{"id":"W2889904364","doi":"10.1016/j.biocon.2018.06.027","title":"Research advances and gaps in marine planning: towards a global database in systematic conservation planning","year":2018,"lang":"en","type":"article","venue":"Biological Conservation","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":90,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Documentation; Marine conservation; Threatened species; Environmental resource management; Best practice; Environmental planning; Systematic review; Marine protected area; Computer science; Data science; Geography; Business; Ecology; Environmental science; Habitat; Political science; MEDLINE; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.001406763,0.0001103669,0.0002311415,0.00005427189,0.0001187605,0.00002797206,0.0001176215,0.00007251405,0.00007319682],"category_scores_gemma":[0.0008172315,0.00007994882,0.00001228738,0.0005537685,0.0002666195,0.0002401829,0.0005142634,0.0001189293,0.00003515875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001740361,"about_ca_system_score_gemma":0.00001304303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003267766,"about_ca_topic_score_gemma":0.003595249,"domain_scores_codex":[0.9985541,0.0003066606,0.0003507771,0.0003281396,0.000196946,0.0002634429],"domain_scores_gemma":[0.9994686,0.0002549923,0.00008097591,0.0001202304,0.00003107685,0.00004413968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000885946,0.00003157913,0.9976882,0.0003368244,0.000002180783,0.00001498714,0.0001333152,0.0000030153,0.0004161251,0.0003562035,0.000370715,0.0005582552],"study_design_scores_gemma":[0.0003131801,0.0002018084,0.9932498,0.0005750254,0.000002027594,0.000007859136,0.0005531741,0.001492849,0.00003578496,0.002007392,0.001453866,0.0001072764],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943099,0.0003745416,0.00004613811,0.0009401627,0.0000681001,0.0004869444,0.000006593217,0.00002731965,0.003740269],"genre_scores_gemma":[0.9989641,0.0000639194,0.0004724943,0.0002804804,0.00003309454,0.0000967,0.00002834693,0.00000254625,0.00005826154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004654226,"threshold_uncertainty_score":0.4939907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1236138730168193,"score_gpt":0.3673883803540717,"score_spread":0.2437745073372524,"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."}}