{"id":"W1970884796","doi":"10.1073/pnas.1406314111","title":"Lagging adaptation to warming climate in <i>Arabidopsis thaliana</i>","year":2014,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":223,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; National Science Foundation","keywords":"Climate change; Adaptation (eye); Lagging; Biology; Ecology; Range (aeronautics); Local adaptation; Global warming; Population","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001353358,0.00006238471,0.00009090763,0.00007181715,0.0001288856,0.00001775351,0.000457327,0.00003618605,0.0004913122],"category_scores_gemma":[0.0003287557,0.00004570373,0.00003588834,0.0008941187,0.0002845694,0.0003227342,0.0001848604,0.00006795815,0.00003275309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001268646,"about_ca_system_score_gemma":0.000002910325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004220559,"about_ca_topic_score_gemma":0.000003454232,"domain_scores_codex":[0.9986395,0.000006046483,0.0002217568,0.0001877237,0.0007736706,0.0001712699],"domain_scores_gemma":[0.9997023,0.00005186647,0.0001772176,0.000006170914,0.00002801673,0.00003438877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003366703,0.0001336472,0.2743782,0.00008078406,0.000005340116,9.176979e-9,0.002380562,0.00410217,0.5855202,0.1220038,0.005685469,0.005676144],"study_design_scores_gemma":[0.0002745672,0.00005462636,0.8207722,0.000118123,0.00000690513,0.000003740255,0.003428387,0.007646554,0.1514077,0.01100671,0.00509192,0.0001885161],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.939456,0.000007078223,0.000002960191,0.003493945,0.00001925728,0.0001055785,0.00001011646,0.000008597317,0.05689646],"genre_scores_gemma":[0.9986235,0.0000114143,0.000503315,0.0007744013,0.00001560746,0.000008496894,1.573187e-7,0.000002243264,0.00006089294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.546394,"threshold_uncertainty_score":0.5379524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04843992682988193,"score_gpt":0.288607197453613,"score_spread":0.2401672706237311,"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."}}