{"id":"W6894067487","doi":"10.5281/zenodo.6392290","title":"R code supplementing the article: \"A temperature-driven model of phenological mismatch provides insights into the potential impacts of climate change on consumer-resource interactions\"","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of Ottawa","funders":"","keywords":"Code (set theory); Climate change; Phenology; Section (typography); Series (stratigraphy); Climate model","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004033217,0.004724329,0.004661948,0.002823445,0.001715585,0.004645797,0.004406106,0.003730721,0.5728183],"category_scores_gemma":[0.04325448,0.002696117,0.004899178,0.003420117,0.001475549,0.002908669,0.003707288,0.003426776,0.4174084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00217818,"about_ca_system_score_gemma":0.005583279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01242964,"about_ca_topic_score_gemma":0.01094347,"domain_scores_codex":[0.996606,0.0008158757,0.0003712224,0.000940298,0.000966171,0.0003004914],"domain_scores_gemma":[0.9826763,0.01147219,0.0009763338,0.001718596,0.002489385,0.0006672467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001807004,0.00001500817,0.000674005,0.001762601,0.0001741231,0.0001488372,0.00006555424,0.001635984,0.0006029386,0.003147438,0.9859365,0.005656219],"study_design_scores_gemma":[0.001393349,0.00009577945,0.003065853,0.0009935433,0.0003294986,0.0006199353,0.00005816385,0.0076309,0.003249684,0.02348514,0.9588283,0.0002497495],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.0009871079,0.0004926945,0.04646796,0.001661506,0.002104838,0.0004304552,0.8022817,0.1323789,0.01319488],"genre_scores_gemma":[0.01480559,0.0008336951,0.08023866,0.003704073,0.0006570155,0.004808311,0.5857983,0.2673581,0.04179635],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.5728183,"threshold_uncertainty_score":0.6093228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04093393541941066,"score_gpt":0.2695563778011685,"score_spread":0.2286224423817579,"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."}}