{"id":"W2119988560","doi":"10.1007/978-3-319-03743-1_7","title":"Tracking Animals in a Dynamic Environment: Remote Sensing Image Time Series","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Normalized Difference Vegetation Index; Tracking (education); Remote sensing; Phenology; Time series; Dimension (graph theory); Computer science; Geography; Dynamic data; Environmental science; Climate change; Ecology; Database; Machine learning; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004728501,0.0006015798,0.0003398782,0.001240647,0.0001627625,0.001613169,0.0008957983,0.001001215,0.01156542],"category_scores_gemma":[0.001246467,0.0003234824,0.0002909728,0.002544534,0.0003649683,0.002238889,0.0004387982,0.0007143986,0.007546301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000403238,"about_ca_system_score_gemma":0.0002554666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002150293,"about_ca_topic_score_gemma":0.003979683,"domain_scores_codex":[0.999826,0.00001626158,0.000007245643,0.00004498985,0.00009886572,0.000006630866],"domain_scores_gemma":[0.999656,0.0002037344,0.00002518209,0.00003185374,0.00007067877,0.00001261017],"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":[0.00001030791,0.00001370581,0.000463758,0.0002530589,0.0000207663,0.00004083171,0.00009642709,0.003771497,0.003069295,0.03906275,0.08048627,0.8727114],"study_design_scores_gemma":[0.000002937259,0.00002549664,0.003309854,0.0003704041,0.00002984489,0.0006377152,0.0001226185,0.02881577,0.003486673,0.05568181,0.9074726,0.00004441302],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006898502,0.11902,0.5892055,0.005922561,0.007259812,0.00008554431,0.003170172,0.002809081,0.2656288],"genre_scores_gemma":[0.06020327,0.1337082,0.337351,0.002123838,0.005556287,0.0001273417,0.005127474,0.001471856,0.4543308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01156542,"threshold_uncertainty_score":0.03869015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006740061718735357,"score_gpt":0.1900824535644347,"score_spread":0.1833423918456994,"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."}}