{"id":"W2076844441","doi":"10.1007/s10980-015-0156-x","title":"Improving inferences about functional connectivity from animal translocation experiments","year":2015,"lang":"en","type":"article","venue":"Landscape Ecology","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Division of Environmental Biology; Baylor University","keywords":"Functional diversity; Computer science; Functional connectivity; Landscape connectivity; Biological dispersal; Ecology; Biology; Neuroscience; Population","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":[0.007725703,0.001083143,0.001355205,0.002110877,0.0009653411,0.001337503,0.001499367,0.001806905,0.004075646],"category_scores_gemma":[0.05447295,0.0006912802,0.001001648,0.002198163,0.001176323,0.003200464,0.001323012,0.001647298,0.0008587713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003376097,"about_ca_system_score_gemma":0.0004058072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00257258,"about_ca_topic_score_gemma":0.003963405,"domain_scores_codex":[0.9950433,0.003278037,0.0001827726,0.001084554,0.0002727212,0.0001386504],"domain_scores_gemma":[0.9176116,0.06939095,0.004251999,0.006683658,0.001353426,0.0007082949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002240429,0.0006079868,0.7171235,0.0009232892,0.004255409,0.001222808,0.0008466091,0.08852138,0.03701282,0.009938893,0.003453005,0.1338539],"study_design_scores_gemma":[0.0007165254,0.00165848,0.4892195,0.0002125202,0.003633102,0.002514194,0.0006749087,0.3615764,0.01551571,0.1163665,0.007719259,0.000192862],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8816845,0.0006072898,0.1116157,0.0002587236,0.00006448007,0.00005073992,0.001985521,0.0005297062,0.003203368],"genre_scores_gemma":[0.9660541,0.0001967854,0.03126504,0.0001528126,0.00005880013,0.00006895351,0.001801327,0.00009800343,0.0003041286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007725703,"threshold_uncertainty_score":0.04085791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02876737640422086,"score_gpt":0.2451361571707373,"score_spread":0.2163687807665164,"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."}}