{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000704369,0.0004108917,0.0003911391,0.000142165,0.0002643589,0.0002299238,0.0003280459,0.000382468,0.001011237],"category_scores_gemma":[0.0002966343,0.0003974875,0.00006357794,0.0003952664,0.000245975,0.0004445946,0.0004000595,0.0006304493,0.0001616432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007477439,"about_ca_system_score_gemma":0.0002030905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002037875,"about_ca_topic_score_gemma":0.0005609881,"domain_scores_codex":[0.9974945,0.0002480246,0.0004906261,0.0009564118,0.0003950745,0.0004153282],"domain_scores_gemma":[0.9986265,0.000182391,0.0003635671,0.0005680703,0.0001260991,0.000133314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001170196,0.0002367551,0.9197477,0.00004550487,0.00003709748,0.00005238589,0.00002641935,0.003453832,0.07517307,0.0002034667,0.0009034844,0.000003276036],"study_design_scores_gemma":[0.0005348015,0.0001416696,0.9700625,0.0002529805,0.00003120503,8.499213e-8,0.000007306064,0.01366423,0.01260091,0.000005567123,0.00211908,0.0005796924],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927574,0.00002059545,0.0049593,0.0007614457,0.0004893572,0.0006884792,0.0000432002,0.0001599992,0.0001201973],"genre_scores_gemma":[0.9950086,0.00000824367,0.003461037,0.0009476739,0.0002431239,0.0002734203,0.000001772674,0.00004995125,0.000006131207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06257217,"threshold_uncertainty_score":0.999902,"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."}}