{"id":"W2794171735","doi":"10.7287/peerj.preprints.26748v1","title":"Monitoring biodiversity in offshore marine protected areas: A habitat mapping approach","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Scotia Community College","funders":"","keywords":"Biodiversity; Habitat; Marine protected area; Species richness; Marine reserve; Benthos; Bathymetry; Marine habitats; Benthic zone; Submarine pipeline; Marine ecosystem; Environmental science; Marine spatial planning; Geography; Marine conservation; Ecology; Ecosystem; Environmental resource management; Oceanography; Geology; Biology; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007310633,0.0003160639,0.0002301531,0.003621483,0.0004671278,0.0009603502,0.0006224799,0.0002442554,0.001275899],"category_scores_gemma":[0.001590262,0.0001817928,0.0002010985,0.003743455,0.0002817164,0.0006930468,0.001096472,0.0002329939,0.0001236125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005537812,"about_ca_system_score_gemma":0.0007344402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02502644,"about_ca_topic_score_gemma":0.0593558,"domain_scores_codex":[0.999655,0.0001576522,0.00001724642,0.00006763087,0.00006661766,0.00003579538],"domain_scores_gemma":[0.9993587,0.0001986952,0.0001792323,0.00008529489,0.0001195489,0.00005860404],"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":[0.0001030163,0.0002264532,0.4533123,0.000314655,0.0002014601,0.0003697263,0.002658346,0.02921397,0.008923833,0.007973392,0.002812814,0.4938899],"study_design_scores_gemma":[0.00002169264,0.0001939886,0.8644078,0.0001880084,0.0001174316,0.0003902741,0.007639046,0.0966223,0.002637962,0.01235819,0.01537741,0.00004593217],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7739432,0.0008381965,0.1973706,0.0006836153,0.00003111656,0.0003557944,0.004029945,0.0002823466,0.02246517],"genre_scores_gemma":[0.843466,0.0005854073,0.1523862,0.00004943394,0.00001748136,0.0002050444,0.001172688,0.0000264273,0.002091362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02502644,"threshold_uncertainty_score":0.04976153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04053630056281521,"score_gpt":0.2332152744230908,"score_spread":0.1926789738602756,"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."}}