{"id":"W2075599818","doi":"10.1111/maec.12228","title":"Finding the hotspots within a biodiversity hotspot: fine‐scale biological predictions within a submarine canyon using high‐resolution acoustic mapping techniques","year":2014,"lang":"en","type":"article","venue":"Marine Ecology","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Koninklijk Belgisch Instituut voor Natuurwetenschappen; Natural Environment Research Council; Sight Research UK","keywords":"Transect; Submarine canyon; Biodiversity; Canyon; Biodiversity hotspot; Species richness; Megafauna; Hotspot (geology); Oceanography; Benthic zone; Environmental science; Ecology; Geography; Geology; Cartography; Biology; Paleontology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0003945715,0.0003010965,0.0002452058,0.001059043,0.0002463143,0.0005813086,0.0002337016,0.000280585,0.000431337],"category_scores_gemma":[0.0007496593,0.0001731668,0.0003663293,0.0005013859,0.0001523164,0.0003218959,0.0004946579,0.0001521031,0.00009689313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002064794,"about_ca_system_score_gemma":0.0002233423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01771037,"about_ca_topic_score_gemma":0.03124263,"domain_scores_codex":[0.9998997,0.00002005509,0.000004992089,0.00004022459,0.0000134557,0.00002150353],"domain_scores_gemma":[0.9996419,0.0001549386,0.00007919046,0.00002679943,0.00004895431,0.00004808502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009747555,0.00004868242,0.9492071,0.00002966781,0.0001347349,0.0001344585,0.0002863529,0.02611659,0.006169758,0.00008480396,0.0001257236,0.01756472],"study_design_scores_gemma":[0.000005956269,0.00005552613,0.8954362,0.00001065207,0.00003802958,0.00004491963,0.0004169885,0.1034248,0.0003613278,0.000112495,0.00008312209,0.000009970308],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989034,0.00002238568,0.0008907845,0.000007654384,7.169753e-7,0.000003167845,0.00004338295,0.00001210414,0.0001163785],"genre_scores_gemma":[0.9989333,0.00001512059,0.0009150518,0.000001559763,0.000001143917,0.000003415678,0.00008622707,0.000001878022,0.00004226635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01771037,"threshold_uncertainty_score":0.0352146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0358066663809103,"score_gpt":0.2373923971494153,"score_spread":0.201585730768505,"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."}}