{"id":"W6902059284","doi":"10.6084/m9.figshare.21270855.v1","title":"The Living Planet Index's ability to capture biodiversity change from uncertain data","year":2023,"lang":"en","type":"other","venue":"Figshare","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Population; Climate change; Biodiversity; Index (typography); Uncertainty analysis; Measurement uncertainty; Propagation of uncertainty","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002023915,0.0004911064,0.0003850504,0.0001616147,0.0002326977,0.0002037355,0.003364298,0.0006304462,0.4553382],"category_scores_gemma":[0.003318719,0.0003901602,0.0000553485,0.000426582,0.00002243202,0.0001322808,0.003887097,0.0006135412,0.3192086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002393235,"about_ca_system_score_gemma":0.0001088092,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04803826,"about_ca_topic_score_gemma":0.1903171,"domain_scores_codex":[0.9972358,0.0002011887,0.0001783461,0.001175586,0.0006270801,0.0005819938],"domain_scores_gemma":[0.9953383,0.0007111745,0.0002830156,0.003385376,0.00004612264,0.0002360267],"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":[0.00001071924,0.00001536187,0.00086515,0.0001129095,0.00008469692,0.00004961649,0.0003141981,0.000001389162,5.366368e-7,1.162923e-7,0.9980513,0.0004939506],"study_design_scores_gemma":[0.00008262022,0.000008256732,0.02155526,0.003471271,0.0000317945,6.879922e-7,0.0001455059,0.00005257575,2.341556e-7,0.000001818637,0.9741871,0.0004628506],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000004371081,0.001486245,3.32433e-8,0.0001444861,0.0002514351,0.001191319,0.979692,0.00171946,0.01551062],"genre_scores_gemma":[0.0002546173,0.00001013943,0.00001126983,0.0003305687,0.001165158,0.0001790433,0.9615018,0.0009869711,0.03556048],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1422788,"threshold_uncertainty_score":0.999855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2058080714450148,"score_gpt":0.306214866991966,"score_spread":0.1004067955469512,"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."}}