{"id":"W2066483527","doi":"10.1890/12-2235.1","title":"How climate extremes—not means—define a species' geographic range boundary via a demographic tipping point","year":2013,"lang":"en","type":"article","venue":"Ecological Monographs","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Division of Mathematical Sciences; National Science Foundation","keywords":"Biological dispersal; Range (aeronautics); Ecology; Biology; Fecundity; Population; Gypsy moth; Local adaptation; Survivorship curve; Tipping point (physics); Climate change; Abundance (ecology); Vital rates; Latitude; Geography; Population growth; Demography; Lepidoptera genitalia","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004986059,0.0005009313,0.0005156185,0.0002350671,0.0007272331,0.0004899084,0.0006080955,0.0003138005,0.1263943],"category_scores_gemma":[0.0000996221,0.0004055586,0.0005995037,0.001525193,0.001120803,0.0006422484,0.0005589693,0.0004480702,0.002528489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002339985,"about_ca_system_score_gemma":0.000006058744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001439096,"about_ca_topic_score_gemma":0.0005862819,"domain_scores_codex":[0.9964564,0.0001797249,0.0005041439,0.0008884721,0.0006053883,0.001365847],"domain_scores_gemma":[0.9985223,0.0001413767,0.0002314105,0.0005697406,0.00004558954,0.0004896299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009646289,0.001067076,0.9450384,0.00004239731,0.00009110248,0.00007126565,0.000190582,0.000007420195,0.01141512,0.002533106,0.02978673,0.009660373],"study_design_scores_gemma":[0.0007783285,0.0003041901,0.9342979,0.00001218993,0.00004032169,0.00003321677,0.0007647272,0.0002503283,0.0001458666,0.00191158,0.06089319,0.0005681468],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9666646,0.0003516513,0.0004461209,0.006488353,0.0003562676,0.001000323,0.0001161662,0.0004669192,0.0241096],"genre_scores_gemma":[0.9949887,0.00106932,0.0005024608,0.002443456,0.00007097222,0.0004184979,0.0001288379,0.00003271114,0.0003449907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1238658,"threshold_uncertainty_score":0.9998396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0245053261193871,"score_gpt":0.2096263171433136,"score_spread":0.1851209910239265,"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."}}