{"id":"W2076548612","doi":"10.1186/1472-6785-10-22","title":"radR: an open-source platform for acquiring and analysing data on biological targets observed by surveillance radar","year":2010,"lang":"en","type":"article","venue":"BMC Ecology","topic":"Avian ecology and behavior","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Acadia University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Brock University; Canadian Natural Resources Limited","keywords":"Radar; Computer science; Software; Data acquisition; Source code; Component (thermodynamics); Data processing; Secondary surveillance radar; Real-time computing; Remote sensing; Data mining; Database; Telecommunications; Geography; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008180931,0.0001532951,0.0002748351,0.00002018075,0.0003532489,0.0000411656,0.0009690014,0.000267273,0.001349692],"category_scores_gemma":[0.0002148618,0.0001279225,0.00002196555,0.00007287708,0.0003876261,0.0003557063,0.0009881433,0.0002001175,0.00006339177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003930698,"about_ca_system_score_gemma":0.00001405115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004514921,"about_ca_topic_score_gemma":0.01065881,"domain_scores_codex":[0.9985574,0.00008511343,0.000213298,0.0006845156,0.00005859881,0.0004010486],"domain_scores_gemma":[0.9988148,0.0003792541,0.0001113304,0.0005616779,0.00000533701,0.0001276112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001009728,0.0001726866,0.9702954,0.000001825397,0.000006876959,0.000002910314,0.00002451134,0.0000108715,0.02360672,0.00002321479,0.002062354,0.003691659],"study_design_scores_gemma":[0.0007351745,0.0004593042,0.9818811,8.319896e-7,0.00001032428,0.00001445624,0.00003822269,0.001120767,0.0007050429,0.0001529674,0.01469019,0.0001916641],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981862,0.0000144076,0.0006569714,0.0001574592,0.0002289495,0.0004292922,0.00008071027,0.00003958674,0.0002064288],"genre_scores_gemma":[0.9892133,0.000007554732,0.009628997,0.0004651498,0.00004912035,0.00003444137,0.0003261933,0.00001449843,0.0002606807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02290167,"threshold_uncertainty_score":0.9995632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09671611915313542,"score_gpt":0.3088678081320784,"score_spread":0.212151688978943,"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."}}