{"id":"W2972962716","doi":"10.1186/s40317-019-0179-1","title":"The influence of dynamic environmental interactions on detection efficiency of acoustic transmitters in a large, deep, freshwater lake","year":2019,"lang":"en","type":"article","venue":"Animal Biotelemetry","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Fish and Wildlife Service; Mitacs; Great Lakes Fishery Commission; New York State Department of Environmental Conservation","keywords":"Telemetry; Range (aeronautics); Biotelemetry; Environmental science; Thermocline; Reef; Ecology; Oceanography; Biology; Telecommunications; Computer science; Geology; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001180672,0.0003627368,0.000339311,0.0004299792,0.000417801,0.001043632,0.0004812672,0.0003226761,0.0004961769],"category_scores_gemma":[0.003449249,0.0003486731,0.0003189058,0.0004047196,0.0004436027,0.0007993181,0.001064753,0.0002348354,0.0001039263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008440177,"about_ca_system_score_gemma":0.0004803267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01062164,"about_ca_topic_score_gemma":0.01641559,"domain_scores_codex":[0.9995515,0.0001311435,0.00003953322,0.000138825,0.00005545219,0.00008356089],"domain_scores_gemma":[0.9975572,0.001524163,0.0004762215,0.0001051729,0.0001982139,0.0001389878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003544076,0.00008063549,0.9632344,0.00004191689,0.0001411894,0.0001098733,0.000272861,0.01196877,0.01919351,0.00005683727,0.00005052319,0.004495118],"study_design_scores_gemma":[0.000007333386,0.0002092676,0.9615827,0.000004071004,0.00006251244,0.00004467852,0.0002261591,0.03581354,0.001888441,0.00007463495,0.00007066975,0.00001593396],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995016,0.00001315658,0.0003724092,0.000004739109,3.809308e-7,0.000001657374,0.0000305159,0.000005532606,0.00006990199],"genre_scores_gemma":[0.9996592,0.000004915442,0.0002454871,0.000002752345,4.801637e-7,0.000002868771,0.00003560711,0.000002671792,0.00004594847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01062164,"threshold_uncertainty_score":0.02111965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002191543137443178,"score_gpt":0.1972079520449787,"score_spread":0.1950164089075355,"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."}}