{"id":"W2154185357","doi":"10.1098/rspb.2012.1931","title":"Quantifying temporal change in biodiversity: challenges and opportunities","year":2012,"lang":"en","type":"review","venue":"Proceedings of the Royal Society B Biological Sciences","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":277,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; University of British Columbia","funders":"Engineering and Physical Sciences Research Council","keywords":"Biodiversity; Autocorrelation; Curse of dimensionality; Time series; Change detection; Environmental change; Global change; Feature (linguistics); Data science; Environmental resource management; Climate change; Computer science; Geography; Ecology; Environmental science; Statistics; Artificial intelligence; Machine learning; Biology; Mathematics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001040852,0.0002417089,0.0005712825,0.00001981747,0.0002716423,0.00004258599,0.0006921018,0.0002771606,0.001162826],"category_scores_gemma":[0.00003331829,0.0001251939,0.0003042601,0.0002691227,0.001943279,0.0001751236,0.001194059,0.0002060095,0.00003284949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001764955,"about_ca_system_score_gemma":0.000008256556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002831895,"about_ca_topic_score_gemma":0.00002388755,"domain_scores_codex":[0.9986084,0.00002685747,0.0002664361,0.0003927227,0.0003008469,0.0004047708],"domain_scores_gemma":[0.9994559,0.00006122552,0.0003268596,0.00005321454,0.00001066343,0.00009209551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003359037,0.0001618107,0.1528794,0.002592118,0.00002788292,4.271492e-7,0.001638326,1.833311e-8,0.000003762716,0.006957289,0.001078432,0.8346571],"study_design_scores_gemma":[0.00009265539,0.0001205384,0.04164871,0.0009560872,0.0000987997,0.000005891648,0.008858663,0.00001141921,0.000002918461,0.00008752984,0.9476689,0.0004479263],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01901303,0.9736843,2.568063e-8,0.0006057784,0.00009523956,0.0003561479,0.00004354507,0.00002422661,0.006177745],"genre_scores_gemma":[0.0130458,0.9866832,0.00002839939,0.0001369731,0.00003556986,0.00003413405,0.000004002138,0.000003058799,0.00002884836],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9465904,"threshold_uncertainty_score":0.9997503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5066790989661801,"score_gpt":0.3433454500387388,"score_spread":0.1633336489274413,"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."}}