{"id":"W4297899836","doi":"10.1007/s10980-022-01516-7","title":"Integrating land use and climate change models with stakeholder priorities to evaluate habitat connectivity change: a case study in southern Québec","year":2022,"lang":"en","type":"article","venue":"Landscape Ecology","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Climate change; Landscape ecology; Environmental resource management; Stakeholder; Land use, land-use change and forestry; Landscape connectivity; Land use; Geography; Wildlife corridor; Habitat; Ecology; Environmental planning; Environmental science; Biological dispersal; Political science; Biology; Population","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001685954,0.0004753592,0.0003584867,0.0007771608,0.003234942,0.002232817,0.001450934,0.001160712,0.002360288],"category_scores_gemma":[0.004256217,0.0002353482,0.0004260078,0.002393355,0.001091945,0.0008544832,0.0008326934,0.001034754,0.0001787847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04316753,"about_ca_system_score_gemma":0.0210734,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.991037,"about_ca_topic_score_gemma":0.9967175,"domain_scores_codex":[0.9988967,0.0004991859,0.00002902788,0.00008925438,0.0001600845,0.0003257559],"domain_scores_gemma":[0.9974076,0.001019629,0.0001896972,0.00009713374,0.0008885604,0.0003974538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001122134,0.002711363,0.715958,0.0002966253,0.0005604504,0.006390197,0.01100192,0.1523804,0.004192292,0.005869511,0.01309107,0.086426],"study_design_scores_gemma":[0.0003975524,0.0008942353,0.5644664,0.000240192,0.0002993985,0.0003738829,0.05561518,0.3569749,0.001620878,0.001773421,0.01716175,0.0001820223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918993,0.0001407133,0.0008601408,0.000753973,0.00001084193,0.0001420441,0.0004796238,0.00003533192,0.005677886],"genre_scores_gemma":[0.9941893,0.0001047053,0.002569649,0.0001484988,0.000003616733,0.00005541459,0.0002746469,0.00001475849,0.002639348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04316753,"threshold_uncertainty_score":0.3132038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08268679665948751,"score_gpt":0.2711802451044528,"score_spread":0.1884934484449652,"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."}}