{"id":"W4361190596","doi":"10.1002/ecy.4044","title":"The <scp>L</scp>iving <scp>P</scp>lanet <scp>I</scp>ndex's ability to capture biodiversity change from uncertain data","year":2023,"lang":"en","type":"article","venue":"Ecology","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Index (typography); Population; Climate change; Econometrics; Biodiversity; Population size; Statistics; Ecology; Computer science; Geography; Biology; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001714019,0.0004306663,0.0004998096,0.0001064827,0.001061512,0.0001499868,0.002902272,0.000450503,0.0001478799],"category_scores_gemma":[0.003434939,0.0003430073,0.0001136687,0.000887095,0.0005146125,0.0003636756,0.003956537,0.0005400018,0.006519989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004379736,"about_ca_system_score_gemma":0.00006169009,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007743831,"about_ca_topic_score_gemma":0.05669876,"domain_scores_codex":[0.9955609,0.0004515825,0.0004769349,0.001531816,0.0005664013,0.00141235],"domain_scores_gemma":[0.9914591,0.005896788,0.0002715205,0.001894607,0.00003153752,0.0004464053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001990888,0.0001184815,0.7670765,0.00003607135,0.00005682172,0.0001417305,0.005699341,0.001839703,0.00109904,0.00006349794,0.2232568,0.0006100387],"study_design_scores_gemma":[0.000297828,0.0001229019,0.7985157,0.00001612612,0.00004057435,0.00001318436,0.007129231,0.01314394,0.0000550005,0.0005117635,0.1800842,0.00006949408],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887793,0.00020779,0.00005228973,0.00100746,0.001652848,0.0009783176,0.00158259,0.0002160984,0.005523288],"genre_scores_gemma":[0.9924147,0.0002872941,0.0002611681,0.001266219,0.0002784198,0.00009059606,0.0006476098,0.00003390091,0.004720149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04895493,"threshold_uncertainty_score":0.9999022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02725885938269976,"score_gpt":0.2398167721812549,"score_spread":0.2125579127985552,"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."}}