{"id":"W4404878954","doi":"10.1016/j.nbsj.2024.100198","title":"Leveraging AI for enhanced alignment of national biodiversity targets with the global biodiversity goals","year":2024,"lang":"en","type":"article","venue":"Nature-Based Solutions","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biodiversity; Environmental resource management; Computer science; Environmental science; Biology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000531455,0.0001428675,0.0001269267,0.0001179961,0.0007016396,0.0001433399,0.0007190881,0.000162445,0.00001750852],"category_scores_gemma":[0.000073191,0.0001099989,0.0001401862,0.0008744277,0.0001882249,0.0003917966,0.0001528506,0.000322194,0.00005998793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004484306,"about_ca_system_score_gemma":0.0005296092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001115163,"about_ca_topic_score_gemma":0.0001021973,"domain_scores_codex":[0.9984706,0.00006384632,0.0001509502,0.0004077449,0.0005581154,0.0003487198],"domain_scores_gemma":[0.9988208,0.0002415861,0.00006813666,0.0002870071,0.000511474,0.00007094543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002305969,0.0006771928,0.002383034,0.0002478875,0.0005196761,0.00003766338,0.003296313,0.0427757,0.01022614,0.6373258,0.2993441,0.002935891],"study_design_scores_gemma":[0.001468059,0.0008030745,0.003441875,0.0002667269,0.0002264098,0.00001602628,0.001347561,0.1850819,0.6398051,0.05248228,0.1136986,0.0013624],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009012589,0.0007234046,0.9595364,0.02849385,0.0005821278,0.000438561,0.0005681891,0.0001753846,0.0004695186],"genre_scores_gemma":[0.9909314,0.000004636331,0.006762254,0.002179054,0.00003249085,0.00002238836,0.00003896157,0.000001945856,0.00002685217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9819188,"threshold_uncertainty_score":0.5396515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02741503141704083,"score_gpt":0.283002027810236,"score_spread":0.2555869963931952,"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."}}