{"id":"W4367369750","doi":"10.48550/arxiv.2304.14355","title":"Hydra I: An extensible multi-source-finder comparison and cataloguing tool","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Radio Astronomy Observations and Technology","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Cape Town; Universidad de Guanajuato; Canada Research Chairs; Tsinghua University; University of the Western Cape; Canadian Space Agency; University of Pretoria; Science and Technology Facilities Council; National Research Foundation; Leverhulme Trust; University of Minnesota; Universities Space Research Association; Department of Science and Innovation, South Africa; Narodowym Centrum Nauki; National Science Foundation","keywords":"Computer science; Suite; Completeness (order theory); Lernaean Hydra; Software; Context (archaeology); Residual; Software suite; Data mining; Noise (video); Visualization; Artificial intelligence; Image (mathematics); Algorithm; Programming language; Mathematics","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":[0.00369653,0.001762937,0.001351432,0.005372046,0.0008472488,0.002675065,0.005089556,0.001199691,0.04267193],"category_scores_gemma":[0.0110235,0.001662627,0.00242915,0.00262337,0.0005930496,0.00412783,0.005416299,0.001621771,0.02367557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009514631,"about_ca_system_score_gemma":0.001585611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006726556,"about_ca_topic_score_gemma":0.00539684,"domain_scores_codex":[0.9983709,0.0001577686,0.0001913704,0.0003750752,0.0007489549,0.0001558417],"domain_scores_gemma":[0.9956959,0.001362173,0.000386785,0.001495698,0.0007811565,0.0002781423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001205344,0.0002689728,0.01348488,0.001248263,0.0005627638,0.001040749,0.0009649871,0.01425214,0.01900202,0.01152898,0.6608117,0.2756292],"study_design_scores_gemma":[0.0009138812,0.0002713962,0.02651832,0.0003413316,0.0002561322,0.00139964,0.0004448892,0.1146645,0.05531113,0.03218836,0.7668396,0.0008509167],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.006962046,0.0002158771,0.1712498,0.0001864286,0.0001476109,0.0004411415,0.04085473,0.7735021,0.006440295],"genre_scores_gemma":[0.07455219,0.0003496228,0.483334,0.0006878742,0.0002065106,0.00161492,0.2077864,0.2166227,0.0148458],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.04267193,"threshold_uncertainty_score":0.1427518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1458433742442176,"score_gpt":0.2241102216389239,"score_spread":0.07826684739470638,"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."}}