{"id":"W2800786764","doi":"10.1101/315424","title":"Operationalizing ecological connectivity in spatial conservation planning with Marxan Connect","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Dalhousie University","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; University of Queensland; University of Leeds; Nature Conservancy; Dalhousie University; University of Melbourne; Centre of Excellence for Environmental Decisions, Australian Research Council","keywords":"Operationalization; Computer science; Environmental resource management; Landscape connectivity; Resilience (materials science); Geography; Ecology; Biological dispersal; Environmental science; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001908379,0.0006719396,0.0002749368,0.002416747,0.0004505094,0.001853692,0.0007830667,0.0004914904,0.01343261],"category_scores_gemma":[0.008991987,0.0004612386,0.0005364246,0.001770938,0.0007846566,0.002069398,0.003237566,0.0005861268,0.001614734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102539,"about_ca_system_score_gemma":0.001093363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006370965,"about_ca_topic_score_gemma":0.01670611,"domain_scores_codex":[0.9991606,0.0003279803,0.00006754329,0.0001489855,0.0002427882,0.00005216604],"domain_scores_gemma":[0.997663,0.001516444,0.0002468742,0.0002568212,0.0002107965,0.0001060227],"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":[0.0006232874,0.0002168855,0.03691676,0.0008322515,0.0002533864,0.000467819,0.004173234,0.1839404,0.006396277,0.2788467,0.1391643,0.3481688],"study_design_scores_gemma":[0.0001798429,0.0001393488,0.0165523,0.0003503032,0.0001354911,0.0005008444,0.0009378544,0.6089484,0.008815782,0.1467539,0.2165264,0.0001595508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1050464,0.0003356749,0.7443682,0.001724145,0.0001641269,0.0002928678,0.01054802,0.05635313,0.08116752],"genre_scores_gemma":[0.4169514,0.0003509785,0.5611171,0.0002784652,0.00003322666,0.0006914252,0.007946814,0.004130987,0.008499652],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01343261,"threshold_uncertainty_score":0.0449366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01780143236126222,"score_gpt":0.2282685030994581,"score_spread":0.2104670707381959,"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."}}