.0005162868,0.00003372572,0.0002357566,0.00008625,0.00005062018,0.00000190151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001544108,"about_ca_system_score_gemma":0.00002435493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008706437,"about_ca_topic_score_gemma":9.956324e-7,"domain_scores_codex":[0.9989394,0.00005354697,0.0001775132,0.0002304182,0.0004809226,0.000118229],"domain_scores_gemma":[0.9996073,0.00009126883,0.00007802329,0.0001257511,0.00003947542,0.00005813234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007042427,0.007562186,0.4689789,0.0002080081,0.0009747085,0.0003569034,0.02516427,0.1315203,0.03486244,0.04015945,0.0226034,0.2669052],"study_design_scores_gemma":[0.001238636,0.001507997,0.4054499,0.00001526327,0.00006516986,0.000004844966,0.0004437885,0.5789773,0.009766219,0.0007222396,0.001484157,0.0003244724],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8244676,0.00001868709,0.1731461,0.0005994393,0.00009499114,0.0001122621,0.00003654736,0.00004014657,0.001484205],"genre_scores_gemma":[0.9978601,7.481141e-7,0.001704934,0.0002790235,0.00002968319,0.00003449673,0.00002513475,0.000002782255,0.00006312332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.447457,"threshold_uncertainty_score":0.3869258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134081829639952,"score_gpt":0.2458504729890356,"score_spread":0.2345096546926361,"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."}}