{"id":"W4386996562","doi":"10.3390/su151914101","title":"Mexico on Track to Protect 30% of Its Marine Area by 2030","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Coastal and Marine Management","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Comisión Nacional de Áreas Naturales Protegidas","keywords":"Marine protected area; Geoprocessing; Marine conservation; Exclusive economic zone; Convention on Biological Diversity; Protected area; Geography; Biodiversity; Environmental protection; Environmental resource management; Marine biodiversity; Environmental planning; Fishery; Environmental science; Cartography; Habitat; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004596914,0.0002301137,0.0001674018,0.00081777,0.0006617776,0.0009349337,0.0003430383,0.0003004585,0.006536589],"category_scores_gemma":[0.0007638344,0.00008104249,0.0002675167,0.001132682,0.0001687679,0.0005994304,0.0007347672,0.0005783091,0.0007213284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001527675,"about_ca_system_score_gemma":0.00308247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1197269,"about_ca_topic_score_gemma":0.1796707,"domain_scores_codex":[0.9998465,0.00002284624,0.000006226564,0.00002344097,0.00004775983,0.00005328665],"domain_scores_gemma":[0.9996127,0.00002957135,0.0001038036,0.00003563882,0.000152886,0.00006536304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002425911,0.0001615428,0.3618371,0.000803364,0.0002809953,0.0006487606,0.0009703496,0.002659309,0.003858534,0.02072318,0.2355033,0.3723109],"study_design_scores_gemma":[0.00003961108,0.0001170331,0.4465408,0.0004332046,0.0001327071,0.0002148844,0.002248781,0.001768641,0.001459664,0.001743809,0.5452746,0.00002618632],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6614253,0.00641747,0.009399492,0.02104098,0.0006062038,0.0003285538,0.0627616,0.001294673,0.2367257],"genre_scores_gemma":[0.860257,0.007054125,0.03485582,0.001569703,0.0001482891,0.0006886687,0.05114258,0.0001271244,0.04415677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1197269,"threshold_uncertainty_score":0.2380599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00877315994349237,"score_gpt":0.2390068649213924,"score_spread":0.2302337049779,"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."}}