{"id":"W4387939636","doi":"10.7717/peerj.16333","title":"Using sentinel nodes to evaluate changing connectivity in a protected area network","year":2023,"lang":"en","type":"article","venue":"PeerJ","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of the Environment, Conservation and Parks; Ministry of Natural Resources and Forestry","funders":"Ministère de l’Environnement, de la Protection de la nature et des Parcs; Ontario Ministry of Natural Resources and Forestry","keywords":"Protected area; Biodiversity; Landscape connectivity; Pairwise comparison; Environmental resource management; Track (disk drive); Computer science; Node (physics); Geography; Baseline (sea); Environmental science; Ecology; Fishery; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004532112,0.00007216755,0.00008366476,0.00008418306,0.0001240821,0.00003003246,0.00006652238,0.00002568986,0.0004095302],"category_scores_gemma":[0.0001231871,0.00007259822,0.00002503942,0.001229728,0.00001443655,0.0001488898,0.0001252017,0.00007461204,0.0004327438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001286196,"about_ca_system_score_gemma":0.00000745443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009475533,"about_ca_topic_score_gemma":0.0003493202,"domain_scores_codex":[0.9992002,0.00005129771,0.0001123713,0.0001988248,0.0001630681,0.0002742294],"domain_scores_gemma":[0.9997507,0.00004284139,0.0000350983,0.0001228044,0.00001000829,0.00003851507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003322054,0.00004603003,0.6807231,0.000007557429,0.000008766236,0.00001243737,0.0009408001,0.2590585,0.04914393,0.00003383253,0.003994539,0.005997327],"study_design_scores_gemma":[0.0001149928,0.000006903108,0.5373788,0.00003387177,0.000003764037,0.000003720286,0.00009225174,0.4608482,0.0002557136,0.000163379,0.001009949,0.00008838361],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954818,0.000001807761,0.001464799,0.001076727,0.0001193993,0.000264102,0.00000138759,0.00008336554,0.001506563],"genre_scores_gemma":[0.9980662,7.106904e-7,0.0007158224,0.0003302111,0.00005691922,0.00006658704,0.000004712761,0.000009239764,0.0007495591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2017898,"threshold_uncertainty_score":0.556219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06794084980826823,"score_gpt":0.3087539331643963,"score_spread":0.240813083356128,"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."}}