{"id":"W4392268123","doi":"","title":"Analyser la disponibilité de l'Information géographique numérique dans les enjeux de suivi et de gestion du trait de côte : application aux cas breton (France) et québécois (Canada)","year":2021,"lang":"fr","type":"preprint","venue":"theses.fr (ABES)","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geography; Cartography; Geographic information system; Environmental resource management; Environmental planning; Environmental science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.008899889,0.0003699167,0.0007814874,0.005679785,0.003070742,0.01026461,0.0011691,0.001113582,0.004675539],"category_scores_gemma":[0.03372633,0.0003513979,0.0004931763,0.008723058,0.003549383,0.003922373,0.001777962,0.0009199949,0.0006196004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02788453,"about_ca_system_score_gemma":0.01877053,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8819248,"about_ca_topic_score_gemma":0.8888803,"domain_scores_codex":[0.9954202,0.001536978,0.0001800834,0.0004776891,0.001953113,0.0004319172],"domain_scores_gemma":[0.9678506,0.02199027,0.001752074,0.0007828139,0.006970454,0.0006537351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004583324,0.0002037416,0.5146037,0.001104665,0.000168173,0.001086426,0.2296714,0.004008817,0.003299142,0.02832811,0.007084463,0.209983],"study_design_scores_gemma":[0.00003553128,0.0001391788,0.7610872,0.000642336,0.0001230287,0.0004870058,0.1454512,0.01502704,0.001708347,0.001965181,0.07321461,0.0001193642],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.966391,0.001098877,0.003375866,0.001963475,0.00002156653,0.00007798029,0.0007896326,0.0001259335,0.02615573],"genre_scores_gemma":[0.9893917,0.0006349509,0.003583113,0.00006015597,0.000007984491,0.00004264582,0.0005177889,0.00004077972,0.00572081],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1180752,"threshold_uncertainty_score":0.2375411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0189792932993449,"score_gpt":0.2855113708409921,"score_spread":0.2665320775416472,"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."}}