{"id":"W2169113815","doi":"10.1111/tgis.12114","title":"Analyzing Animal Movement Characteristics From Location Data","year":2014,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Movement (music); Geography; Range (aeronautics); Habitat; Field (mathematics); Arctic; Orientation (vector space); Cartography; Spatial ecology; Variety (cybernetics); Ecology; Computer science; Biology; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001918779,0.0000547301,0.00006623051,0.00002404964,0.00008300105,0.00001122368,0.0001778462,0.00004658083,0.002626838],"category_scores_gemma":[0.00001877398,0.00006004284,0.000009534219,0.0001702731,0.00004611149,0.0002583419,0.00001503596,0.00008983524,0.0002479722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006906267,"about_ca_system_score_gemma":0.00000655192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001345055,"about_ca_topic_score_gemma":0.002562243,"domain_scores_codex":[0.999432,0.00004360627,0.0001507631,0.0002046906,0.0000707903,0.00009816606],"domain_scores_gemma":[0.9995754,0.00005192644,0.00003726743,0.0003081648,0.00000361617,0.0000236697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002141803,0.0001206387,0.9443988,0.000002578764,0.00001084477,7.572318e-7,0.0002051316,0.001528991,0.001532586,0.0000548874,0.0002896595,0.05183372],"study_design_scores_gemma":[0.0001326326,0.00001711038,0.9437853,0.000004932224,0.00001465137,2.032823e-7,0.00002336614,0.05338388,0.0001057967,0.0002298939,0.002237,0.00006523755],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7900857,0.00000366526,0.2081866,0.0009135145,0.0001005484,0.00006092585,0.0000210639,0.00002064085,0.0006073476],"genre_scores_gemma":[0.9974371,0.00001094874,0.001883441,0.0003842243,0.00002942549,0.00001031991,0.00009605411,0.000004482196,0.0001439677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2073514,"threshold_uncertainty_score":0.9982849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02022415439586346,"score_gpt":0.2387960287634383,"score_spread":0.2185718743675749,"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."}}