{"id":"W2611891293","doi":"10.1146/annurev-ecolsys-110316-022706","title":"Process-Based Models of Phenology for Plants and Animals","year":2017,"lang":"en","type":"article","venue":"Annual Review of Ecology Evolution and Systematics","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":273,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Phenology; Ecology; Key (lock); Process (computing); Ecosystem; Biology; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0004949879,0.00007655231,0.0004061561,0.00001511012,0.0001206237,0.000004268857,0.0001237961,0.00007070089,0.0004127442],"category_scores_gemma":[0.0003890546,0.00006086878,0.00003703373,0.00002131438,0.0003150101,0.00008652876,0.00005486038,0.00002534478,0.000007460082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004524025,"about_ca_system_score_gemma":0.00001373385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001213295,"about_ca_topic_score_gemma":0.00006529166,"domain_scores_codex":[0.9992374,0.00004869124,0.0003820035,0.0001275477,0.00008704072,0.0001172877],"domain_scores_gemma":[0.999101,0.00008852383,0.0005266793,0.0001844518,0.00005842709,0.00004086149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"observational","study_design_scores_codex":[0.0003442314,0.001159583,0.2493053,0.4250617,0.0003042333,0.000006922185,0.003499918,0.0001142246,0.001833414,0.2703082,0.04320225,0.004860007],"study_design_scores_gemma":[0.008540532,0.004775374,0.8080404,0.03220155,0.0009116738,0.0002179968,0.02234454,0.06655269,0.001101433,0.04283463,0.01083069,0.001648519],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9482535,0.02901051,0.002546329,0.001693195,0.0002308835,0.002678996,0.001193155,0.00002254274,0.01437091],"genre_scores_gemma":[0.9956562,0.00397061,0.0001301246,0.000131362,0.00000502076,0.00005120335,0.00001124591,0.00000351364,0.0000407075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.558735,"threshold_uncertainty_score":0.451926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04543102589249316,"score_gpt":0.3223571625184776,"score_spread":0.2769261366259845,"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."}}