{"id":"W3205183691","doi":"10.1111/rec.13580","title":"Optimal restoration of wildlife habitat in landscapes fragmented by resource extraction: a network flow modeling approach","year":2021,"lang":"en","type":"article","venue":"Restoration Ecology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Assembly of First Nations; Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Woodland caribou; Habitat; Wildlife; Woodland; Geography; Boreal; Restoration ecology; Wildlife corridor; Resource (disambiguation); Environmental science; Habitat destruction; Population; Critical habitat; Ecology; Environmental resource management; Computer science; Endangered species","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004877436,0.0001178939,0.0002033222,0.00003970334,0.0001480309,0.00001517453,0.00009869519,0.0002411698,0.0002940554],"category_scores_gemma":[0.0001559169,0.0001350761,0.00003642684,0.0004358988,0.00006367074,0.000370225,0.00006038634,0.0002012992,0.00004152815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002240928,"about_ca_system_score_gemma":0.00006963973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000480512,"about_ca_topic_score_gemma":0.002340595,"domain_scores_codex":[0.9984466,0.0003750758,0.0004297089,0.0003383667,0.0001734881,0.0002367137],"domain_scores_gemma":[0.9994256,0.0001166941,0.0001768468,0.0001984655,0.00003308023,0.00004934456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006028095,0.0001279198,0.3432111,0.000004585151,0.000006682616,0.000004307047,0.0001282306,0.6385168,0.0004731583,0.00007909951,0.01726645,0.000121312],"study_design_scores_gemma":[0.0005771752,0.0001033246,0.2152773,0.000008589504,0.00001352083,0.00001655925,0.0002446743,0.7793731,0.00006414177,0.0001546101,0.004024893,0.0001421195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678862,0.00009154554,0.02842536,0.001780356,0.0001679957,0.0001957833,0.000003851378,0.00003039875,0.00141852],"genre_scores_gemma":[0.9875984,0.00002311546,0.01095044,0.0007183973,0.0000896491,0.00006826987,0.0002685268,0.00001174973,0.0002714744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1408563,"threshold_uncertainty_score":0.5508246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01215649935757787,"score_gpt":0.2255952788684018,"score_spread":0.2134387795108239,"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."}}