{"id":"W4401845731","doi":"10.5530/jscires.13.2.44","title":"Scientometric Insights into the Trends and Evolution of the Underwater Sensing Technologies and their Applications","year":2024,"lang":"en","type":"article","venue":"Journal of Scientometric Research","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; National Research Council Canada","funders":"","keywords":"Underwater; Data science; Geography; Computer science; Oceanography; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.004737007,0.0007150389,0.0004909318,0.02373699,0.0006809894,0.004883834,0.0005127503,0.000616252,0.005603563],"category_scores_gemma":[0.01610782,0.000284674,0.0003815845,0.04059332,0.001296107,0.005935052,0.001348529,0.0009406857,0.001381336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002489302,"about_ca_system_score_gemma":0.001919258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01733683,"about_ca_topic_score_gemma":0.02534505,"domain_scores_codex":[0.9979615,0.0002878678,0.0001625823,0.0001771715,0.001269075,0.0001416969],"domain_scores_gemma":[0.9856111,0.005519912,0.001465939,0.0007846814,0.006308316,0.0003100071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001235016,0.00008689557,0.08862398,0.00166463,0.0001255676,0.000220063,0.002870909,0.009524286,0.007255092,0.0795951,0.03011782,0.7797922],"study_design_scores_gemma":[0.00001347166,0.0001207471,0.4122676,0.000955239,0.0001268193,0.0007284387,0.008392338,0.02976755,0.009045108,0.08803695,0.4503437,0.0002019731],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3501751,0.13753,0.07352684,0.03507764,0.0008199257,0.000325988,0.01531229,0.0008994463,0.3863328],"genre_scores_gemma":[0.8586823,0.07707823,0.03470631,0.0007315041,0.0008839045,0.0001295529,0.005688477,0.0002301951,0.0218695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.976263,"threshold_uncertainty_score":0.03447187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08102982158710223,"score_gpt":0.3691248103624888,"score_spread":0.2880949887753865,"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."}}