{"id":"W3034467536","doi":"10.1088/2515-7620/ab9c33","title":"Pan-Arctic analysis of cultural ecosystem services using social media and automated content analysis","year":2020,"lang":"en","type":"article","venue":"Environmental Research Communications","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Norges Forskningsråd","keywords":"Arctic; Wildlife; Content analysis; Geography; Natural (archaeology); Social media; The arctic; Ecology; World Wide Web; Computer science; Sociology; Archaeology; Social science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005935204,0.0001369276,0.0004130345,0.0001936911,0.0005933478,0.00006990875,0.0009068308,0.00007444411,0.0008442273],"category_scores_gemma":[0.00002867416,0.0001150434,0.0001809086,0.002030184,0.000212978,0.0003159809,0.001488085,0.0002075951,0.00009478413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002377855,"about_ca_system_score_gemma":0.000006614869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002020196,"about_ca_topic_score_gemma":0.009797035,"domain_scores_codex":[0.997708,0.0005613406,0.0004187317,0.0003228809,0.0006850801,0.0003039855],"domain_scores_gemma":[0.9986483,0.000337622,0.0001661073,0.0006394321,0.00001518613,0.000193313],"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":[0.00003739658,0.0003000226,0.9487843,0.00009855038,0.003799776,0.000003095036,0.02040685,0.004157196,0.02160358,0.0000468265,0.00003107941,0.0007313337],"study_design_scores_gemma":[0.0001686913,0.0000223043,0.4912171,0.000009315563,0.001238806,6.027036e-7,0.007214821,0.4996446,0.0001409433,0.00001064214,0.0002193973,0.0001128153],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981533,0.0003756201,0.0000144046,0.0007419148,0.000007673358,0.0001982974,0.0002323075,0.0000445836,0.0002318531],"genre_scores_gemma":[0.998693,0.0004528413,0.0004377743,0.00005492535,0.00001407964,0.0000221912,0.0003104889,0.00001050833,0.000004192093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4954874,"threshold_uncertainty_score":0.9243697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.149313936722464,"score_gpt":0.3556852919790355,"score_spread":0.2063713552565715,"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."}}