{"id":"W2006343822","doi":"10.1111/ecog.00812","title":"A wavelet‐based approach to evaluate the roles of structural and functional landscape heterogeneity in animal space use at multiple scales","year":2014,"lang":"en","type":"article","venue":"Ecography","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Trent University; Université Laval","funders":"Ministère des Transports; European Commission","keywords":"Spatial heterogeneity; Spatial ecology; Ecology; Habitat; Taiga; Temporal scales; Spatial distribution; Scale (ratio); Landscape ecology; Vegetation (pathology); Geography; Biology; Cartography; Remote sensing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001137035,0.0004414635,0.0004116682,0.002779637,0.0002742501,0.0006951387,0.0003850416,0.0003434203,0.001342469],"category_scores_gemma":[0.002522367,0.0001916134,0.0007977883,0.002363268,0.0003763859,0.0006810679,0.0005651006,0.000496873,0.0001531944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003458996,"about_ca_system_score_gemma":0.0003051694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002985415,"about_ca_topic_score_gemma":0.00323268,"domain_scores_codex":[0.9996989,0.00008174545,0.00002591396,0.00006757729,0.00008389726,0.00004212589],"domain_scores_gemma":[0.9991664,0.0004145411,0.0001351325,0.0001100803,0.0001255206,0.00004831633],"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":[0.0009958611,0.0008246111,0.218183,0.0006576871,0.001394187,0.0005475264,0.0011643,0.09933255,0.2047311,0.02337639,0.002165332,0.4466274],"study_design_scores_gemma":[0.0000489266,0.0004684627,0.4102034,0.00003681253,0.000250026,0.0003200904,0.0007160524,0.5650786,0.01009495,0.009952521,0.002742164,0.0000879217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5306724,0.0002593199,0.4652316,0.00009074002,0.00002801363,0.0001524918,0.0008187193,0.0002141156,0.002532589],"genre_scores_gemma":[0.8502013,0.0002190218,0.1478112,0.00003246768,0.00002725753,0.000213799,0.0006262221,0.00004791791,0.0008207331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002985415,"threshold_uncertainty_score":0.006013274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01696288055837161,"score_gpt":0.212273167445743,"score_spread":0.1953102868873714,"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."}}