{"id":"W4415719891","doi":"10.18280/ts.420542","title":"Real-Time Monitoring and Assessment of Natural Resources in Tourist Attractions Using Computer Vision","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Recreation, Leisure, Wilderness Management","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tourism; Natural resource; Natural (archaeology); Machine vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001961903,0.0004732537,0.0004460294,0.001763099,0.0003237132,0.0006149231,0.0005824832,0.0005114785,0.0005273724],"category_scores_gemma":[0.0003296593,0.0001950899,0.0003765307,0.001169326,0.0002290564,0.0006796184,0.0005718967,0.0003691335,0.0002420574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003628159,"about_ca_system_score_gemma":0.0004406913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007954105,"about_ca_topic_score_gemma":0.01433249,"domain_scores_codex":[0.9997861,0.00002077969,0.000007433459,0.00006916681,0.00008063525,0.00003586794],"domain_scores_gemma":[0.9998573,0.00002605499,0.00002402226,0.00001158361,0.00005942296,0.00002142804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003518079,0.0002058569,0.03947346,0.0005941695,0.0001620192,0.0007150523,0.0006312286,0.0762893,0.2149101,0.001744585,0.006208145,0.6587143],"study_design_scores_gemma":[0.00002121369,0.0001633614,0.09176268,0.00005630075,0.0000949735,0.0004863478,0.000717086,0.8615894,0.03828238,0.002012042,0.004729465,0.00008479621],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4643993,0.001518741,0.5233074,0.0002316018,0.00008121337,0.0002068926,0.0009230838,0.002246813,0.007084945],"genre_scores_gemma":[0.7977836,0.0006578074,0.1992811,0.00007770822,0.00003910155,0.00009634877,0.0005552847,0.00005432675,0.001454785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007954105,"threshold_uncertainty_score":0.01581556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02292361532802658,"score_gpt":0.3618674270453143,"score_spread":0.3389438117172878,"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."}}