{"id":"W3172073922","doi":"10.5194/agile-giss-2-21-2021","title":"Using eigen decomposition and sequence-based representation to extract movement patterns from contextualized tracking data","year":2021,"lang":"en","type":"article","venue":"AGILE GIScience Series","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Categorical variable; Dimensionality reduction; Computer science; Principal component analysis; Set (abstract data type); Curse of dimensionality; Artificial intelligence; Representation (politics); Data mining; Biome; Data set; Sequence (biology); Pattern recognition (psychology); Geography; Ecology; Machine learning; Biology","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":[0.0007303892,0.0006743136,0.0005072095,0.003631427,0.0002798028,0.001063628,0.0003412248,0.0004541746,0.001466595],"category_scores_gemma":[0.003361743,0.0002162659,0.0008262061,0.003191439,0.0003469495,0.001079104,0.0007540075,0.0005842302,0.0006522658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003514379,"about_ca_system_score_gemma":0.0006257345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004561359,"about_ca_topic_score_gemma":0.005082891,"domain_scores_codex":[0.9995684,0.0001115367,0.00004468146,0.0001368662,0.00008256183,0.00005587873],"domain_scores_gemma":[0.9985394,0.0006776075,0.0002295772,0.0001916507,0.0003005592,0.00006113118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004224993,0.0003750462,0.03053708,0.0004215647,0.0002834763,0.0004749554,0.001060694,0.1184011,0.0501178,0.01230008,0.006464819,0.7791408],"study_design_scores_gemma":[0.00001033753,0.000108141,0.01598737,0.00005578494,0.00004205475,0.0001731582,0.0003144846,0.9609992,0.004748024,0.01366177,0.003850145,0.00004950593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1063782,0.0004322594,0.8888368,0.0002207676,0.00007479324,0.0001083037,0.001544128,0.001565211,0.00083953],"genre_scores_gemma":[0.4455752,0.0005094726,0.5478504,0.00006699534,0.00006856251,0.0002488618,0.004428765,0.0001447616,0.001106906],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004561359,"threshold_uncertainty_score":0.009069622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1282803332838962,"score_gpt":0.3555813841266788,"score_spread":0.2273010508427825,"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."}}