{"id":"W2412953118","doi":"10.1111/gcb.13390","title":"Amphibian breeding phenology trends under climate change: predicting the past to forecast the future","year":2016,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Wildlife Federation; Ontario Ministry of Natural Resources and Forestry; World Wildlife Fund","keywords":"Phenology; Overwintering; Climate change; Precipitation; Environmental science; Spring (device); Population; Frost (temperature); Climatology; Global warming; Snowmelt; Range (aeronautics); Growing degree-day; Geography; Ecology; Snow; Physical geography; Biology; Meteorology; Demography","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":[0.0004454182,0.0003467357,0.0001661837,0.001410678,0.0001819478,0.0006215948,0.0002368077,0.0003177313,0.0007008383],"category_scores_gemma":[0.001190595,0.0001430618,0.0002981253,0.0007985816,0.0001297037,0.0004638755,0.0002165296,0.0002893892,0.0002459499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004393757,"about_ca_system_score_gemma":0.0002393452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01775955,"about_ca_topic_score_gemma":0.02482976,"domain_scores_codex":[0.9999362,0.00001085183,0.000005972322,0.00002403126,0.0000119208,0.00001101533],"domain_scores_gemma":[0.9993272,0.0001852043,0.0002166695,0.00002901033,0.0001570271,0.00008488865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000563079,0.00004720053,0.9492999,0.0000249887,0.00006696557,0.0000587037,0.00007320949,0.03846268,0.001074917,0.0001072485,0.0004350634,0.01029285],"study_design_scores_gemma":[0.000005917869,0.00006327775,0.8114471,0.00001539359,0.0000353061,0.00007411243,0.0001864884,0.1868437,0.0003861017,0.000195592,0.0007357656,0.00001125555],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958092,0.0002425133,0.002053156,0.0000774017,0.00001022756,0.000009392407,0.0009811785,0.00007690511,0.0007400686],"genre_scores_gemma":[0.9972997,0.0001687492,0.001533248,0.000008302522,0.00001183732,0.000005782872,0.0008100861,0.000007591741,0.0001545781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01775955,"threshold_uncertainty_score":0.03531235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05923086483295783,"score_gpt":0.285148513712756,"score_spread":0.2259176488797981,"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."}}