{"meta":{"query_hash":"0df1003a3d35","filters":{"venue":"Journal of Computational Vision and Imaging Systems"},"cohort_total":116,"direct_labels_cover":0,"predictions_cover":116,"exported":116,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/0df1003a3d35","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Computational+Vision+and+Imaging+Systems"},"results":[{"id":"W2526401461","doi":"10.15353/vsnl.v2i1.95","title":"Deep Quality: A Deep No-reference Quality Assessment System","year":2016,"lang":"en","type":"preprint","venue":"Journal of Computational Vision and Imaging Systems","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Image quality; Quality (philosophy); Convolutional neural network; Benchmark (surveying); Artificial neural network; Subjective video quality; Computer vision; Pattern recognition (psychology); Image (mathematics); Cartography","score_opus":0.04979339762533676,"score_gpt":0.3939700373823744,"score_spread":0.34417663975703766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2526401461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09659121,0.0012220856,0.8626986,0.00065196084,0.00023194526,0.00036253393,0.0022412296,0.029917046,0.006083289],"genre_scores_gemma":[0.7226378,0.0006052553,0.2609381,0.000825328,0.00008650804,0.00021353358,0.0039972784,0.00042263523,0.010273612],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994562,0.000054846998,0.000034468474,0.00014761595,0.00024973074,0.00005715447],"domain_scores_gemma":[0.9992768,0.00010040251,0.00009112315,0.0001387885,0.00032803442,0.00006479562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009531471,0.00080962473,0.0006365166,0.0008568203,0.00018979123,0.0008981742,0.0015377803,0.0008618811,0.0037891779],"category_scores_gemma":[0.0027224983,0.0002997989,0.00038497904,0.00036862408,0.00024605636,0.001406631,0.0016168877,0.00091780483,0.0012173356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011128656,0.0005339868,0.0070843333,0.00023251904,0.00018416818,0.00022781608,0.00007029685,0.04543262,0.053752236,0.0028928195,0.02334895,0.86512727],"study_design_scores_gemma":[0.00006615053,0.0002375892,0.003244664,0.000023717186,0.00004476046,0.00013964112,0.000014909606,0.9673214,0.02308743,0.0024656602,0.0033239177,0.000030098303],"about_ca_topic_score_codex":0.004770031,"about_ca_topic_score_gemma":0.0059085,"teacher_disagreement_score":0.004770031,"about_ca_system_score_codex":0.00086062343,"about_ca_system_score_gemma":0.000539858,"threshold_uncertainty_score":0.01267612},"labels":[],"label_agreement":null},{"id":"W2555769692","doi":"10.15353/vsnl.v1i1.63","title":"Superpixel-based Prostate Cancer Detection from Diffusion Magnetic Resonance Imaging","year":2015,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Magnetic resonance imaging; Prostate cancer; Diffusion-Weighted Magnetic Resonance Imaging; Diffusion MRI; Cancer; Cancer detection; Prostate; Computer science; Computation; Medicine; Artificial intelligence; Radiology; Internal medicine; Algorithm","score_opus":0.028822922260031043,"score_gpt":0.33677633269411783,"score_spread":0.3079534104340868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555769692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013834079,0.0017121511,0.9817259,0.00018298821,0.000049750295,0.00007507301,0.00020736942,0.0012665683,0.00094610924],"genre_scores_gemma":[0.17689003,0.0021991923,0.8167946,0.0003002649,0.00016022038,0.00013296006,0.0007395827,0.00030455043,0.002478542],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994722,0.0000852866,0.00001887461,0.00011378918,0.00026181212,0.00004814178],"domain_scores_gemma":[0.9995484,0.0001758466,0.000064191336,0.00007417365,0.00010686405,0.000030472811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005301183,0.0011205518,0.0010378392,0.0019952937,0.0003064376,0.00084803393,0.0012878096,0.0010673623,0.001842741],"category_scores_gemma":[0.001505451,0.0006611894,0.0009420315,0.001218744,0.00037739714,0.0010153939,0.0010670912,0.00082870154,0.00092673226],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032117811,0.000114821734,0.002117976,0.0005664537,0.00016995595,0.00042213072,0.00012378741,0.07851309,0.18703495,0.004952552,0.005184353,0.7204788],"study_design_scores_gemma":[0.00002142307,0.000097300814,0.003000976,0.000030947962,0.000078418096,0.0010593211,0.000023592542,0.91418666,0.07095008,0.0053716507,0.0051200045,0.000059450722],"about_ca_topic_score_codex":0.0024515921,"about_ca_topic_score_gemma":0.0051792213,"teacher_disagreement_score":0.0024515921,"about_ca_system_score_codex":0.00044505467,"about_ca_system_score_gemma":0.00054491893,"threshold_uncertainty_score":0.006164551},"labels":[],"label_agreement":null},{"id":"W2595012910","doi":"10.15353/vsnl.v2i1.90","title":"Evaluation of a Coherent Point Drift Algorithm for Breast Image Registration via Surface Markers","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Sunnybrook Research Institute","keywords":"Affine transformation; Image registration; Artificial intelligence; Computer vision; Matching (statistics); Point set registration; Computer science; Point (geometry); Algorithm; Mathematics; Image (mathematics); Medicine; Pathology; Geometry","score_opus":0.014613050608372702,"score_gpt":0.31655649150161097,"score_spread":0.30194344089323827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595012910","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081933305,0.00060877495,0.9144733,0.00016124031,0.00010464926,0.00034373405,0.00007288361,0.0015105458,0.0007915604],"genre_scores_gemma":[0.19078477,0.0003425217,0.8066237,0.00007115685,0.000027585022,0.00022111552,0.00035372848,0.00024456612,0.0013308547],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974254,0.0006932399,0.00021080782,0.00042061607,0.0011409876,0.00010894313],"domain_scores_gemma":[0.9972658,0.0012112312,0.00022354942,0.0003595109,0.000850776,0.00008923128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044729747,0.00084327086,0.0007952872,0.0012476827,0.00043063692,0.001128825,0.0013320772,0.0012834588,0.0014902353],"category_scores_gemma":[0.009560397,0.00038903917,0.0006784031,0.0011239204,0.0005073255,0.0009634156,0.0012730871,0.00075500476,0.00059016846],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001174717,0.00034596366,0.004235108,0.00027430925,0.0003080901,0.00011324828,0.00022859857,0.16366935,0.06935548,0.0039467188,0.001949246,0.7543992],"study_design_scores_gemma":[0.00009570582,0.000819681,0.0020402314,0.000013174702,0.00005466254,0.00021764802,0.000049187398,0.95786387,0.035863284,0.0006015988,0.0023477434,0.000033187946],"about_ca_topic_score_codex":0.0032558767,"about_ca_topic_score_gemma":0.0027260869,"teacher_disagreement_score":0.0044729747,"about_ca_system_score_codex":0.00061730685,"about_ca_system_score_gemma":0.0016326212,"threshold_uncertainty_score":0.023655593},"labels":[],"label_agreement":null},{"id":"W2595688356","doi":"10.15353/vsnl.v2i1.105","title":"Compact, Field-Portable Lens-free Microscope using Superresolution Spatio-Spectral Light-field Fusion","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Digital Holography and Microscopy","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Microscope; Optics; Lens (geology); Microscopy; Optical microscope; Resolution (logic); Spectral resolution; SIGNAL (programming language); Materials science; Physics; Computer science; Artificial intelligence; Spectral line; Scanning electron microscope","score_opus":0.009054480962375199,"score_gpt":0.26769385650861327,"score_spread":0.25863937554623806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595688356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3094008,0.0022284132,0.68047833,0.00036495883,0.00013844986,0.00024504145,0.0003543434,0.0020642127,0.0047255233],"genre_scores_gemma":[0.44702905,0.00064936857,0.5472936,0.000117945914,0.000055298384,0.00015692765,0.0002526983,0.00006171664,0.0043833675],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997799,0.000016206906,0.000009884839,0.000051139872,0.000120993886,0.00002191634],"domain_scores_gemma":[0.99982893,0.00003550186,0.00004226764,0.00003158104,0.000029904319,0.00003168823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002563804,0.00036930628,0.0003934685,0.00037948746,0.00024351547,0.00038099725,0.0008835374,0.0005727635,0.0014309278],"category_scores_gemma":[0.00020351913,0.00029992155,0.00023930735,0.00019247655,0.0003416864,0.0007691126,0.0008243989,0.00044221428,0.00044264764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003329877,0.000017319548,0.00012865181,0.0000644233,0.0000045171005,0.000055776694,0.000028060142,0.00024401602,0.98951894,0.0006835768,0.00020060939,0.009020878],"study_design_scores_gemma":[0.000044039516,0.00030243833,0.0017010286,0.000014927807,0.000018895711,0.001318415,0.00002911145,0.015412248,0.97120494,0.00033212363,0.009576954,0.000044888977],"about_ca_topic_score_codex":0.00040611427,"about_ca_topic_score_gemma":0.0008439735,"teacher_disagreement_score":0.0014309278,"about_ca_system_score_codex":0.00039738786,"about_ca_system_score_gemma":0.00032716358,"threshold_uncertainty_score":0.0047869086},"labels":[],"label_agreement":null},{"id":"W2595725800","doi":"10.15353/vsnl.v2i1.91","title":"A Compact Field-portable Computational Multispectral Microscope using Integrated Raspberry Pi","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Achromatic lens; Multispectral image; Broadband; Optics; Microscopy; Microscope; Computer science; Hyperspectral imaging; Optical microscope; Materials science; Computer vision; Artificial intelligence; Physics; Scanning electron microscope","score_opus":0.010141931197936853,"score_gpt":0.2881867206498115,"score_spread":0.27804478945187466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595725800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14747214,0.0012144103,0.83050704,0.0003147993,0.00020665707,0.00039702497,0.00039133438,0.008866478,0.0106300665],"genre_scores_gemma":[0.31300566,0.00048530885,0.6774932,0.0001723605,0.00005717853,0.00029322208,0.00040105337,0.00015959649,0.007932376],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997918,0.000011800073,0.000007240049,0.000054270808,0.00011812092,0.00001680837],"domain_scores_gemma":[0.9998579,0.000027319576,0.000018392502,0.000033568358,0.00004091622,0.000021994227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001739099,0.00033826908,0.00031457862,0.00037063385,0.00028993463,0.00033596132,0.0010295312,0.0003818075,0.0053452556],"category_scores_gemma":[0.00025433674,0.00025262617,0.00017449891,0.0002460906,0.0001800584,0.00078545226,0.00047458799,0.00042885428,0.0013588656],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018847031,0.000093591785,0.0007516615,0.00025784876,0.000017975191,0.00014468736,0.000061701896,0.0020211593,0.86166066,0.0026605267,0.0028044286,0.12933722],"study_design_scores_gemma":[0.0001464566,0.0012593783,0.009385016,0.000066050634,0.00009173651,0.002614089,0.00008620913,0.14160356,0.754194,0.0012350967,0.0891799,0.00013856727],"about_ca_topic_score_codex":0.0005977408,"about_ca_topic_score_gemma":0.0013131477,"teacher_disagreement_score":0.0053452556,"about_ca_system_score_codex":0.00025260137,"about_ca_system_score_gemma":0.00038437307,"threshold_uncertainty_score":0.017881632},"labels":[],"label_agreement":null},{"id":"W2595974524","doi":"10.15353/vsnl.v2i1.96","title":"Depth from Defocus via Active Quasi-random Point Projections","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Projector; Computer science; Leverage (statistics); Projection (relational algebra); Computer vision; Random projection; Artificial intelligence; Point (geometry); Inference; Compressed sensing; Calibration; Algorithm; Mathematics; Geometry","score_opus":0.007389555400073491,"score_gpt":0.25923019862065677,"score_spread":0.2518406432205833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595974524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017688204,0.00018010274,0.9806635,0.00012417832,0.00002631176,0.00003529203,0.00007186629,0.00027188924,0.00093872333],"genre_scores_gemma":[0.41384548,0.0008118653,0.5812079,0.00020181152,0.000093673225,0.00014801629,0.00031829294,0.000097074415,0.003275868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928904,0.00018278576,0.00002310057,0.00012214387,0.00033717605,0.00004581845],"domain_scores_gemma":[0.9993113,0.00026738338,0.00013001371,0.00013840126,0.00011643288,0.000036593337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051166984,0.00089161936,0.00052317296,0.00055975124,0.00027273464,0.00089482876,0.00084791024,0.0007397943,0.0014592217],"category_scores_gemma":[0.0020738128,0.0005994847,0.00054406724,0.0006436528,0.0007472364,0.0017442802,0.0018695411,0.0011367812,0.00046858465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070744404,0.00014993123,0.0018849428,0.0004802162,0.00012700119,0.0003945768,0.00050830375,0.21988216,0.28416005,0.0555641,0.0032845752,0.4328567],"study_design_scores_gemma":[0.000038566108,0.0000967469,0.00071347994,0.000023505596,0.000017640123,0.000282619,0.000037967435,0.93057555,0.049500167,0.01627725,0.0023890163,0.00004748492],"about_ca_topic_score_codex":0.0010359628,"about_ca_topic_score_gemma":0.001522176,"teacher_disagreement_score":0.0014592217,"about_ca_system_score_codex":0.0003420739,"about_ca_system_score_gemma":0.00054712145,"threshold_uncertainty_score":0.004881561},"labels":[],"label_agreement":null},{"id":"W2596043685","doi":"10.15353/vsnl.v2i1.114","title":"A Bayesian Multi-Scale Framework for Photoplethysmogram Imaging Waveform Processing","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ministero dello Sviluppo Economico; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Ministry of Economic Development and Innovation","keywords":"Photoplethysmogram; Waveform; Computer science; Artificial intelligence; Signal processing; Fidelity; SIGNAL (programming language); Scale (ratio); Rendering (computer graphics); Computer vision; Pattern recognition (psychology); Telecommunications; Digital signal processing; Computer hardware","score_opus":0.009405833676186215,"score_gpt":0.27253607597578033,"score_spread":0.26313024229959414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596043685","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014559424,0.00035600993,0.99746156,0.00012655245,0.000019664943,0.000018277604,0.000057985202,0.0001283414,0.00037569986],"genre_scores_gemma":[0.21660508,0.0020242485,0.7722752,0.000357946,0.0003705607,0.0003196626,0.0009830167,0.00024746265,0.006816763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989195,0.00041739416,0.000055382,0.00022870135,0.00028002667,0.00009895155],"domain_scores_gemma":[0.99865496,0.00070741953,0.00015210002,0.00010774496,0.00029262935,0.000085029664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002476406,0.00097474887,0.0012284466,0.0009779931,0.0004930663,0.0015339376,0.0022471435,0.0017647687,0.0031420963],"category_scores_gemma":[0.0042192265,0.0008790721,0.0013495646,0.0012816045,0.00087690575,0.0012748458,0.0014561185,0.001899587,0.0011944653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001518281,0.00010271107,0.00088271796,0.00021383037,0.00017442807,0.00014542605,0.00011753903,0.7276462,0.005938027,0.052434467,0.0040319823,0.20816082],"study_design_scores_gemma":[0.0000062640975,0.000016298749,0.00013701523,0.000006680267,0.000009239654,0.000016373986,0.0000041864446,0.9924495,0.00018499816,0.006353772,0.00080615724,0.00000949536],"about_ca_topic_score_codex":0.017034821,"about_ca_topic_score_gemma":0.018391365,"teacher_disagreement_score":0.017034821,"about_ca_system_score_codex":0.0009506964,"about_ca_system_score_gemma":0.0016292614,"threshold_uncertainty_score":0.033871353},"labels":[],"label_agreement":null},{"id":"W2596307352","doi":"10.15353/vsnl.v2i1.115","title":"Computerized Enumeration and Bio-volume Estimation of the Cyanobacteria Anabaena flos-aquae","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Enumeration; Cyanobacteria; Anabaena; Volume (thermodynamics); Algae; Biological system; Microcystin; Cylindrospermopsin; Environmental science; Biology; Computer science; Ecology; Mathematics; Bacteria; Physics","score_opus":0.008836536396174797,"score_gpt":0.24512959786071473,"score_spread":0.23629306146453993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596307352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44196886,0.00059676444,0.5531315,0.00008674503,0.000029137054,0.00011058509,0.0003866884,0.0021964207,0.0014933603],"genre_scores_gemma":[0.63985354,0.00048200984,0.35737342,0.000039885545,0.000020119676,0.00016437365,0.00060128357,0.00007444477,0.0013909014],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968934,0.000026690133,0.000019249243,0.00009633621,0.0001481552,0.000020243957],"domain_scores_gemma":[0.9996786,0.00009324614,0.000073495474,0.000034217177,0.00010698474,0.00001338757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032446464,0.00044099367,0.0003372882,0.0012850601,0.0001834827,0.00039480286,0.0005242592,0.00039688795,0.0005427436],"category_scores_gemma":[0.0007384398,0.000301305,0.00022537616,0.00043992617,0.00021733463,0.000443782,0.0005395468,0.00024934518,0.0002342305],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010363101,0.00008485668,0.012402804,0.00017535173,0.000027301197,0.00011577778,0.00015708836,0.015542338,0.8155501,0.0004516521,0.00040069298,0.15498844],"study_design_scores_gemma":[0.000018541272,0.00024823262,0.04142609,0.000024940044,0.000046770696,0.0004194152,0.00011446901,0.58896023,0.3645997,0.0007508776,0.003320394,0.00007029542],"about_ca_topic_score_codex":0.0017939679,"about_ca_topic_score_gemma":0.0027271241,"teacher_disagreement_score":0.0017939679,"about_ca_system_score_codex":0.00026203063,"about_ca_system_score_gemma":0.00034521782,"threshold_uncertainty_score":0.0035670996},"labels":[],"label_agreement":null},{"id":"W2596624237","doi":"10.15353/vsnl.v2i1.87","title":"Automated enumeration and size distribution analysis of Microcystis aeruginosa via fluorescence imaging","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Enumeration; Microcystis aeruginosa; Microcystis; Biological system; Cyanobacteria; Algae; Microcystin; Computer science; Pattern recognition (psychology); Biology; Artificial intelligence; Environmental science; Mathematics; Ecology","score_opus":0.0026224848185245706,"score_gpt":0.25619403562213994,"score_spread":0.2535715508036154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596624237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45766866,0.0010326444,0.5345243,0.00016401833,0.000044784432,0.00013898857,0.00065906905,0.0035062344,0.002261248],"genre_scores_gemma":[0.5838083,0.0008058773,0.41219547,0.0001129904,0.00003457534,0.00017223263,0.0005450746,0.00014487986,0.0021806313],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995296,0.000031067917,0.000021085472,0.00014225386,0.00023662887,0.000039317674],"domain_scores_gemma":[0.99956065,0.000100170946,0.00009836527,0.000044887147,0.00017185125,0.000024082929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003153813,0.00048094068,0.00041668036,0.0010921296,0.00022017094,0.00036710384,0.00051232113,0.00044470158,0.00049044326],"category_scores_gemma":[0.0005852509,0.00024154555,0.0002805764,0.0003843958,0.00024301752,0.00043759547,0.0005016828,0.00034265782,0.00032489738],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003097799,0.000029781868,0.0031118684,0.00007832616,0.000010249905,0.000048152,0.00006280331,0.0010960798,0.95768654,0.00014844994,0.0002014044,0.037495334],"study_design_scores_gemma":[0.000007654754,0.00012550276,0.021322334,0.00001668887,0.000026750551,0.00031147455,0.00007435544,0.1080763,0.8674177,0.00030001515,0.0022641234,0.00005704151],"about_ca_topic_score_codex":0.0019756162,"about_ca_topic_score_gemma":0.004063002,"teacher_disagreement_score":0.0019756162,"about_ca_system_score_codex":0.0003608548,"about_ca_system_score_gemma":0.00035935547,"threshold_uncertainty_score":0.0039281845},"labels":[],"label_agreement":null},{"id":"W2597106449","doi":"10.15353/vsnl.v2i1.110","title":"Noise Suppression and Contrast Enhancement via Bayesian Residual Transform (BRT) in Low-Light Conditions","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Residual; Artificial intelligence; Noise (video); Noise reduction; Contrast enhancement; Bayesian probability; Computer vision; Computer science; Pattern recognition (psychology); Contrast (vision); Image enhancement; Image (mathematics); Algorithm","score_opus":0.006856640009819553,"score_gpt":0.2796495878825498,"score_spread":0.27279294787273023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597106449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039131135,0.0002942215,0.95855665,0.00016054131,0.00003071905,0.000030708885,0.00005436813,0.00045293666,0.0012887801],"genre_scores_gemma":[0.49903062,0.00080230803,0.49559614,0.00021913518,0.00006516528,0.00007228282,0.00034233605,0.00027430805,0.0035977603],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995535,0.0000798907,0.000020125619,0.0000842325,0.00021913437,0.000043115768],"domain_scores_gemma":[0.9995497,0.00015327368,0.00008031383,0.00006877132,0.000120666,0.000027194927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008338089,0.0008084687,0.00061749807,0.00048220326,0.00023594136,0.00057480065,0.0007425797,0.00080797466,0.0010078499],"category_scores_gemma":[0.0018540862,0.00031376537,0.00063921884,0.00036411942,0.0006778254,0.0012266267,0.0009399443,0.0010282046,0.00041871684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005753708,0.00024114957,0.0023629684,0.00043321442,0.00013323844,0.00031045824,0.0002589811,0.27670226,0.40986693,0.015614898,0.0024045613,0.29109594],"study_design_scores_gemma":[0.000019503208,0.00013057115,0.0012433365,0.000019182862,0.00003372038,0.00021745585,0.000025278963,0.9209735,0.071177654,0.0041979942,0.0019263951,0.000035330664],"about_ca_topic_score_codex":0.0021246013,"about_ca_topic_score_gemma":0.003263991,"teacher_disagreement_score":0.0021246013,"about_ca_system_score_codex":0.00031613596,"about_ca_system_score_gemma":0.0005675769,"threshold_uncertainty_score":0.004409671},"labels":[],"label_agreement":null},{"id":"W2597881733","doi":"10.15353/vsnl.v2i1.94","title":"Road Defect Detection in Street View Images using Texture Descriptors and Contour Maps","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Plan (archaeology); Support vector machine; Texture (cosmology); Contour line; Computer vision; Quality (philosophy); Pattern recognition (psychology); Transport engineering; Cartography; Geography; Engineering; Image (mathematics)","score_opus":0.005974248596023536,"score_gpt":0.232762559215345,"score_spread":0.22678831061932148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597881733","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63607407,0.0015289698,0.33295593,0.00028784547,0.00014261661,0.00041189766,0.009978592,0.0128906425,0.00572942],"genre_scores_gemma":[0.8079824,0.00088535046,0.17390937,0.000087540684,0.000054639015,0.0000898957,0.013737885,0.00036018065,0.002892839],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996137,0.000022096401,0.00001663625,0.00011208922,0.00015173946,0.0000837686],"domain_scores_gemma":[0.9994684,0.00006898267,0.00008403413,0.000109958804,0.0002149469,0.00005379533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033606944,0.0007525381,0.0006301092,0.004910797,0.00021261776,0.0011948245,0.00067210296,0.0008428567,0.0018259855],"category_scores_gemma":[0.00096344674,0.00031629513,0.00066446187,0.0019295748,0.00030274762,0.0009200843,0.0006336298,0.0005839546,0.0015356204],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084907876,0.0005658228,0.030472353,0.0005397855,0.00021232334,0.00092603895,0.00019784749,0.039709285,0.22048967,0.0011482011,0.018902557,0.68598694],"study_design_scores_gemma":[0.000046889218,0.00021941679,0.087302096,0.00007926563,0.00010475313,0.0012655677,0.00032205382,0.8104406,0.0905851,0.0014094791,0.008151984,0.000072796516],"about_ca_topic_score_codex":0.0076806517,"about_ca_topic_score_gemma":0.015656078,"teacher_disagreement_score":0.0076806517,"about_ca_system_score_codex":0.00037888382,"about_ca_system_score_gemma":0.00044740413,"threshold_uncertainty_score":0.015271902},"labels":[],"label_agreement":null},{"id":"W2598379557","doi":"10.15353/vsnl.v2i1.107","title":"Sparse Correlated Diffusion Imaging: A New Computational Diffusion MRI Modality for Prostate Cancer Detection","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto; Sunnybrook Health Science Centre","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Diffusion MRI; Modality (human–computer interaction); Prostate cancer; Magnetic resonance imaging; Diffusion; Computer science; Radiology; Artificial intelligence; Medicine; Cancer; Physics; Internal medicine","score_opus":0.028915644810776697,"score_gpt":0.34722755198007604,"score_spread":0.31831190716929936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598379557","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013289199,0.0010577613,0.9831693,0.0005178662,0.00006324758,0.00004831039,0.00017852538,0.000394247,0.0012815442],"genre_scores_gemma":[0.1893714,0.0023499154,0.80377346,0.000458664,0.00027079633,0.00013126365,0.00058682205,0.00011566941,0.0029420678],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997855,0.000049922637,0.000010529964,0.000040625077,0.000094882445,0.000018666044],"domain_scores_gemma":[0.9996791,0.000093734685,0.000056728233,0.00005655279,0.000081695754,0.00003215653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004324243,0.0005661759,0.00057941675,0.00077569543,0.00026110615,0.0006923909,0.0007290214,0.00076890783,0.0014411996],"category_scores_gemma":[0.0010461804,0.00026091206,0.00049571524,0.0008872933,0.0005514491,0.0010338392,0.00089873705,0.0007415801,0.00040049784],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032571628,0.00016431026,0.0025093479,0.00060402654,0.00017582449,0.000387895,0.00018461922,0.122752674,0.23017411,0.024840157,0.010969587,0.6069117],"study_design_scores_gemma":[0.000031820637,0.00014298687,0.0014158207,0.000029348757,0.00006090588,0.0009501011,0.000031905234,0.926876,0.04665117,0.008111647,0.015637288,0.00006091796],"about_ca_topic_score_codex":0.0008634474,"about_ca_topic_score_gemma":0.0018000596,"teacher_disagreement_score":0.0014411996,"about_ca_system_score_codex":0.00027243994,"about_ca_system_score_gemma":0.00062198204,"threshold_uncertainty_score":0.0048213005},"labels":[],"label_agreement":null},{"id":"W2598660286","doi":"10.15353/vsnl.v2i1.116","title":"Towards Global Localization Using Global Descriptors","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Viewpoints; Artificial intelligence; Computer science; Outlier; Representation (politics); Similarity (geometry); Matching (statistics); Variety (cybernetics); Object (grammar); Computer vision; Pattern recognition (psychology); Machine learning; Image (mathematics); Mathematics; Statistics","score_opus":0.010850481719173855,"score_gpt":0.25635291226211926,"score_spread":0.2455024305429454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598660286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016038893,0.00069240585,0.97034496,0.00012849759,0.000092878014,0.00007601846,0.00084562047,0.009600036,0.0021807216],"genre_scores_gemma":[0.26538673,0.0010886714,0.71627986,0.00030805322,0.0001421801,0.0002546686,0.007882494,0.0010018996,0.007655382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984622,0.00021872025,0.00007110131,0.000632485,0.0004465209,0.00016910936],"domain_scores_gemma":[0.9983917,0.00019113102,0.000199957,0.0007282259,0.0004182807,0.000070750866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014911274,0.0019940312,0.0022011003,0.0052499287,0.0008451886,0.0024139176,0.0021178497,0.0015929448,0.003491292],"category_scores_gemma":[0.0031279754,0.0006535436,0.0010196087,0.005715386,0.00086564734,0.0032595014,0.005431377,0.0015691029,0.005251426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003243052,0.0002075393,0.0037567234,0.00040318657,0.00015497764,0.00023869821,0.00039815353,0.025782548,0.03151011,0.01256237,0.022515584,0.9021458],"study_design_scores_gemma":[0.00022633087,0.00061911985,0.007068926,0.00020735506,0.00022377382,0.0011803141,0.0013230797,0.78014123,0.069791086,0.05402696,0.08501143,0.00018048887],"about_ca_topic_score_codex":0.00465272,"about_ca_topic_score_gemma":0.005661119,"teacher_disagreement_score":0.0052499287,"about_ca_system_score_codex":0.00067580485,"about_ca_system_score_gemma":0.0011546792,"threshold_uncertainty_score":0.01167953},"labels":[],"label_agreement":null},{"id":"W2599152240","doi":"10.15353/vsnl.v2i1.97","title":"Enhanced Smartphone Spectroscopy via High-throughput Computational Slit","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Spectroscopy and Laser Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Software portability; Spectrometer; Limiting; Throughput; Spectroscopy; Computer science; Spectral resolution; Signal-to-noise ratio (imaging); Noise (video); Slit; Optics; Materials science; Spectral line; Physics; Engineering; Telecommunications; Artificial intelligence","score_opus":0.00514103167597654,"score_gpt":0.2662149115890801,"score_spread":0.2610738799131036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2599152240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2461274,0.00091358414,0.7323445,0.00051696424,0.00015275016,0.00022466593,0.0012903719,0.009191549,0.009238165],"genre_scores_gemma":[0.4693354,0.00047711437,0.52615875,0.00016069514,0.00004352451,0.00021476773,0.0008404348,0.00024140919,0.002527912],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969566,0.00004090611,0.000013769147,0.00006242144,0.00016395433,0.000023222487],"domain_scores_gemma":[0.9994066,0.00023592425,0.000054740653,0.00011488089,0.00014842028,0.000039476978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038102936,0.00042462023,0.000511282,0.0003395314,0.00021239913,0.00054300885,0.0009523269,0.00066215603,0.0036074936],"category_scores_gemma":[0.00094682764,0.00025523215,0.0002871107,0.00034461214,0.00026555138,0.00090974005,0.0008513003,0.0005909033,0.0011562163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050916383,0.00022544374,0.0021806592,0.00056768133,0.000065869805,0.00049664336,0.00020379151,0.019079188,0.8818425,0.0064776363,0.004562468,0.08378892],"study_design_scores_gemma":[0.00012656947,0.00048309696,0.0036338232,0.000034507655,0.00003733609,0.000853749,0.000107530446,0.5555968,0.42042452,0.003086213,0.0155151645,0.0001006668],"about_ca_topic_score_codex":0.0006881019,"about_ca_topic_score_gemma":0.001993886,"teacher_disagreement_score":0.0036074936,"about_ca_system_score_codex":0.00033186786,"about_ca_system_score_gemma":0.00048599657,"threshold_uncertainty_score":0.012068212},"labels":[],"label_agreement":null},{"id":"W2599571388","doi":"10.15353/vsnl.v2i1.84","title":"Automated Failure Detection in Computer Vision Systems","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Detector; Artificial intelligence; Convolutional neural network; Artificial neural network; Computer vision; Pixel; Convolution (computer science); Scale (ratio); Machine vision; Deep learning; Pattern recognition (psychology); Real-time computing; Telecommunications","score_opus":0.00705675858498899,"score_gpt":0.2724328980608549,"score_spread":0.2653761394758659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2599571388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1162298,0.0048063225,0.8719519,0.0014894072,0.00013105295,0.00008646663,0.00020854152,0.0016160217,0.0034805068],"genre_scores_gemma":[0.938742,0.0010861991,0.056554507,0.00014355472,0.00009137014,0.000050133403,0.00019822965,0.000041703002,0.0030922738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983815,0.00041497644,0.00007954826,0.00039160758,0.0005603604,0.00017211717],"domain_scores_gemma":[0.9974425,0.0011532807,0.00034869416,0.00034495414,0.00064651185,0.00006405169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018960455,0.0008652119,0.0006423542,0.0013763078,0.0004922389,0.0012152907,0.0010342558,0.0016342229,0.0020475627],"category_scores_gemma":[0.0073182047,0.0005096627,0.000456528,0.001154737,0.0013322587,0.0021671124,0.0011930986,0.0014832725,0.0004748864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002423662,0.00015620477,0.00942625,0.0004017404,0.00011790168,0.00019948623,0.00018036315,0.56065106,0.008595136,0.032946277,0.0072900346,0.37979323],"study_design_scores_gemma":[0.000006389096,0.00003587924,0.0016316748,0.000018569894,0.0000071390623,0.000055313245,0.000016594544,0.97011065,0.003104287,0.023758117,0.001242131,0.000013361344],"about_ca_topic_score_codex":0.010801715,"about_ca_topic_score_gemma":0.005707847,"teacher_disagreement_score":0.010801715,"about_ca_system_score_codex":0.0022202537,"about_ca_system_score_gemma":0.0009699173,"threshold_uncertainty_score":0.02147764},"labels":[],"label_agreement":null},{"id":"W2600555690","doi":"10.15353/vsnl.v2i1.106","title":"StochasticNet in StochasticNet","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Computer science; Deep neural networks; Deep learning; Artificial neural network; Artificial intelligence; Convolutional neural network; Graph; Machine learning; Theoretical computer science","score_opus":0.009665562301139371,"score_gpt":0.2787130577608325,"score_spread":0.26904749545969314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600555690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009705077,0.0018287841,0.95028174,0.0018097722,0.0012519767,0.000125241,0.0017214549,0.0070635504,0.02621248],"genre_scores_gemma":[0.342731,0.0037882675,0.5895241,0.0028749963,0.0011487736,0.00076860376,0.0085737165,0.0027523683,0.047838196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993431,0.0001458407,0.000043643904,0.00018168458,0.00022149475,0.00006431557],"domain_scores_gemma":[0.9996111,0.000106673215,0.00004811216,0.00009831199,0.00009151616,0.000044259938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007566671,0.0009361195,0.0006455025,0.000538531,0.00038559388,0.0014663697,0.0011620766,0.0010209286,0.014736359],"category_scores_gemma":[0.0018321745,0.00047698763,0.00082953786,0.0005827498,0.0010301204,0.0017833996,0.0015282859,0.0020845414,0.004408876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002277558,0.00012566906,0.0020609251,0.000526833,0.0001545113,0.00033598547,0.00010849167,0.2953736,0.012031707,0.42114466,0.05852268,0.20938717],"study_design_scores_gemma":[0.000034828467,0.00008312284,0.00044270896,0.000069717375,0.000029708173,0.00020567393,0.00001877513,0.7493978,0.007170959,0.14210075,0.10041074,0.00003518263],"about_ca_topic_score_codex":0.004405842,"about_ca_topic_score_gemma":0.008370814,"teacher_disagreement_score":0.014736359,"about_ca_system_score_codex":0.0009542229,"about_ca_system_score_gemma":0.0013721511,"threshold_uncertainty_score":0.04929799},"labels":[],"label_agreement":null},{"id":"W2600720017","doi":"10.15353/vsnl.v2i1.113","title":"A Local ROI-specific Atlas-based Segmentation of Prostate Gland and Transitional Zone in Diffusion MRI","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Segmentation; Atlas (anatomy); Computer science; Prostate; Prostate cancer; Sørensen–Dice coefficient; Magnetic resonance imaging; Artificial intelligence; Effective diffusion coefficient; Prostate gland; Region of interest; Computer vision; Image segmentation; Region growing; Medicine; Anatomy; Radiology; Scale-space segmentation; Cancer","score_opus":0.007047932377798291,"score_gpt":0.26023730487546815,"score_spread":0.2531893724976699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600720017","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028916016,0.00046476498,0.9674364,0.00007916138,0.000022806109,0.0001081593,0.00012620322,0.0021447702,0.0007017856],"genre_scores_gemma":[0.23701195,0.0005765585,0.7595532,0.00011396175,0.00004144611,0.00016320003,0.00044751598,0.0004927791,0.0015993826],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994271,0.00013134048,0.00004557653,0.0001475683,0.00019700322,0.000051337578],"domain_scores_gemma":[0.9994259,0.00015240599,0.00009808533,0.00015342345,0.00012977551,0.00004049305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008370592,0.0006528153,0.0005960563,0.0016395827,0.0004348771,0.00091897126,0.00091995246,0.00086851587,0.0011851985],"category_scores_gemma":[0.0019112333,0.00053452596,0.0009909132,0.001005607,0.00051673996,0.00090398325,0.0010482456,0.00065711327,0.00077796547],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041532607,0.000118473115,0.007079469,0.00051304634,0.00021182932,0.00046832723,0.00055300153,0.06564509,0.3054449,0.0066159368,0.0034802167,0.60945445],"study_design_scores_gemma":[0.000062611594,0.000438937,0.01277696,0.00006943121,0.00025269995,0.0035799139,0.00022312412,0.69603795,0.26632577,0.0058567203,0.014176968,0.00019898172],"about_ca_topic_score_codex":0.002662719,"about_ca_topic_score_gemma":0.0046780133,"teacher_disagreement_score":0.002662719,"about_ca_system_score_codex":0.00037844013,"about_ca_system_score_gemma":0.0012693094,"threshold_uncertainty_score":0.005294442},"labels":[],"label_agreement":null},{"id":"W2601019217","doi":"10.15353/vsnl.v2i1.104","title":"IR Shape From Shading Enhanced RGBD for 3D Scanning","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Computer graphics (images); Shading","score_opus":0.012538906120371248,"score_gpt":0.29724735053137424,"score_spread":0.284708444411003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2601019217","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045638336,0.0004962075,0.9700168,0.00016608319,0.0001060018,0.00007667304,0.0008320692,0.008071065,0.015671303],"genre_scores_gemma":[0.1284821,0.0011217713,0.8492686,0.0004023254,0.000059992628,0.00014786594,0.0029377767,0.0030014045,0.014578191],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99951005,0.0000385638,0.000011326098,0.00004264351,0.0003648664,0.000032614196],"domain_scores_gemma":[0.9997447,0.000034032633,0.000014828081,0.000118979544,0.00007415907,0.000013406253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020599464,0.00081454596,0.00039611122,0.000602148,0.0002041609,0.0009177598,0.0009725536,0.0004958246,0.023631943],"category_scores_gemma":[0.00061920186,0.00044858683,0.0007555053,0.0008481381,0.00031588983,0.0006770445,0.0012575549,0.0008851328,0.008947916],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021723998,0.00012356872,0.00102718,0.0006059862,0.00007455933,0.0002842239,0.00026304845,0.049762793,0.2310926,0.028272273,0.0667205,0.62155604],"study_design_scores_gemma":[0.000053826618,0.00011869653,0.0024105327,0.00013336427,0.00003828143,0.0011938342,0.000096542775,0.56424403,0.1727578,0.019070672,0.23975292,0.00012951417],"about_ca_topic_score_codex":0.0014320865,"about_ca_topic_score_gemma":0.003960265,"teacher_disagreement_score":0.023631943,"about_ca_system_score_codex":0.00040003902,"about_ca_system_score_gemma":0.00043152395,"threshold_uncertainty_score":0.07905668},"labels":[],"label_agreement":null},{"id":"W2601473526","doi":"10.15353/vsnl.v2i1.103","title":"Modout: Learning to Fuse Modalities via Stochastic Regularization","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Agence Nationale de la Recherche","keywords":"Regularization (linguistics); Modalities; Artificial intelligence; Computer science; Fuse (electrical); Dropout (neural networks); Modal; Machine learning; Equivalence (formal languages); Pattern recognition (psychology); Mathematics; Engineering","score_opus":0.00912133655418103,"score_gpt":0.25125430765872875,"score_spread":0.2421329711045477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2601473526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012411998,0.00031709988,0.9817192,0.00015670688,0.00005390366,0.00008310046,0.00016975983,0.004204025,0.0008841637],"genre_scores_gemma":[0.35273433,0.00045397665,0.634798,0.00060128403,0.00012089585,0.00042020064,0.0017131887,0.0009326551,0.008225556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942446,0.00016466096,0.000023501281,0.00016396317,0.0001408115,0.00008261216],"domain_scores_gemma":[0.999597,0.00014358574,0.000048341397,0.000097948345,0.00007200821,0.000041062358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021721274,0.0024775453,0.0014292792,0.0007641701,0.0006302137,0.0008430499,0.002561843,0.0017497586,0.0038147077],"category_scores_gemma":[0.0023700353,0.00083601545,0.0015247836,0.0006410048,0.00086407166,0.0019134368,0.0029305138,0.0021407523,0.0009854743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044929184,0.00023165022,0.0018367382,0.00018807114,0.0003598126,0.00020157103,0.00016081314,0.4015681,0.021077396,0.010182638,0.01795539,0.54578847],"study_design_scores_gemma":[0.00002170775,0.000056510366,0.00014652949,0.000009554758,0.000017355685,0.000041874122,0.000010223628,0.9883605,0.005147664,0.005043946,0.0011342793,0.000009911954],"about_ca_topic_score_codex":0.0054010516,"about_ca_topic_score_gemma":0.00839356,"teacher_disagreement_score":0.0054010516,"about_ca_system_score_codex":0.000871484,"about_ca_system_score_gemma":0.0014373707,"threshold_uncertainty_score":0.012761414},"labels":[],"label_agreement":null},{"id":"W2601645953","doi":"10.15353/vsnl.v2i1.101","title":"Spatial Detection of Vehicles in Images using Convolutional Neural Networks and Stereo Matching","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Matching (statistics); Computer science; Pixel; Computer vision; Pattern recognition (psychology); Artificial neural network; RADIUS; Mathematics; Statistics","score_opus":0.00561190050870166,"score_gpt":0.22326729665161882,"score_spread":0.21765539614291715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2601645953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08866675,0.000523049,0.90608263,0.00014358247,0.00005098498,0.000055764333,0.00027602597,0.0016989071,0.002502318],"genre_scores_gemma":[0.74504626,0.00040378977,0.2500941,0.0001248996,0.000051743755,0.000059046353,0.00064218877,0.000078371035,0.003499517],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995158,0.000044575434,0.000019635583,0.00013611584,0.00019454992,0.00008932963],"domain_scores_gemma":[0.9996778,0.000058771933,0.00007122386,0.000062737585,0.00010934806,0.00002012432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004553732,0.0006699775,0.00048556182,0.00195722,0.00029035538,0.0007595218,0.0010004558,0.0006904803,0.0013916078],"category_scores_gemma":[0.0012539738,0.0005479771,0.00060107897,0.0014526781,0.00036528884,0.0010100319,0.0010608374,0.0005216388,0.00054374116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030407647,0.00020841548,0.005378778,0.00012025122,0.0001397487,0.00015390651,0.000080257436,0.16934893,0.074613,0.006448104,0.0025474718,0.7406571],"study_design_scores_gemma":[0.0000068745103,0.000026125306,0.002144137,0.000008348087,0.000015822468,0.00004809218,0.000015590867,0.9756708,0.018864237,0.0023727324,0.0008186422,0.0000086590735],"about_ca_topic_score_codex":0.012986952,"about_ca_topic_score_gemma":0.02064877,"teacher_disagreement_score":0.012986952,"about_ca_system_score_codex":0.0011021118,"about_ca_system_score_gemma":0.0008861683,"threshold_uncertainty_score":0.025822759},"labels":[],"label_agreement":null},{"id":"W2601810895","doi":"10.15353/vsnl.v2i1.117","title":"Improved OCT Human Corneal segmentation Using Bayesian Residual Transform","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Segmentation; Speckle noise; Residual; Computer vision; Computer science; Speckle pattern; Optical coherence tomography; Noise (video); Optics; Image (mathematics); Algorithm; Physics","score_opus":0.011343117448895731,"score_gpt":0.2761726767459537,"score_spread":0.264829559297058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2601810895","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03455273,0.00039584763,0.9626533,0.00011122141,0.00002556451,0.000044239972,0.00008359289,0.0011872547,0.00094626634],"genre_scores_gemma":[0.3434574,0.00048608234,0.65235204,0.00011920312,0.00004189043,0.000076485594,0.00047213893,0.0003172336,0.0026776274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995431,0.00008833697,0.000023638924,0.00010283137,0.00019046261,0.000051741834],"domain_scores_gemma":[0.9995473,0.00012679253,0.00006625398,0.000082201914,0.00015185415,0.000025583999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051539805,0.0006964105,0.0006079128,0.0012771367,0.0003024075,0.0008908393,0.0006858269,0.0008860417,0.0013837024],"category_scores_gemma":[0.0010538131,0.0005048234,0.00075984176,0.0007148021,0.0003205124,0.00070322346,0.00065239606,0.0004976485,0.0006830931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062143063,0.00013577622,0.0018236003,0.0002786734,0.00010047388,0.0002943835,0.00022892875,0.17460015,0.25946733,0.0038967784,0.0039749714,0.5545776],"study_design_scores_gemma":[0.000013654848,0.00003776367,0.0007670941,0.000008779834,0.000016923252,0.00015420579,0.000015270582,0.97155994,0.025617138,0.00070311525,0.0010857355,0.000020391311],"about_ca_topic_score_codex":0.0064265192,"about_ca_topic_score_gemma":0.0070843603,"teacher_disagreement_score":0.0064265192,"about_ca_system_score_codex":0.00044677185,"about_ca_system_score_gemma":0.0010223746,"threshold_uncertainty_score":0.012778223},"labels":[],"label_agreement":null},{"id":"W2602414472","doi":"10.15353/vsnl.v2i1.109","title":"Scaled Monocular Visual SLAM","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Monocular; Focus (optics); Computer vision; Artificial intelligence; Scale (ratio); Computer science; Metric (unit); Simultaneous localization and mapping; Motion (physics); Geography; Robot; Mobile robot; Engineering; Cartography; Optics; Physics","score_opus":0.003979125726595468,"score_gpt":0.22738409005493987,"score_spread":0.2234049643283444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602414472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02104367,0.0010163318,0.9615838,0.00021491597,0.0004844797,0.00008734852,0.0005701415,0.0030826393,0.011916666],"genre_scores_gemma":[0.7291426,0.0008285753,0.25817516,0.00044236897,0.0001891966,0.00018456446,0.000977634,0.00028562275,0.0097742295],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920386,0.00012836467,0.000030562816,0.00023404883,0.0003189068,0.000084258696],"domain_scores_gemma":[0.9995449,0.00006104793,0.00004194111,0.00017714204,0.00014602215,0.000029004586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033153992,0.0010453067,0.00072505616,0.0004228193,0.00030280795,0.0008939723,0.0008969034,0.000656641,0.007420927],"category_scores_gemma":[0.001724601,0.00038509117,0.0003855178,0.00072511076,0.0005133489,0.0012532742,0.0019191399,0.0006805086,0.0018033131],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046025065,0.00009364517,0.0010843265,0.0006221374,0.0001300989,0.00028250614,0.00021339522,0.1885968,0.104488015,0.02241907,0.020556485,0.66105324],"study_design_scores_gemma":[0.00009695762,0.0003273731,0.0029973378,0.000060361202,0.000034439745,0.00042654196,0.0001072197,0.908079,0.028842054,0.026621021,0.03233529,0.00007231776],"about_ca_topic_score_codex":0.0026384888,"about_ca_topic_score_gemma":0.0028707518,"teacher_disagreement_score":0.007420927,"about_ca_system_score_codex":0.00034406508,"about_ca_system_score_gemma":0.00052797864,"threshold_uncertainty_score":0.024825454},"labels":[],"label_agreement":null},{"id":"W2602840142","doi":"10.15353/vsnl.v2i1.108","title":"Bayesian Compensated Microscopy","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Microscopy; Visualization; Bayesian probability; Virtual microscopy; Artificial intelligence; Probabilistic logic; Computer science; Atomic force microscopy; Materials science; Nanotechnology; Pathology; Medicine","score_opus":0.004010305378086857,"score_gpt":0.2802961613249916,"score_spread":0.27628585594690475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602840142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035985743,0.00026782518,0.9934335,0.00020431318,0.000041790314,0.000026618594,0.00007528191,0.00041432786,0.0019377966],"genre_scores_gemma":[0.16752957,0.0005708646,0.82394487,0.00033714224,0.00009332404,0.00009326967,0.00030210847,0.00017262381,0.0069562327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991398,0.00015323245,0.000022486483,0.00012032103,0.0004937652,0.000070298185],"domain_scores_gemma":[0.99918956,0.00018907958,0.000117144504,0.00015433213,0.00029572126,0.00005421495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007583314,0.0008384558,0.00061968365,0.0007274803,0.00042136558,0.0008125846,0.0016242972,0.0014293343,0.0037625993],"category_scores_gemma":[0.0021357548,0.00046003988,0.0005629988,0.0006303486,0.00072098954,0.0012183732,0.001223317,0.0009764108,0.0011457687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029758798,0.000110077206,0.0013582155,0.00053581723,0.000119731565,0.00028480715,0.00014747585,0.21889466,0.28185546,0.08258794,0.010921423,0.4028868],"study_design_scores_gemma":[0.000016230311,0.000058197194,0.0006161995,0.000019149733,0.000017142169,0.00026237447,0.000009348324,0.9452449,0.03453225,0.00959071,0.009577977,0.000055600987],"about_ca_topic_score_codex":0.0032451598,"about_ca_topic_score_gemma":0.0046873563,"teacher_disagreement_score":0.0037625993,"about_ca_system_score_codex":0.001090727,"about_ca_system_score_gemma":0.0013454669,"threshold_uncertainty_score":0.01258719},"labels":[],"label_agreement":null},{"id":"W2603790773","doi":"10.15353/vsnl.v2i1.100","title":"Automated Histological Analysis System for Quantifying Microstructural Damage Accumulation to the Annulus Fibrosus","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Cervical Cancer and HPV Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Digital image analysis; Annulus (botany); Histology; Segmentation; Image segmentation; Biomedical engineering; Gaussian; Computer science; Digital pathology; Artificial intelligence; Materials science; Digital image; Biological system; Pattern recognition (psychology); Image (mathematics); Computer vision; Image processing; Composite material; Biology; Pathology; Physics; Engineering; Medicine","score_opus":0.057737866331438514,"score_gpt":0.4128584330551147,"score_spread":0.3551205667236762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603790773","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030341363,0.0003232288,0.96226704,0.00011765768,0.000063194086,0.00015408733,0.00032409662,0.0056981007,0.00071125355],"genre_scores_gemma":[0.1614584,0.0003857824,0.83296233,0.00017632985,0.000066265544,0.0003716563,0.0009158649,0.00048564,0.0031776263],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920243,0.0001100719,0.000052965148,0.00017951727,0.00039599382,0.000059088583],"domain_scores_gemma":[0.9982755,0.0002488607,0.00021506127,0.00027504447,0.00091557374,0.00006990092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011025292,0.00086021965,0.00065337174,0.0025431376,0.0005843,0.0008504551,0.001107351,0.0010830449,0.00280424],"category_scores_gemma":[0.001601164,0.0005848146,0.00068035396,0.0006374679,0.00034595764,0.0007483573,0.0005701513,0.00053466,0.0012730167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023110224,0.00018620997,0.010568283,0.00040333284,0.0001889581,0.00028401188,0.00016472967,0.024566092,0.49836066,0.002524077,0.0054951673,0.45702735],"study_design_scores_gemma":[0.00005103,0.00042349685,0.038022265,0.0000785863,0.00017733559,0.0013279417,0.00015074453,0.63722277,0.30327106,0.0038309712,0.015262637,0.00018118382],"about_ca_topic_score_codex":0.0031010455,"about_ca_topic_score_gemma":0.0054224078,"teacher_disagreement_score":0.0031010455,"about_ca_system_score_codex":0.000762844,"about_ca_system_score_gemma":0.0013382642,"threshold_uncertainty_score":0.009381175},"labels":[],"label_agreement":null},{"id":"W2603904625","doi":"10.15353/vsnl.v2i1.111","title":"Co-integrating thermal and hemodynamic imaging for physiological monitoring","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; AGE-WELL","keywords":"Infrared; Popularity; Biomedical engineering; Computer science; Hemodynamics; Thermal; Medicine; Cardiology; Optics; Psychology; Physics","score_opus":0.010405932401461622,"score_gpt":0.26873867724759,"score_spread":0.25833274484612834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603904625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35516065,0.0043543824,0.63496894,0.00042876648,0.00029839922,0.00018241508,0.00008264559,0.001302128,0.0032216518],"genre_scores_gemma":[0.8417824,0.001037391,0.15443586,0.0002508519,0.00015930101,0.00012350631,0.000057867812,0.00010465664,0.0020483576],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994259,0.00011903227,0.00002783583,0.00013168738,0.00024008016,0.00005541441],"domain_scores_gemma":[0.99938166,0.00027829013,0.00009057213,0.000066866865,0.00013752011,0.0000451005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006131486,0.0005975671,0.00044850327,0.00047224632,0.00013270478,0.00070148613,0.0005209126,0.0007189959,0.0012234377],"category_scores_gemma":[0.0013373456,0.00036114856,0.0002712498,0.0002898075,0.0003009559,0.0009697214,0.0011801538,0.00051709916,0.0003622361],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020001858,0.0001300011,0.0017872667,0.00018230961,0.00004456332,0.00018113929,0.00006929638,0.0033070445,0.91446036,0.00035954724,0.00035824644,0.07892014],"study_design_scores_gemma":[0.000044746343,0.0009636728,0.010960211,0.00004600812,0.00015714223,0.0011738215,0.0000752565,0.18477935,0.7951562,0.00094279053,0.005623982,0.000076913944],"about_ca_topic_score_codex":0.0002181418,"about_ca_topic_score_gemma":0.00061851,"teacher_disagreement_score":0.0012234377,"about_ca_system_score_codex":0.00017430302,"about_ca_system_score_gemma":0.00024689626,"threshold_uncertainty_score":0.0040928125},"labels":[],"label_agreement":null},{"id":"W2603920775","doi":"10.15353/vsnl.v2i1.89","title":"Commanding Wheelchair in Virtual Reality with Thoughts by Multiclass BCI based on Movement-related Cortical Potentials","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Brain–computer interface; Wheelchair; Virtual reality; Interface (matter); Computer science; Bridge (graph theory); Human–computer interaction; Motor imagery; Simulation; Electroencephalography; Psychology; Neuroscience","score_opus":0.014529120895866951,"score_gpt":0.2851435387499312,"score_spread":0.27061441785406426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603920775","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77908576,0.0015319014,0.21101527,0.00027674215,0.00014789213,0.00030647966,0.00043042697,0.0015518507,0.005653732],"genre_scores_gemma":[0.97247577,0.00038129443,0.02578397,0.00008526226,0.000018514056,0.000058959893,0.00008307702,0.000041114265,0.0010719766],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998604,0.00003586739,0.0000075375183,0.000027036534,0.00005006961,0.00001915359],"domain_scores_gemma":[0.9998079,0.000079059326,0.000026478121,0.000020821444,0.000047445796,0.000018321734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023770166,0.0004116234,0.00015457651,0.00020972406,0.0001127591,0.00041027885,0.00030933888,0.00023207685,0.0018469109],"category_scores_gemma":[0.0010512812,0.0000904844,0.00010803802,0.00012968833,0.00021212651,0.0002798148,0.00029722278,0.0002305542,0.0002677678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005255705,0.00020278651,0.0043327203,0.00030672192,0.00006861281,0.00037669632,0.00029301457,0.002956392,0.7098553,0.0008413688,0.0015046385,0.27873608],"study_design_scores_gemma":[0.000397623,0.003027512,0.20151058,0.00019911843,0.00050674187,0.006116064,0.0005329498,0.1524363,0.60946316,0.005547944,0.020025095,0.00023691052],"about_ca_topic_score_codex":0.0014365955,"about_ca_topic_score_gemma":0.002885461,"teacher_disagreement_score":0.0018469109,"about_ca_system_score_codex":0.00012180096,"about_ca_system_score_gemma":0.0002064459,"threshold_uncertainty_score":0.0061784983},"labels":[],"label_agreement":null},{"id":"W2617612337","doi":"10.15353/vsnl.v2i1.112","title":"Compact, Field-Portable Smartphone Chiral Molecule Concentration Estimation System via Multi-sensor Computational Polarimetry","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Polarimetry; Polarizer; Computer science; Computational complexity theory; Optics; Physics; Algorithm","score_opus":0.005832929198390765,"score_gpt":0.23503852097832656,"score_spread":0.2292055917799358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617612337","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12736507,0.0015709433,0.85707605,0.0006363537,0.00037795983,0.0004667155,0.0006412278,0.006124323,0.0057414076],"genre_scores_gemma":[0.5253981,0.0010272622,0.46448457,0.00052824244,0.00016537655,0.00038273653,0.00063906057,0.00014497533,0.007229709],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995455,0.000050762672,0.000022423712,0.00010581382,0.00024654146,0.000028967645],"domain_scores_gemma":[0.99961144,0.000079124184,0.000053989403,0.00006514553,0.00015793667,0.000032498494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040172564,0.0006683892,0.00074062176,0.0005178037,0.00025898486,0.0005991757,0.001162834,0.00067838054,0.0032448373],"category_scores_gemma":[0.00064409093,0.0003597262,0.0002992983,0.0002780511,0.00025663938,0.0010496896,0.00093100505,0.0005439389,0.0014201399],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042064665,0.00016414879,0.0025327147,0.00054400903,0.000047563684,0.00044506136,0.00017192969,0.0044542933,0.80718976,0.0022524626,0.004940893,0.17683657],"study_design_scores_gemma":[0.00010338232,0.00070737716,0.004353038,0.00005572114,0.000086983855,0.0017987237,0.0001180115,0.27377903,0.6916077,0.0010587466,0.026130613,0.00020063733],"about_ca_topic_score_codex":0.00070232403,"about_ca_topic_score_gemma":0.001021385,"teacher_disagreement_score":0.0032448373,"about_ca_system_score_codex":0.00028116314,"about_ca_system_score_gemma":0.0003840668,"threshold_uncertainty_score":0.010855079},"labels":[],"label_agreement":null},{"id":"W2753913847","doi":"10.15353/vsnl.v3i1.166","title":"Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction","year":2017,"lang":"en","type":"preprint","venue":"Journal of Computational Vision and Imaging Systems","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph; Vector Institute; Canadian Institute for Advanced Research; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Interpretability; Deep learning; Artificial intelligence; Machine learning; Computer science; Financial market; Stock market; Stock (firearms); Finance; Economics; Engineering","score_opus":0.02321433490793953,"score_gpt":0.3150460745439577,"score_spread":0.2918317396360182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753913847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043308113,0.0005708994,0.9405242,0.0021532455,0.00018998001,0.000070496426,0.00050054165,0.0067534023,0.0059290933],"genre_scores_gemma":[0.62076366,0.00063992344,0.371559,0.000640302,0.00012385321,0.0001488538,0.00056452665,0.00085112767,0.004708768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999821,0.00006937978,0.00000820303,0.000042014835,0.000035631423,0.000023711389],"domain_scores_gemma":[0.9990582,0.00051352946,0.00010591596,0.0001486664,0.00009757268,0.00007616773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071389024,0.0009352151,0.0003417745,0.0006832256,0.00034918217,0.001847034,0.001111889,0.0014084546,0.008115354],"category_scores_gemma":[0.0031790833,0.00035210312,0.0008351066,0.00030900433,0.00092818623,0.0022764564,0.002287379,0.0024872313,0.0006253521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009891702,0.00033607337,0.0051894845,0.0006584115,0.00018807597,0.0007480843,0.0022339371,0.38227147,0.069735095,0.16445425,0.032270897,0.34092495],"study_design_scores_gemma":[0.00003412369,0.00006767693,0.0008718744,0.000046786787,0.000022155802,0.000095235184,0.00009846558,0.91328454,0.012269103,0.06555212,0.0076243537,0.00003359987],"about_ca_topic_score_codex":0.0019577714,"about_ca_topic_score_gemma":0.0023610834,"teacher_disagreement_score":0.008115354,"about_ca_system_score_codex":0.00052979426,"about_ca_system_score_gemma":0.0005389577,"threshold_uncertainty_score":0.027148545},"labels":[],"label_agreement":null},{"id":"W2757138395","doi":"10.15353/vsnl.v3i1.177","title":"Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection","year":2017,"lang":"en","type":"preprint","venue":"Journal of Computational Vision and Imaging Systems","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Nvidia","keywords":"Radiomics; Skin cancer; Basal cell carcinoma; Cancer; Medicine; Cancer detection; Computer science; Artificial intelligence; Basal cell; Pathology; Internal medicine","score_opus":0.019307027257483452,"score_gpt":0.32198936635384046,"score_spread":0.302682339096357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757138395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08190075,0.002423383,0.9081749,0.0005327783,0.00013681834,0.000114203875,0.00079165923,0.0035986041,0.0023269323],"genre_scores_gemma":[0.6057105,0.0013273495,0.382729,0.00097826,0.00013426396,0.00022010923,0.002558827,0.00028197942,0.0060598343],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955124,0.0001076374,0.000021765769,0.00012462947,0.00013114739,0.00006349826],"domain_scores_gemma":[0.9995327,0.00018364415,0.00008802288,0.000057217312,0.00010164722,0.000036762893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056489086,0.0010621424,0.00084901747,0.00077114074,0.00027553592,0.00073274947,0.00082939793,0.0008984203,0.0015989832],"category_scores_gemma":[0.0016242424,0.0003506789,0.0006036838,0.0005563139,0.000576609,0.00078470487,0.0007293692,0.0010503578,0.0008060479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011293035,0.0003602507,0.0052869334,0.0005505203,0.00018700454,0.000736594,0.00021035233,0.21013589,0.27962282,0.011965366,0.012513696,0.4773014],"study_design_scores_gemma":[0.000028796638,0.00021291274,0.0006710688,0.0000154122,0.0000408238,0.0002489164,0.00003551315,0.9269732,0.06114922,0.0065950057,0.003990515,0.00003849853],"about_ca_topic_score_codex":0.0020803378,"about_ca_topic_score_gemma":0.0038610133,"teacher_disagreement_score":0.0020803378,"about_ca_system_score_codex":0.00049468054,"about_ca_system_score_gemma":0.0008039411,"threshold_uncertainty_score":0.0053491592},"labels":[],"label_agreement":null},{"id":"W2771070736","doi":"10.15353/vsnl.v3i1.161","title":"Polyploidism in Deep Neural Networks: m-Parent Evolutionary Synthesis of Deep Neural Networks in Varying Population Sizes","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"MNIST database; Artificial neural network; Artificial intelligence; Modern evolutionary synthesis; Population; Deep neural networks; Computer science; Evolutionary algorithm; Evolutionary acquisition of neural topologies; Biology; Evolutionary biology; Time delay neural network; Demography","score_opus":0.011659559114029314,"score_gpt":0.27425315886240553,"score_spread":0.2625935997483762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771070736","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7787293,0.00041572106,0.21596724,0.00027177014,0.000061791536,0.00006755458,0.00010011198,0.0004071491,0.0039793723],"genre_scores_gemma":[0.92542934,0.00012659881,0.072530374,0.000088800116,0.000005789285,0.000092408154,0.00009723623,0.000055186694,0.0015743409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981815,0.00005418334,0.00001086269,0.000047604917,0.00004446539,0.000024752659],"domain_scores_gemma":[0.9989728,0.00062419387,0.0001048559,0.00012644078,0.000124154,0.00004755659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000716174,0.00036892202,0.00031514262,0.00028793255,0.00025966825,0.00041124804,0.0005261776,0.00060034584,0.0013431585],"category_scores_gemma":[0.0036390645,0.00022427028,0.00029055338,0.00022985577,0.00044959824,0.0008130168,0.00057275535,0.000619696,0.0001461803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039543564,0.00022355614,0.0048461366,0.00019166384,0.00010075153,0.0005390821,0.0003522972,0.64016,0.17782257,0.024078917,0.0010150452,0.15027453],"study_design_scores_gemma":[0.000040961615,0.00025409678,0.00082179543,0.000011501688,0.000026279062,0.00010480904,0.000053447664,0.945613,0.046491254,0.0046533444,0.001916111,0.000013404276],"about_ca_topic_score_codex":0.0006230281,"about_ca_topic_score_gemma":0.0011515552,"teacher_disagreement_score":0.0013431585,"about_ca_system_score_codex":0.0006193125,"about_ca_system_score_gemma":0.00028729034,"threshold_uncertainty_score":0.0044934154},"labels":[],"label_agreement":null},{"id":"W2771573249","doi":"10.15353/vsnl.v3i1.182","title":"Automated Screening for Diabetic Retinopathy Using Compact Deep Networks","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Nvidia","keywords":"Diabetic retinopathy; Blindness; Retinopathy; Medicine; Fundus (uterus); Diabetes mellitus; Optometry; Retinal; Retina; Ophthalmology; Intensive care medicine; Computer science; Neuroscience; Psychology; Endocrinology","score_opus":0.02637068858273546,"score_gpt":0.3566245915677359,"score_spread":0.33025390298500046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771573249","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65511644,0.0024829085,0.32913294,0.0014724445,0.00022921633,0.00010914186,0.0011418433,0.004954887,0.005360204],"genre_scores_gemma":[0.9562965,0.00030432013,0.040354077,0.00026427567,0.000042197615,0.00002819467,0.0006834221,0.000035974146,0.0019909875],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979883,0.00004071669,0.000010440608,0.000059470276,0.000048007245,0.000042528696],"domain_scores_gemma":[0.9995123,0.00023998095,0.000057467314,0.000052188483,0.00011013817,0.000027980954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053631165,0.0006079942,0.0003737306,0.0006228425,0.0002416218,0.00051301066,0.0006998365,0.00056713563,0.0014039181],"category_scores_gemma":[0.0017971805,0.0002855653,0.00042383667,0.0003567371,0.0002055473,0.0007446951,0.0005843878,0.0006418738,0.00028340038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011087334,0.00071001175,0.016745439,0.00017167804,0.00030210483,0.0004228432,0.00010919204,0.38286096,0.029471254,0.0017723,0.00845158,0.557874],"study_design_scores_gemma":[0.000010251137,0.000053717544,0.0016229976,0.000008670986,0.000017857545,0.00004174492,0.000011401504,0.99422425,0.003092769,0.00063602155,0.00027402624,0.0000062789145],"about_ca_topic_score_codex":0.011574187,"about_ca_topic_score_gemma":0.014204761,"teacher_disagreement_score":0.011574187,"about_ca_system_score_codex":0.00074966147,"about_ca_system_score_gemma":0.00046881803,"threshold_uncertainty_score":0.023013592},"labels":[],"label_agreement":null},{"id":"W2771668473","doi":"10.15353/vsnl.v3i1.178","title":"Hyperspectral image classification using deep convolutional neural networks","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Artificial intelligence; Pixel; Convolutional neural network; Pattern recognition (psychology); Computer science; Deep learning; Curse of dimensionality; Process (computing); Feature (linguistics); Spectral signature; Remote sensing; Geography","score_opus":0.021865555687465024,"score_gpt":0.2844800363320834,"score_spread":0.2626144806446184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771668473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.109532505,0.0019198221,0.87853426,0.00040375622,0.00013281911,0.000108178865,0.0004823227,0.003192836,0.005693523],"genre_scores_gemma":[0.77069014,0.0013595776,0.21838653,0.00023344465,0.00009606698,0.00010604732,0.0017924012,0.00008699429,0.007248775],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973077,0.000029826762,0.0000145914455,0.000080744205,0.0000952287,0.000048907237],"domain_scores_gemma":[0.99978954,0.00004937351,0.00004201568,0.00003363393,0.00007457709,0.00001080376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000412485,0.0008092884,0.00040969046,0.0010912134,0.00022472406,0.00070352847,0.0006016197,0.0005584957,0.0011468896],"category_scores_gemma":[0.0006950198,0.00024105092,0.0005378044,0.0008415029,0.00034244178,0.0010894404,0.0006274242,0.00070863066,0.00060880627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018252141,0.00026132024,0.004246481,0.00018722884,0.00017691022,0.00010474679,0.000063955544,0.22073226,0.055457614,0.0049986886,0.0058375252,0.7077507],"study_design_scores_gemma":[0.0000028634663,0.000021005031,0.0010258654,0.000009974502,0.000011961985,0.000018135743,0.000010078142,0.9874958,0.009045468,0.0013303199,0.0010210993,0.000007419833],"about_ca_topic_score_codex":0.008175384,"about_ca_topic_score_gemma":0.009479895,"teacher_disagreement_score":0.008175384,"about_ca_system_score_codex":0.0007110504,"about_ca_system_score_gemma":0.00054080534,"threshold_uncertainty_score":0.016255558},"labels":[],"label_agreement":null},{"id":"W2771720622","doi":"10.15353/vsnl.v3i1.180","title":"Compensated Row-Column Ultrasound Imaging System Using Edge-Guided Three Dimensional Random Fields","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Column (typography); Speckle noise; Enhanced Data Rates for GSM Evolution; Computer science; Row and column spaces; Speckle pattern; Point (geometry); Noise (video); Algorithm; Computer vision; Mathematics; Image (mathematics); Row; Geometry; Telecommunications","score_opus":0.020333768441158268,"score_gpt":0.29508210330424683,"score_spread":0.2747483348630886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771720622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026585124,0.00025271997,0.96699005,0.00022367103,0.00009543779,0.00012543506,0.00015200993,0.00328604,0.0022895045],"genre_scores_gemma":[0.1816469,0.00020648204,0.8117091,0.00043705956,0.00007696754,0.00015600982,0.00034618416,0.00015281938,0.0052684466],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959284,0.00008542202,0.000022949205,0.00007583223,0.0001909233,0.000032057003],"domain_scores_gemma":[0.99942756,0.00014490663,0.000070914764,0.0001162497,0.00018394804,0.000056415916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042409572,0.0006903135,0.00066148525,0.00042390553,0.0002798831,0.00060807937,0.0007941195,0.000682077,0.0034027013],"category_scores_gemma":[0.00073758914,0.00043514604,0.00034365055,0.00032063495,0.000285959,0.0006724121,0.00092169485,0.0004610902,0.0012845314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007412746,0.00015478254,0.0014220113,0.00029807616,0.000082522354,0.0003768722,0.0002318772,0.026557278,0.6875867,0.0032843382,0.0071108057,0.2721534],"study_design_scores_gemma":[0.00015906196,0.00072688045,0.0023272336,0.000038107868,0.00011339667,0.0013816111,0.0000726517,0.6479797,0.32679516,0.0011319306,0.019082207,0.00019209678],"about_ca_topic_score_codex":0.0010932976,"about_ca_topic_score_gemma":0.0019435114,"teacher_disagreement_score":0.0034027013,"about_ca_system_score_codex":0.00026777497,"about_ca_system_score_gemma":0.00070889475,"threshold_uncertainty_score":0.011383176},"labels":[],"label_agreement":null},{"id":"W2771726357","doi":"10.15353/vsnl.v3i1.163","title":"Integrating Multispectral Hemodynamic Imaging for Bulk Tissue Oxygenation Analysis","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; AGE-WELL","keywords":"Multispectral image; Oxygenation; Biomedical engineering; Perfusion; Hemodynamics; Cuff; Materials science; Medicine; Computer science; Artificial intelligence; Cardiology; Internal medicine; Surgery","score_opus":0.00986763713271915,"score_gpt":0.3680426756805231,"score_spread":0.35817503854780397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771726357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49174857,0.00083920505,0.5038541,0.00021364991,0.00015311828,0.00015854629,0.00019129111,0.0011830435,0.0016584304],"genre_scores_gemma":[0.6983062,0.00046288714,0.29822183,0.00017655244,0.000094877905,0.00015085885,0.00014481897,0.000086729895,0.0023551902],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996269,0.00006260266,0.000015702724,0.00010663919,0.00015770024,0.000030536998],"domain_scores_gemma":[0.99923646,0.00033018016,0.00010426187,0.00010050834,0.00015785679,0.0000707545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005484408,0.00037139925,0.00030727076,0.000492806,0.000119733806,0.0005001008,0.0005191589,0.0004357107,0.001358579],"category_scores_gemma":[0.0011043587,0.00024145348,0.00020863587,0.00027364312,0.00026680302,0.00054471876,0.0007517583,0.0004087706,0.00032791958],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011654132,0.00006639361,0.0010913715,0.000055436074,0.000011539176,0.000050705836,0.000035699697,0.000585187,0.97284085,0.00023822203,0.00010736085,0.024800833],"study_design_scores_gemma":[0.000022121945,0.00071115163,0.015971968,0.000014804881,0.000049239457,0.00062453665,0.000040626022,0.055577394,0.9239703,0.0005059591,0.0024559416,0.000055978748],"about_ca_topic_score_codex":0.0003850673,"about_ca_topic_score_gemma":0.0010733345,"teacher_disagreement_score":0.001358579,"about_ca_system_score_codex":0.00023459124,"about_ca_system_score_gemma":0.0003468129,"threshold_uncertainty_score":0.004544854},"labels":[],"label_agreement":null},{"id":"W2771823977","doi":"10.15353/vsnl.v3i1.173","title":"Skin Lesion Segmentation using Deep Hypercolumn Descriptors","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph; Vector Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Jaccard index; Segmentation; Artificial intelligence; Skin lesion; Pattern recognition (psychology); Computer science; Lesion; Image segmentation; Index (typography); Medicine; Dermatology; Pathology","score_opus":0.028352155831728405,"score_gpt":0.322665110409059,"score_spread":0.2943129545773306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771823977","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23096247,0.0046414146,0.722015,0.0008462077,0.00048176144,0.0008187245,0.0095140105,0.02145334,0.009267041],"genre_scores_gemma":[0.58327764,0.0015046627,0.3724415,0.0007132521,0.00027717403,0.00031473683,0.027058352,0.0010194709,0.013393248],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99954516,0.0000384014,0.000027737098,0.00015204959,0.00014335473,0.00009330059],"domain_scores_gemma":[0.99960905,0.000070618116,0.000050289284,0.000097688324,0.000121740646,0.000050581242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043492042,0.0014845408,0.00124173,0.0031309042,0.00039656364,0.0015083452,0.0012550462,0.0012109748,0.0043270397],"category_scores_gemma":[0.0008959408,0.00035525026,0.001134337,0.0014477947,0.00040598036,0.0010556274,0.0011628972,0.0009826106,0.0027476065],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007191283,0.0005369229,0.0055818185,0.00044146995,0.0003457346,0.00027970964,0.00007112818,0.0449223,0.11507164,0.0020561563,0.029470872,0.80050325],"study_design_scores_gemma":[0.00011379644,0.00038736884,0.0068093394,0.00006711564,0.00013444864,0.00073070475,0.000086082575,0.86075044,0.11436976,0.005525786,0.010964084,0.000061056],"about_ca_topic_score_codex":0.0057421336,"about_ca_topic_score_gemma":0.011704361,"teacher_disagreement_score":0.0057421336,"about_ca_system_score_codex":0.0006884952,"about_ca_system_score_gemma":0.0010028965,"threshold_uncertainty_score":0.014475346},"labels":[],"label_agreement":null},{"id":"W2772031001","doi":"10.15353/vsnl.v3i1.162","title":"Design space exploration of Convolutional Neural Networks based on Evolutionary Algorithms","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Hyperparameter; MNIST database; Convolutional neural network; Computer science; Genetic algorithm; Artificial intelligence; Traverse; Pattern recognition (psychology); Design space exploration; Digit recognition; Machine learning; Algorithm; Space (punctuation); Artificial neural network","score_opus":0.03235407918901075,"score_gpt":0.29562278268781067,"score_spread":0.26326870349879994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772031001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043357942,0.00093162357,0.9504148,0.0001849128,0.0000397436,0.00007375961,0.0000281046,0.00024127575,0.004727914],"genre_scores_gemma":[0.61579347,0.00081870897,0.37896934,0.00016384941,0.00003766492,0.00042022482,0.00011821659,0.00010548439,0.0035731308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996351,0.0001499205,0.000018367127,0.000048571346,0.00010241528,0.00004563452],"domain_scores_gemma":[0.99950325,0.00031096814,0.000046617053,0.00004023166,0.00008016098,0.000018713492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011274731,0.0009769915,0.0006882666,0.0008787811,0.00035733767,0.000653325,0.00085651537,0.0008994095,0.0014440061],"category_scores_gemma":[0.0021109479,0.0005080013,0.00089157175,0.00052233133,0.00079023634,0.00058280834,0.00089773315,0.0008297251,0.00016500344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000183803,0.000026819213,0.00058264955,0.00004812501,0.000036381927,0.00004754143,0.00004354367,0.9513436,0.001549639,0.010170387,0.00028086078,0.035852104],"study_design_scores_gemma":[0.0000063475986,0.00002373797,0.000065446075,0.0000085463535,0.000006192862,0.000014520683,0.000007578708,0.9951747,0.0004031591,0.0037155706,0.0005714341,0.0000027956976],"about_ca_topic_score_codex":0.0023930555,"about_ca_topic_score_gemma":0.0022484537,"teacher_disagreement_score":0.0023930555,"about_ca_system_score_codex":0.0007817627,"about_ca_system_score_gemma":0.00077488687,"threshold_uncertainty_score":0.00596267},"labels":[],"label_agreement":null},{"id":"W2772417463","doi":"10.15353/vsnl.v3i1.169","title":"Effects of Spatial Transformer Location on Segmentation Performance of a Dense Transformer Network","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Segmentation; Computer science; Transformer; Spatial contextual awareness; Artificial intelligence; Pixel; Spatial analysis; Reuse; Pattern recognition (psychology); Machine learning; Data mining; Voltage; Geography; Remote sensing; Engineering; Electrical engineering","score_opus":0.007615766324722921,"score_gpt":0.2725299803004689,"score_spread":0.26491421397574594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772417463","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91347617,0.0014740829,0.07134239,0.0009365579,0.0002680179,0.000082675346,0.00040476862,0.0033783955,0.0086369915],"genre_scores_gemma":[0.9841045,0.00025917438,0.013141146,0.0001544419,0.00001433911,0.000022995815,0.00034668893,0.00014876349,0.0018079729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958867,0.00008722816,0.00002773357,0.00011880475,0.00006106133,0.00011650486],"domain_scores_gemma":[0.99802387,0.0011630522,0.00013924993,0.00023404013,0.0002917728,0.00014818016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012835932,0.0013541353,0.00051125034,0.0005333084,0.00044335958,0.0008376676,0.0012480975,0.0013654076,0.0033818847],"category_scores_gemma":[0.0070300694,0.0004884162,0.00031678192,0.00038087176,0.000809585,0.0023797757,0.0012661611,0.0011011767,0.000705947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004302633,0.00072529283,0.008367546,0.0003789776,0.00022028227,0.00057024794,0.00022673573,0.6456887,0.06690106,0.0043359096,0.004674891,0.2636077],"study_design_scores_gemma":[0.000074828335,0.0005312422,0.0018749852,0.000043262116,0.00007709563,0.00016699173,0.00011659248,0.9470048,0.046291813,0.0029522043,0.00084306486,0.000023104658],"about_ca_topic_score_codex":0.009292432,"about_ca_topic_score_gemma":0.012257186,"teacher_disagreement_score":0.009292432,"about_ca_system_score_codex":0.0012004434,"about_ca_system_score_gemma":0.00087194785,"threshold_uncertainty_score":0.018476665},"labels":[],"label_agreement":null},{"id":"W2772584911","doi":"10.15353/vsnl.v3i1.179","title":"Motion Detection in High Resolution Enhancement","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Christie (Canada); University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer vision; Artificial intelligence; Computer science; Motion estimation; Superposition principle; Motion (physics); Quarter-pixel motion; Resolution (logic); Mathematics","score_opus":0.011906540111765056,"score_gpt":0.29863789196273915,"score_spread":0.2867313518509741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772584911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05347566,0.00081088924,0.9441478,0.00007558426,0.000021097825,0.000030110594,0.000027842223,0.000365994,0.0010449296],"genre_scores_gemma":[0.3524547,0.0010391454,0.6430639,0.000083873325,0.00004996898,0.000049303337,0.000100334415,0.00008473899,0.003073951],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997271,0.000069664304,0.000012374263,0.00005412733,0.00010945758,0.000027366588],"domain_scores_gemma":[0.9996823,0.00011045998,0.000055375636,0.000058344423,0.0000731891,0.000020375734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039327762,0.0005817386,0.00034515516,0.00055003306,0.00016042018,0.00037144384,0.00046666258,0.0004254476,0.0011901293],"category_scores_gemma":[0.000952596,0.0003786953,0.00031601754,0.00044352125,0.00040879805,0.0008787798,0.00076005835,0.00038545462,0.00034964402],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028727757,0.00007729692,0.0012300014,0.00023009506,0.00004330571,0.00022100535,0.00017069999,0.04996153,0.55419034,0.012545229,0.0009797837,0.38006347],"study_design_scores_gemma":[0.000039034163,0.00026156384,0.002758637,0.000026872776,0.000043626937,0.000710207,0.000039770766,0.612518,0.37063345,0.0058145705,0.007114384,0.00003988163],"about_ca_topic_score_codex":0.00061103154,"about_ca_topic_score_gemma":0.0007642166,"teacher_disagreement_score":0.0011901293,"about_ca_system_score_codex":0.00025383866,"about_ca_system_score_gemma":0.00025905474,"threshold_uncertainty_score":0.003981352},"labels":[],"label_agreement":null},{"id":"W2772759524","doi":"10.15353/vsnl.v3i1.172","title":"Ensembles of Random Projections for Nonlinear Dimensionality Reduction","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Dimensionality reduction; Random projection; Generalization; Nonlinear system; Embedding; Computer science; Curse of dimensionality; Algorithm; Reduction (mathematics); Artificial intelligence; Mathematics; Pattern recognition (psychology)","score_opus":0.021659844421247267,"score_gpt":0.32386730766506533,"score_spread":0.3022074632438181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772759524","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005127195,0.0007237195,0.9928514,0.0001349944,0.000046244226,0.000033735407,0.00010588962,0.00027915955,0.00069763995],"genre_scores_gemma":[0.22152153,0.002988283,0.7687678,0.00020577703,0.00032966078,0.00047276772,0.0013664763,0.00022643442,0.004121271],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818474,0.0007701953,0.000091544585,0.00035544368,0.0005208575,0.00007722676],"domain_scores_gemma":[0.9980888,0.00084876816,0.0001748037,0.00046772938,0.00035399114,0.00006594936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019464013,0.0013806886,0.0011498426,0.0010213519,0.0005202214,0.0010094167,0.00093720475,0.0008525728,0.0024464175],"category_scores_gemma":[0.0058239894,0.00046559056,0.0012098138,0.0013719283,0.0008523494,0.0023291162,0.0019059305,0.0020179255,0.0012945129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017269337,0.00012704148,0.001777401,0.00041466474,0.0002895851,0.00015690057,0.00019494476,0.3607309,0.01249227,0.10833076,0.008164817,0.5071481],"study_design_scores_gemma":[0.000008624594,0.000071889954,0.0006357715,0.00003370433,0.000025469935,0.00012607385,0.000029907234,0.9501819,0.0038758353,0.03975565,0.0052245324,0.00003065436],"about_ca_topic_score_codex":0.0012416443,"about_ca_topic_score_gemma":0.0016793589,"teacher_disagreement_score":0.0024464175,"about_ca_system_score_codex":0.00042998616,"about_ca_system_score_gemma":0.0007294195,"threshold_uncertainty_score":0.010293722},"labels":[],"label_agreement":null},{"id":"W2773392077","doi":"10.15353/vsnl.v3i1.164","title":"Impact of Training Images on Radiometric Compensation","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Christie (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Radiometric dating; Compensation (psychology); Computer science; Casual; Projection (relational algebra); Computer vision; Radiometry; Remote sensing; Artificial intelligence; Psychology; Geology","score_opus":0.039029933921952,"score_gpt":0.3172151118862849,"score_spread":0.2781851779643329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773392077","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60916823,0.010541067,0.35477048,0.0011975401,0.0012984566,0.00024814007,0.0009852406,0.004805039,0.016985817],"genre_scores_gemma":[0.8655036,0.0028611724,0.12383237,0.00047770006,0.00013266238,0.00009252242,0.0015444452,0.00094602146,0.0046095448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977591,0.00054681546,0.00012898397,0.0004063523,0.0008475606,0.00031115048],"domain_scores_gemma":[0.9905172,0.0055271583,0.0004932529,0.0013044492,0.0019524908,0.0002055533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020471993,0.001552422,0.00076639757,0.0008904549,0.0006272326,0.0012005366,0.0008116702,0.0013877851,0.0043396866],"category_scores_gemma":[0.018372098,0.00040781507,0.00046226193,0.0010314569,0.00080497947,0.001500913,0.0013010193,0.0010601217,0.0011717934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002281877,0.00040056225,0.0066792252,0.0010948179,0.00015928312,0.00020285998,0.00033174935,0.12292074,0.11076799,0.0016225805,0.005134008,0.74840426],"study_design_scores_gemma":[0.00015626564,0.001297104,0.04563089,0.0005210057,0.0004503389,0.0014851969,0.0005000319,0.5418231,0.38623172,0.0029644042,0.018696299,0.0002436194],"about_ca_topic_score_codex":0.0050365245,"about_ca_topic_score_gemma":0.0031968346,"teacher_disagreement_score":0.0050365245,"about_ca_system_score_codex":0.0005996481,"about_ca_system_score_gemma":0.000630017,"threshold_uncertainty_score":0.0145177245},"labels":[],"label_agreement":null},{"id":"W2774043055","doi":"10.15353/vsnl.v3i1.170","title":"Equivalence of histogram equalization, histogram matching and the Nyul algorithm for intensity standardization in MRI","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph; Toronto Metropolitan University; Vector Institute","funders":"","keywords":"Histogram matching; Histogram equalization; Histogram; Adaptive histogram equalization; Preprocessor; Matching (statistics); Equivalence (formal languages); Mathematics; Artificial intelligence; Algorithm; Computer science; Pattern recognition (psychology); Image (mathematics); Statistics; Discrete mathematics","score_opus":0.012984926226158901,"score_gpt":0.3070395583726457,"score_spread":0.2940546321464868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774043055","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005740998,0.0002678437,0.9915343,0.00009632383,0.00005968022,0.000049192186,0.000029848577,0.00034389054,0.0018778607],"genre_scores_gemma":[0.13203807,0.0006290762,0.8625471,0.00012281779,0.00013521402,0.00015779684,0.00018024884,0.00037109008,0.003818564],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828255,0.0004912261,0.000101843776,0.00033456564,0.00066886045,0.00012101101],"domain_scores_gemma":[0.99818856,0.0007550024,0.0001276432,0.00047926386,0.00038690938,0.00006249101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002193501,0.0005404495,0.0007969866,0.0015670358,0.0006070981,0.0017186718,0.0012352084,0.0009858133,0.0025086424],"category_scores_gemma":[0.008442377,0.00046411483,0.00056875317,0.00149529,0.0016921543,0.002956362,0.0015966219,0.0013378789,0.0010374982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005359483,0.000117613316,0.0014355686,0.000218914,0.00008491939,0.000115043615,0.00026985962,0.047349103,0.042245448,0.16619986,0.002862925,0.73856485],"study_design_scores_gemma":[0.00008792289,0.0002647514,0.005240354,0.00006709943,0.000053243977,0.0006596066,0.00013988029,0.76106465,0.07005303,0.14149898,0.020739773,0.00013078652],"about_ca_topic_score_codex":0.001833026,"about_ca_topic_score_gemma":0.0016714153,"teacher_disagreement_score":0.0025086424,"about_ca_system_score_codex":0.0007005976,"about_ca_system_score_gemma":0.0011892386,"threshold_uncertainty_score":0.011600435},"labels":[],"label_agreement":null},{"id":"W2774295060","doi":"10.15353/vsnl.v3i1.167","title":"Comparison of phase-resolved Doppler optical coherence tomography and optical coherence tomography angiography for measuring retinal blood vessels size","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Optical coherence tomography; Retinal; Doppler effect; Optical coherence tomography angiography; Angiography; Tomography; Medicine; Optics; Biomedical engineering; Radiology; Materials science; Ophthalmology; Physics","score_opus":0.03158701623171817,"score_gpt":0.3538721551921979,"score_spread":0.3222851389604797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774295060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9442347,0.004307582,0.049326956,0.00011891569,0.00007618075,0.00009723792,0.00016741674,0.000091280475,0.0015797443],"genre_scores_gemma":[0.9280185,0.0025809074,0.06798628,0.00017644247,0.0000846397,0.00021252318,0.00016229549,0.000021729044,0.0007567063],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883705,0.000465368,0.00006911947,0.00014913754,0.0004185667,0.000060874932],"domain_scores_gemma":[0.99643445,0.001701303,0.00061170635,0.00018989707,0.00087530934,0.0001872331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014640521,0.00033450266,0.00029434613,0.0014385572,0.00013130433,0.0004393422,0.00024477646,0.00044202965,0.00044088397],"category_scores_gemma":[0.0037491254,0.00020156188,0.00014889809,0.0004206085,0.00016526593,0.0008451294,0.00028925407,0.00028471195,0.00010125876],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003999043,0.000651734,0.057133958,0.0005195328,0.00023722959,0.00018233918,0.0001748499,0.0013289314,0.7594233,0.00082423247,0.00028083564,0.175244],"study_design_scores_gemma":[0.00035550978,0.010879528,0.4153642,0.00012709002,0.0007306562,0.0034765566,0.000524867,0.053372413,0.51086533,0.0009031047,0.003209514,0.00019123066],"about_ca_topic_score_codex":0.0004345866,"about_ca_topic_score_gemma":0.0014008898,"teacher_disagreement_score":0.0014640521,"about_ca_system_score_codex":0.00023395818,"about_ca_system_score_gemma":0.00031591032,"threshold_uncertainty_score":0.0077427626},"labels":[],"label_agreement":null},{"id":"W2774346682","doi":"10.15353/vsnl.v3i1.165","title":"Depth from Defocus via Active Multispectral Quasi-random Point Projections using Deep Learning","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial intelligence; Multispectral image; RGB color model; Projection (relational algebra); Point (geometry); Computer vision; Computer science; Depth map; Deep learning; Pattern recognition (psychology); Image (mathematics); Mathematics; Algorithm; Geometry","score_opus":0.012237742298038552,"score_gpt":0.2949107335159443,"score_spread":0.2826729912179058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774346682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01971326,0.00012604777,0.97865605,0.00007300952,0.000013311552,0.000025053145,0.00005264801,0.00053902203,0.0008016922],"genre_scores_gemma":[0.43348354,0.00033974342,0.5636079,0.00012079857,0.000030941763,0.00007110056,0.00022156526,0.000102314225,0.0020220955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968505,0.000051728013,0.000010615027,0.000060018025,0.00015853866,0.00003409278],"domain_scores_gemma":[0.9995926,0.00012389336,0.00007960391,0.00007502482,0.00010322419,0.000025748499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039437367,0.000907063,0.0004461754,0.0005541719,0.00019812507,0.0007817005,0.0009044813,0.0005895658,0.0012915086],"category_scores_gemma":[0.0011521807,0.0004465405,0.0004988456,0.0005038129,0.000520032,0.0017652536,0.0016699105,0.0012514812,0.00031153997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031248914,0.00016111784,0.002185647,0.00023438639,0.00012228162,0.00012984364,0.00021059468,0.31951836,0.12912257,0.012023953,0.0018207775,0.534158],"study_design_scores_gemma":[0.000007896164,0.000032583244,0.00039949993,0.000008797414,0.00000889174,0.000056699846,0.000012903545,0.97529036,0.020005321,0.0035681915,0.0005956359,0.00001328494],"about_ca_topic_score_codex":0.0022238418,"about_ca_topic_score_gemma":0.0049179415,"teacher_disagreement_score":0.0022238418,"about_ca_system_score_codex":0.0006105461,"about_ca_system_score_gemma":0.00080343103,"threshold_uncertainty_score":0.004429817},"labels":[],"label_agreement":null},{"id":"W2774520696","doi":"10.15353/vsnl.v3i1.176","title":"A Multi-layer Perceptron Approach to Automatically Detect Tissue via NIR Multispectral Imaging","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Multispectral image; Artificial intelligence; Perceptron; Pixel; Pattern recognition (psychology); Computer science; Multilayer perceptron; Artificial neural network","score_opus":0.019390136558388817,"score_gpt":0.36749906293320056,"score_spread":0.3481089263748117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774520696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034128577,0.0009561705,0.9600755,0.00034978916,0.00012781163,0.000044655055,0.00018094097,0.0030185732,0.0011178904],"genre_scores_gemma":[0.63876605,0.00047347817,0.3531397,0.00053088216,0.00014721876,0.00014958347,0.0004987628,0.00016240448,0.0061319196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995648,0.00011037834,0.000030342007,0.0001347939,0.00008761232,0.000072079],"domain_scores_gemma":[0.9994042,0.00033188178,0.00005941591,0.000044268505,0.00012557316,0.000034586268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012575286,0.0009945575,0.0008040656,0.0006036878,0.00032623697,0.00073083444,0.0013366753,0.0013672437,0.00153194],"category_scores_gemma":[0.0016661738,0.00054214156,0.0008260931,0.00052346673,0.0004231062,0.00085157773,0.00065185694,0.001249175,0.0007401589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005180746,0.00033960628,0.0029736583,0.00020423847,0.00028784832,0.00030569785,0.00008729071,0.5226798,0.034680035,0.0018238199,0.004856663,0.4312432],"study_design_scores_gemma":[0.00000562078,0.000031936706,0.00031518025,0.000005796278,0.000016111324,0.00002405684,0.0000052276237,0.9959007,0.0026975577,0.00073960464,0.00025170884,0.00000651542],"about_ca_topic_score_codex":0.0039859465,"about_ca_topic_score_gemma":0.004348654,"teacher_disagreement_score":0.0039859465,"about_ca_system_score_codex":0.0006567896,"about_ca_system_score_gemma":0.00052666554,"threshold_uncertainty_score":0.00792551},"labels":[],"label_agreement":null},{"id":"W2775452007","doi":"10.15353/vsnl.v3i1.174","title":"Foot Depth Map Point Cloud Completion using Deep Learning with Residual Blocks","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Point cloud; Residual; Computer science; 3d scanning; Foot (prosody); Artificial intelligence; Computer vision; Point (geometry); Object (grammar); Mathematics; Algorithm; Geometry","score_opus":0.019530842986673937,"score_gpt":0.3176888595566746,"score_spread":0.29815801657000063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775452007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04243716,0.0004225025,0.95184684,0.00025817505,0.00006673259,0.0000957316,0.00030282384,0.0032374267,0.0013326269],"genre_scores_gemma":[0.5997923,0.00034341376,0.39032736,0.00032020916,0.000077433375,0.00019859178,0.0023428202,0.00023451481,0.00636327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972135,0.00003989462,0.000015055463,0.00007552425,0.000085203625,0.0000630387],"domain_scores_gemma":[0.99939346,0.00019678779,0.000055078897,0.000112264446,0.00017997282,0.00006238877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006075285,0.0011282773,0.0009961701,0.0005689312,0.0003216827,0.0007104367,0.0018688872,0.0010324671,0.0040329453],"category_scores_gemma":[0.0019182193,0.0005676283,0.0007938433,0.00072673714,0.0004259593,0.001057571,0.0013641377,0.0020646683,0.0014210179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029926418,0.00023725918,0.0018518048,0.00010485137,0.000108082466,0.000119923556,0.00008354435,0.5083272,0.009314649,0.0032806466,0.006884828,0.46938792],"study_design_scores_gemma":[0.0000074893674,0.000023057175,0.000103143,0.0000035091048,0.0000032135458,0.000009374056,0.0000054123266,0.9979942,0.0007597415,0.00078189885,0.00030564837,0.0000033095018],"about_ca_topic_score_codex":0.029607818,"about_ca_topic_score_gemma":0.03284573,"teacher_disagreement_score":0.029607818,"about_ca_system_score_codex":0.0007664009,"about_ca_system_score_gemma":0.001691287,"threshold_uncertainty_score":0.05887097},"labels":[],"label_agreement":null},{"id":"W2775632911","doi":"10.15353/vsnl.v3i1.175","title":"Estimating Optimal Depth of VGG Net with Tree-Structured Parzen Estimators","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Sunnybrook Hospital; University of Toronto","funders":"","keywords":"Estimator; Computer science; Computation; Convolutional neural network; Grid; Tree (set theory); Architecture; Artificial intelligence; Pattern recognition (psychology); Algorithm; Mathematics; Statistics","score_opus":0.012086793988530622,"score_gpt":0.2977692761548934,"score_spread":0.2856824821663628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775632911","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066341154,0.0009704111,0.92946196,0.00028581452,0.000041004907,0.000071866336,0.00023528315,0.001521951,0.0010706531],"genre_scores_gemma":[0.57394725,0.00049463584,0.42189458,0.00032566997,0.000052303152,0.00014582639,0.0008929329,0.00031252214,0.001934394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995958,0.00009686103,0.000023137209,0.00011389598,0.00009265455,0.000077629134],"domain_scores_gemma":[0.99916756,0.00045237606,0.00009159436,0.00009042216,0.00015489876,0.000043214153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011922948,0.0010301262,0.0011518889,0.0010467112,0.0003882084,0.000968966,0.0012370717,0.0018583119,0.0019642436],"category_scores_gemma":[0.0046738633,0.0007518403,0.0007193198,0.00070586655,0.0005715395,0.0026808137,0.0009673366,0.0013997477,0.0005521931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029405556,0.00013131391,0.004869697,0.00015416706,0.00012306869,0.00012775914,0.00011292147,0.7110116,0.009755587,0.010228322,0.005749,0.25744247],"study_design_scores_gemma":[0.000007503412,0.000012330493,0.00021453522,0.0000051844713,0.0000056087874,0.000016559026,0.0000064433366,0.9958365,0.0009838272,0.002739962,0.0001671366,0.0000044699714],"about_ca_topic_score_codex":0.008395275,"about_ca_topic_score_gemma":0.011629228,"teacher_disagreement_score":0.008395275,"about_ca_system_score_codex":0.0011569826,"about_ca_system_score_gemma":0.0015644819,"threshold_uncertainty_score":0.016692817},"labels":[],"label_agreement":null},{"id":"W2776336566","doi":"10.15353/vsnl.v3i1.181","title":"Efficient Deep Network Architecture for Vision-Based Vehicle Detection Keyvan Kasiri,","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Centres of Excellence","keywords":"Deep learning; Artificial intelligence; Computer science; Software deployment; Artificial neural network; Architecture; Object detection; Deep neural networks; Process (computing); Network architecture; Machine learning; Pattern recognition (psychology); Computer security","score_opus":0.004742313726864791,"score_gpt":0.2403935391249207,"score_spread":0.2356512253980559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2776336566","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.104704134,0.0019519749,0.88516295,0.00087911746,0.00022066552,0.000039449256,0.00011815453,0.0019327253,0.0049908287],"genre_scores_gemma":[0.765953,0.0012605679,0.21893926,0.00020870376,0.00005956697,0.000061060564,0.00047519902,0.00012602584,0.012916584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991274,0.000010505434,0.0000047297135,0.000022012808,0.000033729015,0.000016265441],"domain_scores_gemma":[0.9998733,0.000022159884,0.000009030339,0.000012973713,0.00007167932,0.000010831335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001772834,0.00039322663,0.00021959507,0.0002899247,0.00024916173,0.00039648637,0.00067472016,0.00040640065,0.0014066858],"category_scores_gemma":[0.0006042021,0.00025976085,0.00022717504,0.00025394786,0.00019642859,0.0007394056,0.00049475837,0.0008557034,0.00052659604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025346462,0.0001269144,0.0016819721,0.00012538701,0.00006999787,0.00014480995,0.00008344534,0.4629844,0.08020203,0.0106922295,0.007285836,0.43634945],"study_design_scores_gemma":[0.0000034790428,0.000020534175,0.00014639128,0.0000026853775,0.0000068279332,0.000017662882,0.0000046928103,0.9897106,0.0078111803,0.001164659,0.0011076428,0.0000036659628],"about_ca_topic_score_codex":0.0067720055,"about_ca_topic_score_gemma":0.009164884,"teacher_disagreement_score":0.0067720055,"about_ca_system_score_codex":0.0006389396,"about_ca_system_score_gemma":0.00059006497,"threshold_uncertainty_score":0.013465166},"labels":[],"label_agreement":null},{"id":"W2781180907","doi":"10.15353/vsnl.v3i1.183","title":"Goldilocks and the Three Parameters:Empirically Finding the \"Just Right\" for Segmenting Food Images for the AFINI-T System","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Weighting; Segmentation; Artificial intelligence; Market segmentation; Computer science; Regularization (linguistics); Goldilocks principle; Computer vision; Pattern recognition (psychology); Statistics; Business; Mathematics; Marketing; Medicine","score_opus":0.0402296305299808,"score_gpt":0.33322782916205596,"score_spread":0.29299819863207516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781180907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36939946,0.0024094123,0.61824363,0.0013278803,0.00017006538,0.00041115948,0.00029356818,0.0027677172,0.004977047],"genre_scores_gemma":[0.66378736,0.00036161023,0.33279276,0.00041928003,0.000043563876,0.00012971892,0.00038387594,0.00025641616,0.0018254566],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984842,0.0004297005,0.0001106265,0.00049222953,0.00027451228,0.00020887738],"domain_scores_gemma":[0.9967424,0.0017945459,0.00034529326,0.00047990726,0.0004996198,0.00013818272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039574197,0.001112602,0.0012460029,0.0013191189,0.0010634095,0.0024473788,0.0016396951,0.0028417597,0.0018387948],"category_scores_gemma":[0.017725794,0.00067656266,0.00059642456,0.00069513737,0.0012672052,0.0028797444,0.0015068945,0.0014873574,0.0004856692],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022002966,0.00055122946,0.029120848,0.0006401876,0.000329825,0.00039332165,0.0004985517,0.30520687,0.0629084,0.012772119,0.0057912376,0.57958716],"study_design_scores_gemma":[0.0000675641,0.00017816089,0.0050284374,0.00004983765,0.000051863302,0.00023104179,0.00008964833,0.97054344,0.018990487,0.003658416,0.0010705891,0.000040531482],"about_ca_topic_score_codex":0.012628492,"about_ca_topic_score_gemma":0.014220266,"teacher_disagreement_score":0.012628492,"about_ca_system_score_codex":0.0014386949,"about_ca_system_score_gemma":0.0015814811,"threshold_uncertainty_score":0.025110006},"labels":[],"label_agreement":null},{"id":"W2897286657","doi":"10.15353/jcvis.v4i1.324","title":"SAMSON: Spectral Absorption-fluorescence Microscopy System for ON-site-imaging of algae","year":2018,"lang":"en","type":"preprint","venue":"Journal of Computational Vision and Imaging Systems","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Algae; Absorption (acoustics); Fluorescence; Multispectral image; Sample (material); Optics; Chlorophyta; Materials science; Fluorescence microscope; Microscopy; Computer science; Optoelectronics; Biological system; Remote sensing; Artificial intelligence; Chemistry; Botany; Physics; Biology; Geology","score_opus":0.01405045621632022,"score_gpt":0.296428778594859,"score_spread":0.28237832237853877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897286657","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070840165,0.0013826821,0.82658005,0.00059453765,0.0003247463,0.00043645545,0.0033073227,0.07570295,0.0208311],"genre_scores_gemma":[0.17917703,0.000825885,0.7896784,0.00064987177,0.00010283054,0.00072846387,0.003961602,0.0025771803,0.02229882],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995165,0.00004768878,0.0000190125,0.00012978679,0.00024103452,0.000045875848],"domain_scores_gemma":[0.99961394,0.00009032686,0.000036491456,0.00009821575,0.00011477352,0.000046377518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062469265,0.00066806853,0.0005804603,0.0011934605,0.00052101293,0.00051498925,0.0013202683,0.0009479532,0.01732241],"category_scores_gemma":[0.0006019369,0.00052406784,0.0004496042,0.0005721707,0.00027460753,0.0010638493,0.0013692166,0.0006789177,0.003928688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005066214,0.000109881985,0.0030228572,0.0005912792,0.000086794986,0.00029666946,0.0003470588,0.0022347588,0.7069034,0.0069813477,0.068641216,0.21027806],"study_design_scores_gemma":[0.00010566092,0.00031148343,0.008407283,0.00009190878,0.000069805574,0.002322194,0.00014837336,0.087143466,0.56031615,0.0032273342,0.33768544,0.00017089074],"about_ca_topic_score_codex":0.0009119613,"about_ca_topic_score_gemma":0.0021001338,"teacher_disagreement_score":0.01732241,"about_ca_system_score_codex":0.00057175773,"about_ca_system_score_gemma":0.00056965917,"threshold_uncertainty_score":0.057949245},"labels":[],"label_agreement":null},{"id":"W2897594239","doi":"10.15353/vsnl.v3i1.193","title":"A Comprehensive Spectral Analysis of the Auto-fluorescence Characteristics of Three Algae Species at Twelve Discrete Excitation Wavelengths","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Algae; Chlorophyta; Fluorescence; Phylum; Biological system; Botany; Fluorescence spectroscopy; Biology; Computer science; Pattern recognition (psychology); Artificial intelligence; Optics; Physics; Gene","score_opus":0.017912940040178262,"score_gpt":0.292263605993652,"score_spread":0.27435066595347374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897594239","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9251914,0.0008409309,0.06768836,0.00010234733,0.00003070168,0.00006263668,0.001466953,0.0010166486,0.0036000502],"genre_scores_gemma":[0.91372424,0.00077733566,0.08079783,0.00010052786,0.000014189677,0.0000849286,0.0022570966,0.00016827094,0.0020755508],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975985,0.000022196327,0.000011059235,0.00006914085,0.000108280554,0.000029419325],"domain_scores_gemma":[0.9995764,0.000072176954,0.000046453944,0.00006409134,0.00020909887,0.000031798216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004681027,0.0004051265,0.00035449598,0.001961473,0.00057864655,0.00032721317,0.00030457933,0.0004007591,0.0013448707],"category_scores_gemma":[0.00032116054,0.00016847433,0.0006957488,0.0012591472,0.00021684612,0.0005152414,0.00036468304,0.00046897115,0.00043256488],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017037561,0.00014351238,0.016952766,0.00023249217,0.00012151452,0.00009443609,0.00015766984,0.0018113547,0.9054789,0.00022623707,0.00065776514,0.073952936],"study_design_scores_gemma":[0.000015156886,0.00044341446,0.25968376,0.000043911765,0.00028815665,0.0011454042,0.00038828223,0.03868434,0.69115144,0.00076119695,0.0072755553,0.000119499295],"about_ca_topic_score_codex":0.001216249,"about_ca_topic_score_gemma":0.0024507095,"teacher_disagreement_score":0.001961473,"about_ca_system_score_codex":0.00023739117,"about_ca_system_score_gemma":0.00024046008,"threshold_uncertainty_score":0.004499018},"labels":[],"label_agreement":null},{"id":"W2905619401","doi":"10.15353/jcvis.v4i1.336","title":"Guarding Against Adversarial Attacks using Biologically Inspired Contour Integration","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Adversarial system; Robustness (evolution); Computer science; Artificial intelligence; System integration; Computer vision; Biology","score_opus":0.019343367401511623,"score_gpt":0.3128733315685395,"score_spread":0.2935299641670279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905619401","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086065866,0.00037782552,0.9073999,0.00048013165,0.00008268132,0.00005271849,0.00002390893,0.0007259485,0.004790993],"genre_scores_gemma":[0.93637747,0.00018519454,0.061158616,0.00023059946,0.000036172343,0.000037391113,0.000038547478,0.00008708615,0.0018489665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993899,0.00014269077,0.00003072159,0.00011912803,0.00023453553,0.00008291824],"domain_scores_gemma":[0.996959,0.0015464019,0.0005118548,0.0005615477,0.00027318244,0.00014796673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013599551,0.0008489289,0.00076969847,0.0005897996,0.0005186415,0.0012183802,0.0011167225,0.0015180157,0.0016670545],"category_scores_gemma":[0.006295442,0.00042008937,0.00054014724,0.00030336823,0.0020922776,0.0017766639,0.0026454306,0.002084329,0.00037700034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020342416,0.00008410202,0.001261211,0.00005047424,0.000082548686,0.00021633419,0.0001401621,0.8562254,0.044543136,0.03370107,0.0012908871,0.062201347],"study_design_scores_gemma":[0.0000069874686,0.00004970843,0.00012712185,0.0000059974195,0.000006690879,0.00004744496,0.000008157439,0.98647493,0.0045213643,0.00836232,0.00038121082,0.00000812831],"about_ca_topic_score_codex":0.00077303126,"about_ca_topic_score_gemma":0.0006098549,"teacher_disagreement_score":0.0016670545,"about_ca_system_score_codex":0.00070515886,"about_ca_system_score_gemma":0.0005324896,"threshold_uncertainty_score":0.0071921945},"labels":[],"label_agreement":null},{"id":"W2905664354","doi":"","title":"Modelling of Terrain Surfaces Using Aerial Radar Mapping","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Point cloud; Computer science; Radar; Computer vision; Terrain; Artificial intelligence; Remote sensing; 3D radar; Man-portable radar; Radar imaging; Geography; Radar engineering details; Cartography","score_opus":0.023044331815710806,"score_gpt":0.24959124250950313,"score_spread":0.22654691069379232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905664354","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03057896,0.00015076941,0.9598993,0.00011342846,0.000038932627,0.000090021225,0.0005037014,0.0012135876,0.007411272],"genre_scores_gemma":[0.5638112,0.00057776243,0.42761067,0.000053853793,0.00003847183,0.00021922271,0.001215075,0.00046807487,0.006005654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997863,0.000037838032,0.000015235957,0.000044396562,0.00009218686,0.000024131363],"domain_scores_gemma":[0.99979347,0.000071555376,0.00002552364,0.00004800337,0.00004787189,0.000013568333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015119194,0.0007006614,0.00045121732,0.00092420325,0.0003015211,0.0015090529,0.0009738513,0.00085411075,0.003176138],"category_scores_gemma":[0.0008339976,0.0005441824,0.000973164,0.00083370163,0.0005167535,0.00086783833,0.0012427185,0.00057653454,0.0010890581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018122513,0.000012141124,0.00066442566,0.000060624283,0.0000138261175,0.00022310967,0.00013809507,0.96644986,0.004473361,0.006326181,0.0007151748,0.020905038],"study_design_scores_gemma":[0.000004252653,0.00000641758,0.00022127248,0.000008772579,0.000002895865,0.000058692014,0.00003485668,0.99395037,0.0008309914,0.0024392155,0.0024344688,0.00000785928],"about_ca_topic_score_codex":0.0060972325,"about_ca_topic_score_gemma":0.005993346,"teacher_disagreement_score":0.0060972325,"about_ca_system_score_codex":0.00032267143,"about_ca_system_score_gemma":0.0003763603,"threshold_uncertainty_score":0.012123466},"labels":[],"label_agreement":null},{"id":"W2905768928","doi":"","title":"Text Enhancement in Projected Imagery","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Thresholding; Filter (signal processing); Artificial intelligence; Projection (relational algebra); Range (aeronautics); Class (philosophy); Quality (philosophy); Visualization; Image (mathematics); Pattern recognition (psychology); Computer vision; Algorithm; Physics","score_opus":0.011008206715201642,"score_gpt":0.31547194517791644,"score_spread":0.3044637384627148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905768928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19454248,0.0016631829,0.7946007,0.00022957406,0.00013153015,0.00012096307,0.00017815758,0.0020185483,0.0065147765],"genre_scores_gemma":[0.54958194,0.0019059215,0.4363154,0.00023191345,0.00015872678,0.00006501082,0.00039086893,0.00036309953,0.01098712],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998048,0.000031025713,0.000008728766,0.000041141768,0.00009234819,0.00002198404],"domain_scores_gemma":[0.99959666,0.00012903815,0.000055246248,0.00007604282,0.000113473674,0.000029580044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027475785,0.0006842835,0.000347578,0.00056668743,0.00015283856,0.0005852713,0.0003476932,0.00036256452,0.0028137942],"category_scores_gemma":[0.0010940785,0.00026197766,0.00044623218,0.0003690182,0.0004100685,0.000979424,0.00063604506,0.00052065845,0.0008946088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062870106,0.00005849298,0.0006265409,0.0004523702,0.00004800601,0.0004094236,0.00016811318,0.014231561,0.7141045,0.0022839475,0.0015330284,0.26545542],"study_design_scores_gemma":[0.00006150922,0.00065260555,0.007913653,0.00006769578,0.00009910062,0.00256081,0.0001361031,0.23095274,0.7426771,0.00305624,0.011771478,0.00005100981],"about_ca_topic_score_codex":0.00032642955,"about_ca_topic_score_gemma":0.0005221763,"teacher_disagreement_score":0.0028137942,"about_ca_system_score_codex":0.00013800923,"about_ca_system_score_gemma":0.00013668634,"threshold_uncertainty_score":0.0094130635},"labels":[],"label_agreement":null},{"id":"W2905799946","doi":"","title":"FEELS: a full-spectrum enhanced emotion learning system for assisting individuals with autism spectrum disorder","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Autism spectrum disorder; Loneliness; Surprise; Psychology; Anger; Isolation (microbiology); Cognitive psychology; Autism; Psychological intervention; Computer science; Artificial intelligence; Developmental psychology; Communication; Social psychology; Psychiatry","score_opus":0.015738982135225305,"score_gpt":0.3005130013891047,"score_spread":0.2847740192538794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905799946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4166379,0.0016477903,0.5182282,0.0014658504,0.0006639965,0.0007233608,0.001572766,0.04228113,0.016779033],"genre_scores_gemma":[0.8435524,0.0003370189,0.13308439,0.0013460066,0.00007819176,0.0005979158,0.0010747324,0.00048881647,0.019440439],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999058,0.000016611366,0.0000062681643,0.000032520496,0.000024081872,0.000014636708],"domain_scores_gemma":[0.9999182,0.000031255422,0.000009366555,0.000006288983,0.000018539555,0.000016223765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021493016,0.00057052914,0.0002690512,0.00013804082,0.00012805861,0.00022515345,0.0006194967,0.00046349212,0.006481603],"category_scores_gemma":[0.0006653893,0.0001497664,0.00027893556,0.000031444193,0.00013911033,0.0005716207,0.00090853515,0.00046325644,0.0009884273],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025682594,0.0009895943,0.008253405,0.0007111152,0.00019251036,0.0014091957,0.0009354585,0.015513791,0.35286286,0.0016429744,0.048061337,0.5668595],"study_design_scores_gemma":[0.0006373198,0.003185466,0.026802674,0.00019209406,0.00027243886,0.0020942998,0.00059059315,0.8054531,0.11010463,0.005718144,0.04473138,0.00021776576],"about_ca_topic_score_codex":0.00064575387,"about_ca_topic_score_gemma":0.0012743281,"teacher_disagreement_score":0.006481603,"about_ca_system_score_codex":0.00018567772,"about_ca_system_score_gemma":0.00015890706,"threshold_uncertainty_score":0.021683156},"labels":[],"label_agreement":null},{"id":"W2905820297","doi":"","title":"Human Perception-based Image Enhancement Using a Deep Generative Model","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Autoencoder; Artificial intelligence; Image (mathematics); Histogram; Generative model; Computer science; Generative grammar; Deep learning; Pixel; Pattern recognition (psychology); Frame (networking); Image quality; Perception; Computer vision","score_opus":0.032283111042921774,"score_gpt":0.36988181051999447,"score_spread":0.3375986994770727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905820297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008485291,0.00023832425,0.98953104,0.00017279907,0.000024218827,0.000017660217,0.00003583276,0.0003386205,0.0011561333],"genre_scores_gemma":[0.7232607,0.00082229584,0.26818153,0.00036717716,0.00007279502,0.00007330937,0.00019147972,0.00019465171,0.0068360986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982125,0.000046616227,0.000005382918,0.000056254845,0.00004842451,0.000022155009],"domain_scores_gemma":[0.9997745,0.00011258885,0.00002182137,0.00003581147,0.000037698912,0.000017585962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005104172,0.0005326157,0.00045101476,0.0003890015,0.00014653674,0.0007052748,0.000849295,0.0007193266,0.0019479424],"category_scores_gemma":[0.0010494861,0.00045397042,0.0008874316,0.00026996416,0.0005405111,0.0009718136,0.00087958993,0.0011487161,0.00043685318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011579692,0.0000855961,0.00076334085,0.00012669637,0.00013306731,0.00016003188,0.00016390522,0.7788904,0.047013115,0.028515397,0.0019174481,0.14211527],"study_design_scores_gemma":[0.0000028982931,0.000014996202,0.00014406866,0.0000044176163,0.000008716878,0.000030839685,0.0000039796882,0.9926288,0.0021397097,0.004599923,0.00041678108,0.000004849795],"about_ca_topic_score_codex":0.0028542478,"about_ca_topic_score_gemma":0.0034040501,"teacher_disagreement_score":0.0028542478,"about_ca_system_score_codex":0.00054012507,"about_ca_system_score_gemma":0.00037497698,"threshold_uncertainty_score":0.006516516},"labels":[],"label_agreement":null},{"id":"W2905954757","doi":"","title":"Frame Augmentation for Imbalanced Object Detection Datasets","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Object (grammar); Artificial intelligence; Frame (networking); Exploit; Perspective (graphical); Object detection; Class (philosophy); Set (abstract data type); Computer vision; Training set; Data set; Pattern recognition (psychology)","score_opus":0.011839027312742346,"score_gpt":0.30401579411248314,"score_spread":0.2921767667997408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905954757","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53219503,0.0072844895,0.38999954,0.0025711912,0.0025861948,0.001142938,0.029495094,0.026694305,0.00803135],"genre_scores_gemma":[0.71105707,0.0009507909,0.1951318,0.00077677035,0.00061140273,0.00084415585,0.084314235,0.00071311084,0.005600764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977697,0.000484118,0.00012301882,0.0008203117,0.0005663374,0.00023659258],"domain_scores_gemma":[0.9972537,0.0008127649,0.00026613724,0.0010634491,0.0004367775,0.00016710014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003382444,0.0026978154,0.0019777326,0.001966813,0.0011027273,0.0013944337,0.0028496129,0.0018592195,0.0027570939],"category_scores_gemma":[0.009882776,0.00053827895,0.0012256253,0.0018893767,0.001016258,0.002041043,0.002057115,0.002508109,0.0017547606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0039915484,0.0026110194,0.01273697,0.00090591726,0.000583182,0.000612626,0.0003859887,0.2156073,0.032691557,0.00416179,0.11621267,0.60949934],"study_design_scores_gemma":[0.0002444283,0.0006807174,0.011323321,0.00008454203,0.00010165217,0.0005949753,0.0002205111,0.9257197,0.027956896,0.010233036,0.022754919,0.00008528664],"about_ca_topic_score_codex":0.005762383,"about_ca_topic_score_gemma":0.007827004,"teacher_disagreement_score":0.005762383,"about_ca_system_score_codex":0.0015254711,"about_ca_system_score_gemma":0.00095113134,"threshold_uncertainty_score":0.017888308},"labels":[],"label_agreement":null},{"id":"W2905969616","doi":"","title":"Multi-Projector Content Preservation with Linear Filters","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Projector; Computer vision; Artificial intelligence; Computer science; Redundancy (engineering); Pixel; Transformation (genetics); Computer graphics (images); Image quality; Content (measure theory); Mathematics; Image (mathematics)","score_opus":0.04144646022041148,"score_gpt":0.3168103369503841,"score_spread":0.2753638767299726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905969616","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012528776,0.00011573462,0.98581576,0.00006479586,0.000017847196,0.000026766327,0.00002995223,0.0008191187,0.00058122363],"genre_scores_gemma":[0.3824073,0.00038543096,0.60768455,0.0001859963,0.000080070735,0.00015913812,0.00026489055,0.0002533156,0.008579178],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942505,0.00008636788,0.00002863673,0.00016106496,0.00021161174,0.00008729434],"domain_scores_gemma":[0.9991999,0.00028582063,0.00011436157,0.00015414426,0.00020249016,0.00004330913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097114436,0.0010354666,0.00089598994,0.00079165224,0.0004275387,0.0010688244,0.0012900125,0.0012750208,0.002832026],"category_scores_gemma":[0.0017687188,0.0006912252,0.0011884255,0.0008746831,0.000806904,0.0015034268,0.001300963,0.0014202137,0.0012991849],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004227672,0.00025552805,0.00081391365,0.00013874797,0.00013881257,0.00009697622,0.00014038592,0.38977984,0.06947566,0.0065297717,0.002541441,0.52966624],"study_design_scores_gemma":[0.000007708938,0.00005243888,0.00019575657,0.000004725494,0.000011074077,0.0000317986,0.0000077583545,0.9861037,0.011745757,0.0011796178,0.00065003685,0.000009568536],"about_ca_topic_score_codex":0.007401903,"about_ca_topic_score_gemma":0.007574789,"teacher_disagreement_score":0.007401903,"about_ca_system_score_codex":0.0010253164,"about_ca_system_score_gemma":0.00096385303,"threshold_uncertainty_score":0.0147176385},"labels":[],"label_agreement":null},{"id":"W2906070710","doi":"","title":"Fast Radiometric Compensation for Nonlinear Projectors","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Compensation (psychology); Computer science; Lookup table; Computation; Computer vision; Projector; Radiometric dating; Artificial intelligence; Nonlinear system; Remote sensing; Algorithm; Geography","score_opus":0.029044199769413404,"score_gpt":0.32096584121485566,"score_spread":0.29192164144544225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906070710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025777876,0.00031696248,0.97130907,0.000053623946,0.000032700384,0.000032864704,0.00003578709,0.000708797,0.0017322332],"genre_scores_gemma":[0.20092978,0.0004199132,0.79451746,0.000034698107,0.00002979832,0.00005999122,0.00012127102,0.00011978264,0.0037673512],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943465,0.00007812756,0.000019450343,0.00008961658,0.00033698793,0.00004127427],"domain_scores_gemma":[0.9993144,0.00019652769,0.000103833285,0.00016941955,0.00019422047,0.000021528413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046594665,0.00065261126,0.00046513704,0.00049497746,0.00038571117,0.0005234558,0.0008229802,0.0004342696,0.0025710245],"category_scores_gemma":[0.0013226698,0.00036049073,0.00025351145,0.00057021546,0.00054208416,0.0010690215,0.0007646322,0.00078060507,0.00080868363],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002748989,0.00005718017,0.00073103386,0.00026127064,0.000036401187,0.0001134765,0.00016357611,0.02733797,0.5846487,0.012791084,0.0022016473,0.3713829],"study_design_scores_gemma":[0.000033801774,0.00011770456,0.0014532189,0.000023729415,0.000016646172,0.00041781142,0.000045788787,0.5666226,0.4187245,0.0034085736,0.009073862,0.00006170087],"about_ca_topic_score_codex":0.0014614953,"about_ca_topic_score_gemma":0.0026580594,"teacher_disagreement_score":0.0025710245,"about_ca_system_score_codex":0.000534296,"about_ca_system_score_gemma":0.00061606313,"threshold_uncertainty_score":0.008600891},"labels":[],"label_agreement":null},{"id":"W2906108313","doi":"","title":"Visually guided vergence in a new stereo camera system","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Vergence (optics); Foveal; Focus (optics); Stereopsis; Saccade; Gaze; Feature (linguistics); Computer graphics (images); Eye movement","score_opus":0.01517793210070564,"score_gpt":0.32399877067113997,"score_spread":0.30882083857043435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906108313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18292409,0.000611924,0.79258335,0.0004871775,0.0002553371,0.00016891051,0.00034888263,0.0032238765,0.019396376],"genre_scores_gemma":[0.7165463,0.00019278527,0.27219018,0.00025039798,0.00005970193,0.00006759105,0.00021003379,0.00007863943,0.010404455],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997793,0.000024825027,0.000006483905,0.000061725375,0.00010123584,0.000026552972],"domain_scores_gemma":[0.99984443,0.00002030621,0.000014658621,0.000032962056,0.00005670289,0.000030922194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026105563,0.00020702701,0.00024281231,0.0002143984,0.0002793318,0.000406689,0.0006798617,0.0005459441,0.002793403],"category_scores_gemma":[0.00046237715,0.00022140224,0.000254846,0.00014131874,0.0003061803,0.0005909703,0.00060752966,0.00042915897,0.0005331699],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007287228,0.000320994,0.0027225474,0.00018607806,0.000103434,0.0006004632,0.0004760637,0.056722645,0.6039804,0.024909973,0.009105222,0.30014345],"study_design_scores_gemma":[0.00025946475,0.0006669541,0.0065989494,0.000021914888,0.000060282076,0.00074656896,0.00007456404,0.9160163,0.0475448,0.0071182763,0.020789677,0.00010219054],"about_ca_topic_score_codex":0.007199634,"about_ca_topic_score_gemma":0.011203046,"teacher_disagreement_score":0.007199634,"about_ca_system_score_codex":0.00063483755,"about_ca_system_score_gemma":0.00068924594,"threshold_uncertainty_score":0.014315486},"labels":[],"label_agreement":null},{"id":"W2906143523","doi":"10.15353/jcvis.v4i1.328","title":"Near-field Sensors with Machine Learning for Breast Tumor Detection","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Terahertz technology and applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Breast tumor; Microwave; Antenna (radio); Near and far field; Reflection (computer programming); Electromagnetic field; Field (mathematics); Breast tissue; Computer science; Acoustics; Biomedical engineering; Optics; Physics; Breast cancer; Mathematics; Engineering; Medicine; Telecommunications; Cancer","score_opus":0.003311052166889452,"score_gpt":0.22393309405639578,"score_spread":0.22062204188950632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906143523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0121622635,0.0022496788,0.98199135,0.00031784244,0.00012567778,0.00003689019,0.00008956492,0.0011119954,0.00191481],"genre_scores_gemma":[0.4423236,0.0018979016,0.5474988,0.00040100754,0.0002975764,0.00017253222,0.00035842328,0.00009220825,0.0069580474],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952567,0.00014857987,0.000021193691,0.00011284468,0.00016353547,0.00002821475],"domain_scores_gemma":[0.9995975,0.00019607406,0.000053111453,0.000058087433,0.00008069549,0.000014495195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000603739,0.00053400884,0.00059261615,0.0005831573,0.00017577088,0.00047431595,0.00072790054,0.0009976141,0.002616282],"category_scores_gemma":[0.0015656346,0.00028627814,0.0003874682,0.0006821193,0.00032515178,0.00077931373,0.00053503545,0.00077665626,0.0015806021],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019223004,0.00024741908,0.0015953134,0.0002357526,0.00012271739,0.000105292595,0.000056006615,0.15079808,0.062311474,0.011010944,0.0045214146,0.76880336],"study_design_scores_gemma":[0.0000065835516,0.0000708951,0.0006599856,0.000011551805,0.000009795198,0.000074823976,0.000011398857,0.97919923,0.010393018,0.006064529,0.003482581,0.000015696032],"about_ca_topic_score_codex":0.00043190623,"about_ca_topic_score_gemma":0.0005781372,"teacher_disagreement_score":0.002616282,"about_ca_system_score_codex":0.00027188074,"about_ca_system_score_gemma":0.00023851033,"threshold_uncertainty_score":0.008752346},"labels":[],"label_agreement":null},{"id":"W2906161254","doi":"10.15353/jcvis.v4i1.325","title":"Non-invasive Glucose Monitoring at mm-Wave Frequencies","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Research Institute for Aging, University of Waterloo; Natural Sciences and Engineering Research Council of Canada","keywords":"Radar; Dielectric; Environmental science; Materials science; Computer science; Telecommunications; Optoelectronics","score_opus":0.013267375879077555,"score_gpt":0.3242235205525173,"score_spread":0.31095614467343974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906161254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16833375,0.32022315,0.49418765,0.0023911246,0.0018624023,0.00021353402,0.0007393856,0.001069198,0.010979883],"genre_scores_gemma":[0.5807455,0.17676388,0.22269242,0.0033808548,0.0026054138,0.00031019378,0.0009631554,0.00020896677,0.012329536],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925524,0.0001581848,0.000034551576,0.00019319094,0.0003195863,0.00003923265],"domain_scores_gemma":[0.9991761,0.00041801613,0.00016858947,0.000049951814,0.00015830931,0.00002912841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070974516,0.0006598473,0.0006865181,0.00047310148,0.000121905876,0.00078296935,0.00089450233,0.000874067,0.0011742555],"category_scores_gemma":[0.0009911484,0.00022646868,0.00032865477,0.00046370155,0.00029754997,0.00093954586,0.0004726325,0.0007292086,0.000666438],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083988806,0.00015831851,0.0046433676,0.002624138,0.00015403198,0.000536645,0.00013874604,0.0010097884,0.6322739,0.0015899447,0.003173302,0.3528579],"study_design_scores_gemma":[0.00005900846,0.0015674512,0.019905185,0.00044567653,0.0003708906,0.005477222,0.00018415022,0.015403733,0.8911339,0.0020349438,0.06324221,0.00017568136],"about_ca_topic_score_codex":0.00023496078,"about_ca_topic_score_gemma":0.0002874665,"teacher_disagreement_score":0.0011742555,"about_ca_system_score_codex":0.00022249547,"about_ca_system_score_gemma":0.00013650581,"threshold_uncertainty_score":0.003928244},"labels":[],"label_agreement":null},{"id":"W2906164531","doi":"10.15353/jcvis.v4i1.340","title":"MonolithNet: Training monolithic deep neural networks via a partitioned training strategy","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Training (meteorology); Artificial neural network; Computer science; Artificial intelligence; Residual; Deep neural networks; Stochastic gradient descent; Convergence (economics); Deep learning; Gradient descent; Machine learning; Algorithm; Geography","score_opus":0.029801870094672972,"score_gpt":0.29968007370889693,"score_spread":0.269878203614224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906164531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0419478,0.00043760554,0.95056665,0.00020756341,0.00007801403,0.000113305025,0.00012261,0.0027970988,0.003729435],"genre_scores_gemma":[0.57135564,0.00025496382,0.42033234,0.0004876218,0.000060275557,0.00035954601,0.0005975661,0.000505473,0.0060465415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997075,0.00007530191,0.000016364938,0.0000965391,0.000060271756,0.000043973636],"domain_scores_gemma":[0.9995183,0.00018864118,0.00003735881,0.00012708228,0.00009187256,0.00003675577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007647743,0.0012338725,0.00059617107,0.00035022484,0.0003497077,0.00059363886,0.0018995907,0.0008374729,0.004250835],"category_scores_gemma":[0.0017419071,0.00063162774,0.00040250574,0.0003320356,0.00072392734,0.002229672,0.0013079777,0.0012998,0.0011608391],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003608553,0.0002708642,0.001530101,0.00020558015,0.00013634567,0.00020171636,0.00015892547,0.71834195,0.027655318,0.016070744,0.0068485755,0.22821897],"study_design_scores_gemma":[0.000020181196,0.0000868454,0.000094115916,0.000008488425,0.0000106667385,0.00003300545,0.000012896616,0.98968655,0.005662373,0.0033390482,0.00104106,0.0000047244207],"about_ca_topic_score_codex":0.002234423,"about_ca_topic_score_gemma":0.006344077,"teacher_disagreement_score":0.004250835,"about_ca_system_score_codex":0.000512764,"about_ca_system_score_gemma":0.00093793054,"threshold_uncertainty_score":0.014220417},"labels":[],"label_agreement":null},{"id":"W2906232045","doi":"","title":"Understanding Blur and Model Learning in Projector Compensation","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Projector; Computer science; Computer vision; Artificial intelligence; Point (geometry); Compensation (psychology); Perception; Computer graphics (images); Mathematics","score_opus":0.044818143818419164,"score_gpt":0.3104162512863905,"score_spread":0.26559810746797136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906232045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049785618,0.0007189244,0.948277,0.00043163836,0.000029455075,0.000012681355,0.000015246532,0.00012735829,0.00060208695],"genre_scores_gemma":[0.86397654,0.0012648351,0.13295417,0.00013673278,0.00008782635,0.000034889097,0.000042793432,0.00005254143,0.0014497866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996573,0.00013252502,0.000016801074,0.000061164246,0.00008709416,0.000045139448],"domain_scores_gemma":[0.99793696,0.0013728943,0.00018028026,0.0002269714,0.00022388875,0.000059086906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011332629,0.00050750206,0.0005899494,0.00033803663,0.0002978545,0.0010485322,0.00066818536,0.0010616696,0.0007979407],"category_scores_gemma":[0.0069808713,0.00042457084,0.0003857533,0.00037420244,0.0008347259,0.0028571836,0.001210946,0.0013003746,0.0001076222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019564865,0.00011099691,0.0014343595,0.00013505996,0.00005769349,0.000107349435,0.00032048716,0.79330915,0.017200414,0.036910314,0.0008680793,0.14935045],"study_design_scores_gemma":[0.0000034652585,0.000019208039,0.00020102218,0.000002751187,0.0000039397505,0.000015030192,0.0000117670625,0.99144816,0.0015246725,0.0066137267,0.00014945412,0.0000068539853],"about_ca_topic_score_codex":0.004922204,"about_ca_topic_score_gemma":0.0030847918,"teacher_disagreement_score":0.004922204,"about_ca_system_score_codex":0.000616931,"about_ca_system_score_gemma":0.0005332645,"threshold_uncertainty_score":0.009787142},"labels":[],"label_agreement":null},{"id":"W2906361685","doi":"","title":"OLIV: An Artificial Intelligence-Powered Assistant for Object Localization for Impaired Vision","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Object (grammar); Computer science; Artificial intelligence; Object detection; Human–computer interaction; Computer vision; Cognitive neuroscience of visual object recognition; Natural language processing; Segmentation","score_opus":0.055040076432918354,"score_gpt":0.3733454231758003,"score_spread":0.3183053467428819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906361685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08841275,0.0006499797,0.8458071,0.0003983393,0.00027602332,0.00049859675,0.0005015995,0.043392148,0.020063512],"genre_scores_gemma":[0.5041659,0.0008281967,0.44857532,0.0012281468,0.00014840999,0.00043482726,0.0010766326,0.0008641917,0.042678446],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975985,0.000023279661,0.000014097291,0.00005092408,0.000109644,0.000042264313],"domain_scores_gemma":[0.99963367,0.00011334052,0.000038011127,0.000049429647,0.00009096265,0.00007458844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029233503,0.0008169385,0.000476881,0.0004915843,0.0003597202,0.0007478373,0.0016947308,0.0006450772,0.008779967],"category_scores_gemma":[0.0010877791,0.00020428942,0.00028735664,0.00018050343,0.0003857397,0.0011935459,0.0017860781,0.00049037806,0.0031532326],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017554275,0.00062578946,0.0047181603,0.00075769937,0.00008663198,0.002423035,0.0013071406,0.003755328,0.18236427,0.0052686734,0.033357453,0.7635804],"study_design_scores_gemma":[0.000595799,0.0038683899,0.0145035265,0.00045629204,0.000530048,0.012323587,0.0013563226,0.29217112,0.27830428,0.013336589,0.38212812,0.00042595473],"about_ca_topic_score_codex":0.0011022183,"about_ca_topic_score_gemma":0.0019472333,"teacher_disagreement_score":0.008779967,"about_ca_system_score_codex":0.00025427248,"about_ca_system_score_gemma":0.000556257,"threshold_uncertainty_score":0.029371917},"labels":[],"label_agreement":null},{"id":"W2906422279","doi":"","title":"Intuitive Data-Driven Visualization of Food Relatedness via t-Distributed Stochastic Neighbor Embedding","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Embedding; Cluster analysis; Context (archaeology); Intuition; Visualization; Heuristic; Data mining; Machine learning; Artificial intelligence; Psychology; Biology","score_opus":0.024099524233126914,"score_gpt":0.342303351972302,"score_spread":0.3182038277391751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906422279","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09641487,0.00034343684,0.8962522,0.0004022534,0.00007220761,0.0000972646,0.0017951883,0.0029408576,0.0016817817],"genre_scores_gemma":[0.44903454,0.00045102675,0.54560536,0.0001254542,0.000038105445,0.00021861769,0.0023869227,0.00044279668,0.0016971363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972326,0.00009590918,0.000018031165,0.00007102558,0.00006753815,0.000024277195],"domain_scores_gemma":[0.9990246,0.0004883642,0.00011651965,0.00011330237,0.00019346195,0.0000638447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006870823,0.00077648496,0.00048621272,0.0014267014,0.00027228464,0.0012191108,0.0006142588,0.00061496324,0.0035103788],"category_scores_gemma":[0.0032030526,0.00029455865,0.0005513292,0.0010631497,0.00037607102,0.0014116996,0.0017591396,0.00085584685,0.0005734294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00141291,0.00037591424,0.018548336,0.0013248002,0.00029273215,0.0008707812,0.0029481978,0.3075385,0.14321296,0.04197807,0.015719993,0.46577683],"study_design_scores_gemma":[0.000042003718,0.00008682249,0.004341483,0.000039425926,0.0000233126,0.00023267143,0.00031686222,0.9537588,0.011785088,0.023788536,0.0055286717,0.00005629414],"about_ca_topic_score_codex":0.002021221,"about_ca_topic_score_gemma":0.0034307106,"teacher_disagreement_score":0.0035103788,"about_ca_system_score_codex":0.00035828358,"about_ca_system_score_gemma":0.0004708976,"threshold_uncertainty_score":0.011743426},"labels":[],"label_agreement":null},{"id":"W2906501467","doi":"10.15353/jcvis.v4i1.329","title":"On Robustness of Deep Neural Networks: A Comprehensive Study on the Effect of Architecture and Weight Initialization to Susceptibility and Transferability of Adversarial Attacks","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Robustness (evolution); Initialization; Transferability; Computer science; Adversarial system; Artificial neural network; Network architecture; Artificial intelligence; Network model; Architecture; Machine learning; Data mining; Computer security","score_opus":0.009030126548489809,"score_gpt":0.2898208874482386,"score_spread":0.28079076089974875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906501467","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55192065,0.013833754,0.41952714,0.0018514636,0.00035030165,0.0003096478,0.0005405101,0.0017069549,0.009959643],"genre_scores_gemma":[0.97967505,0.0026136388,0.01572123,0.0001517984,0.00007179063,0.000066029424,0.00025392152,0.00016202108,0.0012844583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964574,0.001344149,0.00033812516,0.000525247,0.0009764778,0.00035867942],"domain_scores_gemma":[0.9580013,0.031992782,0.0035273694,0.004369487,0.0016558198,0.00045320878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065094107,0.0022321534,0.0011224974,0.001639439,0.00062065653,0.0012930072,0.0010798733,0.0015460425,0.0018317795],"category_scores_gemma":[0.041471265,0.00065760413,0.0011926645,0.0008387279,0.0018016762,0.0033808707,0.0021706964,0.0026567348,0.00031271062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038990597,0.00015123638,0.003817236,0.00034892996,0.00033432298,0.0001428484,0.00007870051,0.9287296,0.009087333,0.004518242,0.00065832067,0.051743254],"study_design_scores_gemma":[0.000019528881,0.0007374762,0.0031471325,0.00016734032,0.00016526665,0.00022415278,0.000066600915,0.95739007,0.02753893,0.009426382,0.0010626998,0.000054368964],"about_ca_topic_score_codex":0.0022433726,"about_ca_topic_score_gemma":0.0018223458,"teacher_disagreement_score":0.0065094107,"about_ca_system_score_codex":0.0014377862,"about_ca_system_score_gemma":0.0007594603,"threshold_uncertainty_score":0.034425437},"labels":[],"label_agreement":null},{"id":"W2906568950","doi":"10.15353/jcvis.v4i1.326","title":"ConvART: Improving Adaptive Resonance Theory for Unsupervised Image Clustering","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adaptive resonance theory; Cluster analysis; Computer science; Artificial intelligence; Benchmark (surveying); Image (mathematics); Unsupervised learning; Pattern recognition (psychology); Convolutional neural network; Spectral clustering; Distortion (music); Stability (learning theory); Machine learning; Artificial neural network","score_opus":0.012384101255200234,"score_gpt":0.2747517400077667,"score_spread":0.26236763875256647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906568950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030100904,0.00018888697,0.99382234,0.000085204236,0.000039593615,0.000033393164,0.000059512382,0.0014246386,0.0013363935],"genre_scores_gemma":[0.14301553,0.00048037225,0.846618,0.00041782137,0.00017597272,0.0002516518,0.000824109,0.0010983845,0.0071181464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902713,0.00023646976,0.00003554135,0.0002554474,0.00037714644,0.000068309615],"domain_scores_gemma":[0.99869555,0.00045971232,0.00013359357,0.0002737523,0.0003774767,0.000059956332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015377552,0.0013700585,0.0010504014,0.0020227202,0.00072418747,0.0011610183,0.0028466904,0.0016601842,0.0032451488],"category_scores_gemma":[0.004024928,0.0006272144,0.0012855608,0.0013654402,0.0010832379,0.0021862607,0.0018399863,0.0018042781,0.0025050915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001728011,0.00015151763,0.00079543097,0.0002201944,0.00018680957,0.00014330141,0.00018772575,0.4923747,0.023492603,0.037551966,0.0154323885,0.42929065],"study_design_scores_gemma":[0.000006209015,0.0000191394,0.00010120369,0.000005753303,0.0000070939413,0.000028411101,0.000009152897,0.98727137,0.0026650464,0.007854191,0.0020221297,0.00001022388],"about_ca_topic_score_codex":0.0060249693,"about_ca_topic_score_gemma":0.007360487,"teacher_disagreement_score":0.0060249693,"about_ca_system_score_codex":0.000999544,"about_ca_system_score_gemma":0.00084933825,"threshold_uncertainty_score":0.011979759},"labels":[],"label_agreement":null},{"id":"W2962752334","doi":"10.15353/vsnl.v3i1.171","title":"Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":369,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Computer science; Object detection; Artificial intelligence; Inference; Speedup; Computer vision; Object (grammar); Frame rate; Viola–Jones object detection framework; Deep learning; Leverage (statistics); Real-time computing; Pattern recognition (psychology); Parallel computing; Face detection","score_opus":0.011080841512763487,"score_gpt":0.29477906507503077,"score_spread":0.28369822356226726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962752334","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040091574,0.001354715,0.9034333,0.00029372657,0.0003352186,0.00035039496,0.00067543326,0.047920816,0.0055447407],"genre_scores_gemma":[0.31307852,0.0007927556,0.66815364,0.0009128784,0.00013806687,0.00046605262,0.0027608348,0.0017900841,0.011907173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997093,0.00002523975,0.0000096662125,0.0000831031,0.00011370671,0.00005899859],"domain_scores_gemma":[0.99972564,0.00005572626,0.000030064013,0.00005562092,0.00008753379,0.000045469485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049717224,0.0011193163,0.00071546045,0.00078233646,0.00038693205,0.0008685241,0.0024388826,0.00072908774,0.0058920407],"category_scores_gemma":[0.0012575712,0.00045899276,0.0003875607,0.000292812,0.00037872326,0.0016870045,0.0014229504,0.0009868963,0.0014713128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014437957,0.00034813385,0.0022393744,0.00029272298,0.00014865819,0.00040852398,0.00022977252,0.035788074,0.12223105,0.006338178,0.06664778,0.7638839],"study_design_scores_gemma":[0.00013094234,0.00037184506,0.0011227919,0.00003829218,0.000047085414,0.00020173915,0.000047876736,0.928446,0.04506101,0.0021937592,0.022274965,0.00006370091],"about_ca_topic_score_codex":0.008864671,"about_ca_topic_score_gemma":0.014636396,"teacher_disagreement_score":0.008864671,"about_ca_system_score_codex":0.0007411106,"about_ca_system_score_gemma":0.0011339659,"threshold_uncertainty_score":0.019710839},"labels":[],"label_agreement":null},{"id":"W2996857901","doi":"","title":"Fast Minutia-based Palmprint Matching Using CNN and Generalized Hough Transform","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Minutiae; Artificial intelligence; Hough transform; Convolutional neural network; Computer science; Matching (statistics); Computer vision; Pattern recognition (psychology); Rotation (mathematics); Image (mathematics); Process (computing); Mathematics; Fingerprint recognition; Fingerprint (computing)","score_opus":0.013762015257125754,"score_gpt":0.2739707817555335,"score_spread":0.26020876649840774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996857901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07487928,0.0010874976,0.91553485,0.00014245111,0.00010300202,0.00011110621,0.0002279995,0.004988901,0.0029250067],"genre_scores_gemma":[0.5553694,0.0010606133,0.43456954,0.00016182872,0.000061629034,0.000107021035,0.00072703225,0.00022916248,0.0077137426],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992555,0.000048776878,0.000028892564,0.00016321754,0.0003974412,0.00010614299],"domain_scores_gemma":[0.99968815,0.000052180843,0.000041202853,0.00007698616,0.00012185842,0.000019610035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004196037,0.00068336265,0.00094997033,0.0014891841,0.0002941405,0.0006516454,0.0010997908,0.0005598694,0.003073309],"category_scores_gemma":[0.0008243867,0.00046957168,0.0006325566,0.0013559058,0.00030739704,0.0012625909,0.0008706154,0.0005164596,0.0010741011],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036448138,0.00010171677,0.0016612473,0.00012895389,0.00011707127,0.00014410261,0.000048258386,0.035205185,0.10136193,0.0015283329,0.002749996,0.85658866],"study_design_scores_gemma":[0.000018047185,0.00008762321,0.0032843796,0.000010448218,0.000034328765,0.00041409858,0.000027808743,0.9265446,0.06542481,0.0011240666,0.0030013958,0.000028418213],"about_ca_topic_score_codex":0.010890651,"about_ca_topic_score_gemma":0.013223774,"teacher_disagreement_score":0.010890651,"about_ca_system_score_codex":0.00073229295,"about_ca_system_score_gemma":0.0008536039,"threshold_uncertainty_score":0.021654546},"labels":[],"label_agreement":null},{"id":"W2996942694","doi":"","title":"2D-Multiple Signal Processing Approach to Human Orientation Monitoring Using Millimeter-wave FMCW Radar","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Extremely high frequency; Orientation (vector space); Radar; Computer science; Continuous-wave radar; Computer vision; SIGNAL (programming language); Signal processing; Continuous wave; Acoustics; Artificial intelligence; Electronic engineering; Engineering; Telecommunications; Radar imaging; Optics; Physics","score_opus":0.022545793273549032,"score_gpt":0.2693451496160119,"score_spread":0.2467993563424629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996942694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022728099,0.00030668103,0.9965293,0.00007416333,0.00005702787,0.0000121981075,0.000016134596,0.00010296381,0.0006288028],"genre_scores_gemma":[0.17800821,0.0022918824,0.811147,0.00029347325,0.00040140245,0.00014968056,0.00019744724,0.000076337594,0.0074345805],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997085,0.0000642059,0.000013982424,0.00008478388,0.000105760955,0.000022830875],"domain_scores_gemma":[0.99984145,0.000055327804,0.000023118917,0.00002320865,0.000047769932,0.000009090328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030571947,0.00066549174,0.00050030224,0.0005680559,0.00017925206,0.0005915482,0.0006006008,0.00075314107,0.0016230334],"category_scores_gemma":[0.00057859806,0.00027103597,0.00065837294,0.0007488874,0.00032672222,0.00062134577,0.00059771293,0.0007055071,0.0008827758],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017369846,0.00016883085,0.00085288327,0.00037379417,0.00014023627,0.00037492215,0.0002367267,0.18356416,0.14186527,0.024928117,0.003346646,0.6439747],"study_design_scores_gemma":[0.000013400704,0.00014253696,0.00084637856,0.000015749598,0.000025675463,0.0002563222,0.00003425444,0.97727317,0.010273731,0.0053880173,0.0057077263,0.00002301347],"about_ca_topic_score_codex":0.0007614724,"about_ca_topic_score_gemma":0.0007877537,"teacher_disagreement_score":0.0016230334,"about_ca_system_score_codex":0.00017296165,"about_ca_system_score_gemma":0.00028678353,"threshold_uncertainty_score":0.005429566},"labels":[],"label_agreement":null},{"id":"W2997004138","doi":"","title":"Remote Sensing of Blood Glucose Level Using an FMCW Radar Sensor","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Remote sensing; Radar; Reliability (semiconductor); Extremely high frequency; Blood glucose monitoring; Diabetes mellitus; Environmental science; Medicine; Computer science; Power (physics); Telecommunications; Endocrinology; Geology; Physics","score_opus":0.018852498670128632,"score_gpt":0.3407219249598508,"score_spread":0.3218694262897222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997004138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29749742,0.002872837,0.6916698,0.0005995601,0.000567294,0.00009028569,0.0001974424,0.0010232412,0.0054821293],"genre_scores_gemma":[0.76323575,0.0015091052,0.2304627,0.0005670009,0.0002065021,0.00007502449,0.00017674317,0.00003241141,0.0037347458],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972993,0.000048324182,0.000010361451,0.00007341083,0.000111200905,0.000026736565],"domain_scores_gemma":[0.99986887,0.000040018265,0.00003120361,0.000016150903,0.000033123764,0.000010628772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026433158,0.0004409947,0.00035764594,0.00026900682,0.00011045223,0.00037092104,0.00041978314,0.0007329035,0.0006426659],"category_scores_gemma":[0.00035152037,0.00014602415,0.0002567767,0.0002890181,0.00017156174,0.00040910154,0.00035011035,0.00040536816,0.00041574833],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015558989,0.00008905588,0.0010113249,0.00013241793,0.000024683839,0.00014473805,0.000036825142,0.001395034,0.9357166,0.00070479076,0.0005616722,0.060027182],"study_design_scores_gemma":[0.00007241712,0.0014597179,0.0067716497,0.000038663882,0.00010466823,0.0019255573,0.00007108054,0.12411109,0.85646844,0.00084001437,0.008059848,0.00007690815],"about_ca_topic_score_codex":0.00014885324,"about_ca_topic_score_gemma":0.00016946664,"teacher_disagreement_score":0.0007329035,"about_ca_system_score_codex":0.00014530854,"about_ca_system_score_gemma":0.00011426742,"threshold_uncertainty_score":0.00214988},"labels":[],"label_agreement":null},{"id":"W2997197218","doi":"","title":"DeepLABNet: End-to-end Learning of Deep Radial Basis Networks","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"End-to-end principle; Radial basis function; Deep learning; Artificial intelligence; Computer science; Artificial neural network; Activation function; Basis (linear algebra); Radial basis function network; Function (biology); Mathematics","score_opus":0.0058472219097668665,"score_gpt":0.2467689429445632,"score_spread":0.24092172103479634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997197218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055712555,0.0001871454,0.979704,0.00015161162,0.00006845739,0.000077888166,0.00033389917,0.012325169,0.001580537],"genre_scores_gemma":[0.21294263,0.00030536915,0.7734252,0.0003623828,0.000075600525,0.00043838995,0.002398499,0.0016283876,0.008423496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993975,0.00012654124,0.00002956631,0.00015237651,0.00021536376,0.00007858361],"domain_scores_gemma":[0.9991291,0.00030066154,0.00006587622,0.00021163867,0.00022576115,0.00006703019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014659327,0.0016257068,0.00093925005,0.0005581508,0.0004841492,0.001362003,0.0027057081,0.0018807405,0.00940966],"category_scores_gemma":[0.004740358,0.0007536731,0.0006206175,0.0005693112,0.00068327505,0.002529246,0.0023645272,0.003373706,0.004712745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041728804,0.00030929304,0.0011098074,0.00024836115,0.00013055389,0.00024995412,0.00012600611,0.4819291,0.012014567,0.024849214,0.032208633,0.44640726],"study_design_scores_gemma":[0.000012565592,0.00002564036,0.000057924615,0.000007773116,0.0000033625233,0.000018196673,0.000005691305,0.9866052,0.0045803227,0.006719342,0.001956782,0.00000712734],"about_ca_topic_score_codex":0.004005394,"about_ca_topic_score_gemma":0.0074409633,"teacher_disagreement_score":0.00940966,"about_ca_system_score_codex":0.00086563535,"about_ca_system_score_gemma":0.0014671297,"threshold_uncertainty_score":0.031478465},"labels":[],"label_agreement":null},{"id":"W2997236691","doi":"","title":"Why Do I Trust Your Model? Building and Explaining Predictive Models for Peritoneal Dialysis Eligibility","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Interpretability; Accountability; Peritoneal dialysis; Health care; Transparency (behavior); Computer science; Realm; Medicine; Artificial intelligence; Machine learning; Political science; Law; Computer security; Internal medicine","score_opus":0.025356208312936335,"score_gpt":0.3226810353900463,"score_spread":0.29732482707710994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997236691","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13553645,0.00065615494,0.82706594,0.030185128,0.00020206138,0.00012295575,0.0008679995,0.0007537899,0.0046094516],"genre_scores_gemma":[0.8938115,0.0003613924,0.10325553,0.00103357,0.00011633242,0.00009597356,0.00045134214,0.00012129674,0.0007529565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961844,0.0025516537,0.00017214686,0.00051358156,0.00038430924,0.00019386213],"domain_scores_gemma":[0.950636,0.041236356,0.0033194204,0.0026482223,0.0017618014,0.00039824325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009341119,0.00071549835,0.0006249213,0.0011270231,0.0006328941,0.003148934,0.0015850842,0.0015852866,0.0025993502],"category_scores_gemma":[0.06832444,0.0004920686,0.0011255136,0.0007916842,0.0018922195,0.004570394,0.0017971762,0.0034560945,0.0003747476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054804253,0.00028811395,0.07492587,0.0005543103,0.0006489987,0.0010024691,0.00778415,0.41454583,0.0022547643,0.34734496,0.01630403,0.13379848],"study_design_scores_gemma":[0.000043194184,0.000045176548,0.004212121,0.00015935759,0.00010499456,0.00013189553,0.0005443523,0.74960715,0.0008099188,0.24003096,0.004257874,0.00005303403],"about_ca_topic_score_codex":0.008403356,"about_ca_topic_score_gemma":0.0077548292,"teacher_disagreement_score":0.009341119,"about_ca_system_score_codex":0.0016622918,"about_ca_system_score_gemma":0.0016407906,"threshold_uncertainty_score":0.049401164},"labels":[],"label_agreement":null},{"id":"W2997253686","doi":"","title":"Sensitivity Assessment for Projector Camera Geometry Reconstruction Systems","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sensitivity (control systems); Projector; Principal (computer security); Point (geometry); Computer vision; Mathematics; Artificial intelligence; Computer science; Geometry; Optics; Physics; Engineering","score_opus":0.017898445152587504,"score_gpt":0.3073568121318725,"score_spread":0.289458366979285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997253686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09698646,0.0018157918,0.893287,0.00028914958,0.000056067947,0.00018550915,0.00022823717,0.0007811247,0.006370732],"genre_scores_gemma":[0.86291945,0.0011684771,0.13267688,0.00016044763,0.000059541795,0.000095688774,0.00051390764,0.00020658075,0.0021989725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99240476,0.003176785,0.0002910795,0.00060600665,0.003221766,0.00029966538],"domain_scores_gemma":[0.9779393,0.01624282,0.0008749406,0.0015949329,0.0031088756,0.00023916946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062308484,0.0011148045,0.0006946612,0.002333318,0.000505266,0.0015651687,0.00072550547,0.0014662036,0.0028128228],"category_scores_gemma":[0.036951955,0.0005712566,0.0007273323,0.0011207338,0.000728361,0.0016741753,0.0026359065,0.00083972793,0.00068028364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016274359,0.00017588749,0.010658147,0.0009061735,0.00047844843,0.00041733956,0.00047704283,0.65316844,0.06241761,0.018710406,0.0023636245,0.24859951],"study_design_scores_gemma":[0.00001952167,0.00039436217,0.0074680657,0.00009852771,0.0001039436,0.0007789929,0.00014352714,0.9412401,0.03865861,0.008700962,0.0023025526,0.00009086584],"about_ca_topic_score_codex":0.0016764372,"about_ca_topic_score_gemma":0.00096818485,"teacher_disagreement_score":0.0062308484,"about_ca_system_score_codex":0.0012825998,"about_ca_system_score_gemma":0.00046857816,"threshold_uncertainty_score":0.03295225},"labels":[],"label_agreement":null},{"id":"W2997495177","doi":"","title":"Integration of Random Forests and MM-Wave FMCW Radar Technology for Gait Recognition","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Random forest; Radar; Computer science; Gait; Artificial intelligence; Remote sensing; Physical medicine and rehabilitation; Telecommunications; Geography; Medicine","score_opus":0.009894246554338883,"score_gpt":0.23841567097845873,"score_spread":0.22852142442411985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997495177","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017785523,0.00033068136,0.9800305,0.00012393777,0.000117910444,0.000028232678,0.00006168895,0.00043064865,0.0010908886],"genre_scores_gemma":[0.3854186,0.000551381,0.61028075,0.00030265903,0.00019732893,0.000086623455,0.0003189504,0.00006622323,0.0027775064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934167,0.00013751788,0.000034584238,0.00014012602,0.00026985203,0.00007630947],"domain_scores_gemma":[0.99930525,0.00026580718,0.00009317728,0.00007682088,0.00022479218,0.00003419827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093706144,0.00050585513,0.0004168573,0.00079038664,0.00014963823,0.000497699,0.00045610848,0.0005450683,0.0007965561],"category_scores_gemma":[0.0013550196,0.00022213894,0.00047428746,0.0005165878,0.00021785744,0.0007809581,0.0003400299,0.00042747616,0.0006708439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020476853,0.00038334497,0.0059143235,0.00019756409,0.00014778605,0.00023748152,0.00005547201,0.04748252,0.29065305,0.0066763186,0.0040278044,0.6440196],"study_design_scores_gemma":[0.000018150282,0.0003636203,0.006084778,0.00004121869,0.0000668463,0.0005864185,0.000028696988,0.8785342,0.1013698,0.005954788,0.006889537,0.00006192067],"about_ca_topic_score_codex":0.0006928652,"about_ca_topic_score_gemma":0.0015911616,"teacher_disagreement_score":0.00093706144,"about_ca_system_score_codex":0.00017728914,"about_ca_system_score_gemma":0.00022117648,"threshold_uncertainty_score":0.004955709},"labels":[],"label_agreement":null},{"id":"W2997556918","doi":"","title":"On the Use of Low-Cost Radars and Machine Learning for In-Vehicle Passenger Detection","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Capon; Radar; Computer science; Artificial intelligence; Real-time computing; Beamforming; Telecommunications","score_opus":0.014371326852123908,"score_gpt":0.2283615228017572,"score_spread":0.21399019594963328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997556918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009919949,0.0019401906,0.9847194,0.00029621882,0.000090514535,0.00002532941,0.000024363144,0.00035355272,0.0026304633],"genre_scores_gemma":[0.30347127,0.005182022,0.6823624,0.0005629666,0.0003998918,0.0000651322,0.00015728157,0.000092043345,0.007706993],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993919,0.00020752141,0.000021109508,0.00011274225,0.00022760859,0.000039193365],"domain_scores_gemma":[0.99880683,0.000676111,0.00009509217,0.0001452899,0.00025492458,0.000021706976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008406494,0.00080191623,0.0004986649,0.00067012245,0.00020233035,0.0007240543,0.00073597213,0.0008292648,0.0024818326],"category_scores_gemma":[0.0018747055,0.00025345825,0.00031490237,0.0006844084,0.0004569447,0.0012369632,0.0005096187,0.00061418617,0.0012201495],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015651553,0.00014921349,0.0020435709,0.00032440003,0.000106922176,0.00017155803,0.00004873334,0.042305913,0.085193306,0.020679025,0.0021234327,0.8466975],"study_design_scores_gemma":[0.000039723243,0.00066684396,0.0045829443,0.00009870652,0.00013455693,0.00096491975,0.00006110699,0.81008005,0.1303085,0.016909383,0.036056776,0.00009641392],"about_ca_topic_score_codex":0.00068278005,"about_ca_topic_score_gemma":0.00085611036,"teacher_disagreement_score":0.0024818326,"about_ca_system_score_codex":0.00021646623,"about_ca_system_score_gemma":0.00027658028,"threshold_uncertainty_score":0.00830251},"labels":[],"label_agreement":null},{"id":"W2997616821","doi":"","title":"Generative Modeling for Retinal Fundus Image Synthesis","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Fundus (uterus); Generative grammar; Image (mathematics); Annotation; Generative adversarial network; Generative model; Deep learning; Residual; Encoder; Pattern recognition (psychology); Computer vision; Algorithm; Medicine; Ophthalmology","score_opus":0.015503852863635046,"score_gpt":0.31356338756035956,"score_spread":0.2980595346967245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997616821","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01721222,0.00044744345,0.97939426,0.00038390455,0.000039805534,0.00004544401,0.00024211354,0.00058406795,0.0016507176],"genre_scores_gemma":[0.81272644,0.00068538054,0.1784394,0.00038486294,0.00008085493,0.00016098305,0.0009044504,0.00031318227,0.0063043465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996275,0.0001360558,0.000013814952,0.000087597924,0.000101291735,0.000033783344],"domain_scores_gemma":[0.9990883,0.0005858751,0.00009266086,0.000116720155,0.00007989877,0.000036516536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097337825,0.0005983605,0.00048237454,0.00053067063,0.0001913521,0.000676632,0.00075078674,0.00075939554,0.0022854763],"category_scores_gemma":[0.003114921,0.0005500543,0.0009568507,0.0003377667,0.00072767516,0.0005033197,0.0009706694,0.001188759,0.00047197743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083688195,0.00002641616,0.0007371423,0.00004698065,0.000047513142,0.00009529532,0.000046613888,0.9449804,0.0066711744,0.016918803,0.0012415496,0.029104324],"study_design_scores_gemma":[0.0000038778107,0.000007636449,0.00009469791,0.0000042262695,0.0000038958256,0.00002823618,0.0000023449918,0.99409676,0.0009625347,0.004397441,0.0003943527,0.000004120977],"about_ca_topic_score_codex":0.0044100527,"about_ca_topic_score_gemma":0.004300982,"teacher_disagreement_score":0.0044100527,"about_ca_system_score_codex":0.0008998309,"about_ca_system_score_gemma":0.0005402839,"threshold_uncertainty_score":0.008768797},"labels":[],"label_agreement":null},{"id":"W2997637596","doi":"","title":"Zone-DR: Discovery Radiomics via Zone-level Deep Radiomic Sequencer Discovery for Zone-based Prostate Cancer Grading using Diffusion Weighted Imaging","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Radiomics; Prostate cancer; Grading (engineering); Medicine; Prostate; Diffusion MRI; Medical physics; Artificial intelligence; Radiology; Cancer; Computer science; Magnetic resonance imaging; Internal medicine","score_opus":0.017166095800419324,"score_gpt":0.30044156458497573,"score_spread":0.2832754687845564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997637596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057696216,0.00069287006,0.9383805,0.00017421285,0.000032636934,0.000099254496,0.00016558524,0.0016322717,0.0011265714],"genre_scores_gemma":[0.48617405,0.00036615957,0.51060003,0.00020376682,0.000043017906,0.00008647907,0.00044810132,0.00014683465,0.0019316161],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932015,0.00018254828,0.000044205255,0.00018462508,0.00019445491,0.000074087715],"domain_scores_gemma":[0.9987871,0.00039639854,0.0002446138,0.0002467753,0.00023677941,0.000088379325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001472116,0.00072255573,0.00070149754,0.0013718656,0.00027453713,0.00093294034,0.00114462,0.0007874689,0.0010763551],"category_scores_gemma":[0.0030303132,0.00033359157,0.0007073764,0.0005940053,0.00052392745,0.0011205577,0.0014741556,0.00079989876,0.00060841884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010833014,0.00024487582,0.011986262,0.0005438611,0.00021300558,0.00048110902,0.00032408466,0.10745886,0.25194672,0.011218059,0.002799161,0.61170065],"study_design_scores_gemma":[0.000082386054,0.0007568044,0.005267079,0.00002755837,0.00014466321,0.0015322812,0.00011950665,0.84165287,0.13319474,0.011449336,0.0056704115,0.000102398204],"about_ca_topic_score_codex":0.00092924066,"about_ca_topic_score_gemma":0.0015293208,"teacher_disagreement_score":0.001472116,"about_ca_system_score_codex":0.0004390851,"about_ca_system_score_gemma":0.00072782807,"threshold_uncertainty_score":0.00778538},"labels":[],"label_agreement":null},{"id":"W2997722046","doi":"","title":"Sports Field Localization using Memory Networks","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Field (mathematics); Analytics; Data science; Artificial intelligence","score_opus":0.0053501856968487735,"score_gpt":0.24696866767634768,"score_spread":0.2416184819794989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997722046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09522878,0.0009063888,0.88845646,0.00026256722,0.000108611,0.00008136094,0.0009176613,0.0049495855,0.009088582],"genre_scores_gemma":[0.79452413,0.0006878711,0.18757425,0.00013261952,0.00015446702,0.0001223258,0.0019680276,0.00015687468,0.014679391],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998074,0.000023905584,0.000008131782,0.0000748145,0.000037804777,0.00004802254],"domain_scores_gemma":[0.99972814,0.000069065914,0.00004827246,0.000049564653,0.000082671955,0.000022243896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020219496,0.00085753336,0.0003990466,0.0015834845,0.00046377763,0.0009820879,0.0011227888,0.00050032086,0.0030061544],"category_scores_gemma":[0.0009302177,0.00031007736,0.00027816248,0.0012751707,0.0002455106,0.0012980736,0.00084270496,0.00041237648,0.0012614526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006813837,0.00015879083,0.003937245,0.00011261085,0.00010794848,0.00023927409,0.000119667675,0.1386847,0.033920415,0.005205174,0.00908958,0.8077432],"study_design_scores_gemma":[0.000016602848,0.000060514805,0.0014784767,0.0000138523,0.000031300464,0.000080672056,0.0000741006,0.9758641,0.013418271,0.0053117606,0.0036346235,0.000015734468],"about_ca_topic_score_codex":0.014439559,"about_ca_topic_score_gemma":0.021021184,"teacher_disagreement_score":0.014439559,"about_ca_system_score_codex":0.0006485219,"about_ca_system_score_gemma":0.00053734065,"threshold_uncertainty_score":0.028711021},"labels":[],"label_agreement":null},{"id":"W2997748230","doi":"","title":"Challenges with Machine Learning for Microwave Breast Tumor detection","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Breast tumor; Microwave; Microwave imaging; Artificial intelligence; Breast tissue; Modality (human–computer interaction); Breast cancer; Computer science; Breast MRI; Machine learning; Mammography; Medical physics; Medicine; Telecommunications; Internal medicine; Cancer","score_opus":0.005721709867167127,"score_gpt":0.21165273679889987,"score_spread":0.20593102693173274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997748230","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016987141,0.052527152,0.86974084,0.047613442,0.00121418,0.00017657246,0.0005825922,0.0013506208,0.009807429],"genre_scores_gemma":[0.34397104,0.036591105,0.5989221,0.004416633,0.0047120927,0.00052618823,0.0012275205,0.0003047266,0.0093286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932326,0.0034406164,0.0003898229,0.000962779,0.0018042554,0.0001700103],"domain_scores_gemma":[0.9626644,0.030615317,0.0007521948,0.0019567953,0.0036848972,0.00032627457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011765988,0.001060611,0.0018209551,0.0017769227,0.00085135055,0.003951883,0.0028619352,0.0034108267,0.0027190244],"category_scores_gemma":[0.029883169,0.0006564542,0.00096127193,0.0019214445,0.0019041372,0.005198215,0.0021202255,0.0047424915,0.002704944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018414397,0.00032628456,0.004688344,0.001268292,0.00029889375,0.0001939963,0.00032939026,0.14058411,0.0025146715,0.06893674,0.026384667,0.75429046],"study_design_scores_gemma":[0.000029448987,0.00013968418,0.001761273,0.00029407875,0.00002897954,0.00021150199,0.00037815975,0.75944996,0.0024547612,0.20364381,0.031521324,0.00008693295],"about_ca_topic_score_codex":0.002737973,"about_ca_topic_score_gemma":0.0019653984,"teacher_disagreement_score":0.011765988,"about_ca_system_score_codex":0.0013109179,"about_ca_system_score_gemma":0.0012956477,"threshold_uncertainty_score":0.062225223},"labels":[],"label_agreement":null},{"id":"W2998042392","doi":"","title":"A Comparative Study Between Apparent Diffusion Imaging and Correlated Diffusion Imaging for Prostate Cancer","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Prostate cancer; Medicine; Magnetic resonance imaging; Grading (engineering); Effective diffusion coefficient; Histopathology; Diffusion MRI; Cancer; Cancer detection; Radiology; Modality (human–computer interaction); Prostate; Diffusion-Weighted Magnetic Resonance Imaging; Diffusion imaging; Nuclear medicine; Pathology; Artificial intelligence; Internal medicine; Computer science","score_opus":0.018034618549463824,"score_gpt":0.3478507290520844,"score_spread":0.3298161105026206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998042392","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9418676,0.039261866,0.013654139,0.00048807226,0.0001936758,0.00024212177,0.00076409546,0.00013697844,0.003391486],"genre_scores_gemma":[0.9869735,0.005263913,0.0063765217,0.00009491303,0.000167717,0.000051032046,0.0006427404,0.000042830747,0.00038690353],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979067,0.0008804449,0.00014588714,0.00038115468,0.0005950325,0.00009088277],"domain_scores_gemma":[0.9909978,0.005721493,0.00076601916,0.0006034011,0.0015487374,0.00036251673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052968627,0.00062831223,0.00059745833,0.002036675,0.00030241645,0.00097517157,0.0004554444,0.0006456803,0.0017121125],"category_scores_gemma":[0.019675976,0.00019001577,0.00074327143,0.001118206,0.0005375458,0.0014941372,0.00054390886,0.00039947993,0.00029310863],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017180292,0.0012606228,0.43532833,0.0026169128,0.003017648,0.0011696451,0.0009978416,0.01299964,0.023830712,0.002114566,0.003500017,0.4959838],"study_design_scores_gemma":[0.00057045853,0.014001042,0.85165316,0.0004748454,0.0040572803,0.0059888824,0.0011180849,0.09391731,0.014718936,0.0025299464,0.010677784,0.00029227632],"about_ca_topic_score_codex":0.0027961456,"about_ca_topic_score_gemma":0.0033341807,"teacher_disagreement_score":0.0052968627,"about_ca_system_score_codex":0.0007264806,"about_ca_system_score_gemma":0.00053696404,"threshold_uncertainty_score":0.028012812},"labels":[],"label_agreement":null},{"id":"W2998159825","doi":"","title":"Deep Learning Inference Frameworks for ARM CPU","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Inference; Server; Usability; Enhanced Data Rates for GSM Evolution; Central processing unit; Adaptation (eye); Edge device; Deep learning; Artificial intelligence; Edge computing; Machine learning; Human–computer interaction; Cloud computing; Operating system","score_opus":0.008470528831988292,"score_gpt":0.2939385152586868,"score_spread":0.2854679864266985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998159825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019280977,0.00060795626,0.9712971,0.0003378622,0.000107778455,0.000062773506,0.0006241802,0.019630628,0.0054036514],"genre_scores_gemma":[0.12643754,0.0011814542,0.8427385,0.0006940892,0.00015834213,0.0005797339,0.004020363,0.0034185357,0.020771386],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920624,0.000106957035,0.00006417205,0.00018701464,0.00032318378,0.000112420596],"domain_scores_gemma":[0.9991246,0.00023354644,0.000055530843,0.00022808913,0.00031278862,0.00004549296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010269813,0.0013056371,0.0007252708,0.0007661482,0.00044980244,0.0016659851,0.0034899123,0.0012987215,0.01989189],"category_scores_gemma":[0.0043990603,0.0008118469,0.0012256771,0.00085620733,0.00054998323,0.0018279717,0.0018408798,0.003090356,0.00831042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048829446,0.00016687157,0.0014024642,0.0004445314,0.00017088308,0.00020205868,0.00014666126,0.3089022,0.008558768,0.14461917,0.089758456,0.44513968],"study_design_scores_gemma":[0.00003164625,0.000024460789,0.00020661451,0.000047509104,0.000021806374,0.000061806975,0.000013783965,0.9329907,0.005285063,0.03620307,0.025090076,0.000023450624],"about_ca_topic_score_codex":0.012598454,"about_ca_topic_score_gemma":0.015231309,"teacher_disagreement_score":0.01989189,"about_ca_system_score_codex":0.001583051,"about_ca_system_score_gemma":0.0018479431,"threshold_uncertainty_score":0.06654501},"labels":[],"label_agreement":null},{"id":"W2998171852","doi":"","title":"Identifying Sea Ice Ridging in SAR Imagery using various Machine Learning Models","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sea ice; Ridge; Geology; Remote sensing; Climatology; Oceanography; Artificial intelligence; Computer science; Paleontology","score_opus":0.01681326089846145,"score_gpt":0.2504236639719005,"score_spread":0.23361040307343908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998171852","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91933215,0.0011846651,0.075211205,0.00039620962,0.00008256399,0.00006982966,0.00048137747,0.00069277914,0.0025492155],"genre_scores_gemma":[0.97946584,0.00022717445,0.019088868,0.000051853203,0.000020969897,0.000015355196,0.0005137625,0.000010258658,0.0006058962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999734,0.00007876916,0.000021374659,0.00007575258,0.000040041192,0.000050128783],"domain_scores_gemma":[0.99910307,0.00054500386,0.00009646114,0.00006640358,0.00015184631,0.000037282865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014531964,0.00088160066,0.0003974315,0.0012154973,0.00029232918,0.0010599429,0.00042589434,0.00048204794,0.0005890454],"category_scores_gemma":[0.002219038,0.00014212281,0.0005056599,0.0005408698,0.00028895197,0.00088366703,0.00035134415,0.0005235688,0.00023779846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028995812,0.0004675875,0.1594575,0.00011210515,0.0002530471,0.00015507836,0.00013993579,0.5265088,0.0052501955,0.00062246615,0.0015816629,0.30516165],"study_design_scores_gemma":[0.000004445083,0.00006694669,0.01259382,0.000011608024,0.000022979088,0.0000285475,0.00008549655,0.9852116,0.0013719032,0.00035925722,0.00023345643,0.000009921788],"about_ca_topic_score_codex":0.015557356,"about_ca_topic_score_gemma":0.019455796,"teacher_disagreement_score":0.015557356,"about_ca_system_score_codex":0.0005727043,"about_ca_system_score_gemma":0.0005002174,"threshold_uncertainty_score":0.030933619},"labels":[],"label_agreement":null},{"id":"W2998323295","doi":"","title":"Investigating the Impact of Inclusion in Face Recognition Training Data","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Scrutiny; Facial recognition system; Computer science; Leverage (statistics); Identification (biology); Audit; Scale (ratio); Inclusion (mineral); Artificial intelligence; Data science; Psychology; Pattern recognition (psychology); Political science; Social psychology; Law","score_opus":0.04836902372862347,"score_gpt":0.33388939078759694,"score_spread":0.2855203670589735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998323295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9684073,0.0023698015,0.014611973,0.00196194,0.00067895884,0.0001919596,0.0022937544,0.00061167835,0.008872774],"genre_scores_gemma":[0.97978216,0.00051081926,0.011685439,0.0005967548,0.00014213823,0.00008875785,0.004254019,0.00009061484,0.002849295],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9881231,0.005159568,0.00062939594,0.002196192,0.0031067117,0.0007851115],"domain_scores_gemma":[0.9534042,0.031919885,0.002339039,0.008652592,0.0030098197,0.00067450845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009893896,0.00093420036,0.00059143576,0.00087277626,0.0014256436,0.0017342139,0.0010282936,0.0012580515,0.0027552957],"category_scores_gemma":[0.049499206,0.0003453564,0.000679689,0.00079355744,0.001431355,0.0042049508,0.0023337211,0.0017801498,0.0015791024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0048020226,0.0032413993,0.448292,0.0015690572,0.0008216522,0.0011433532,0.0017881496,0.043877475,0.032888778,0.0058889836,0.036150623,0.4195365],"study_design_scores_gemma":[0.00023646564,0.0055487137,0.45236096,0.0006843431,0.0007953363,0.0048136166,0.0066239233,0.33210376,0.121873945,0.010797769,0.06386611,0.00029512742],"about_ca_topic_score_codex":0.007584348,"about_ca_topic_score_gemma":0.011315338,"teacher_disagreement_score":0.009893896,"about_ca_system_score_codex":0.00068633596,"about_ca_system_score_gemma":0.0009092158,"threshold_uncertainty_score":0.052324533},"labels":[],"label_agreement":null},{"id":"W2998437255","doi":"","title":"Understanding BatchNorm in Ternary Training","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ternary operation; Artificial neural network; Binary number; Computer science; Function (biology); Training (meteorology); Artificial intelligence; Mathematics; Arithmetic; Physics","score_opus":0.04719880229903626,"score_gpt":0.2811962168183445,"score_spread":0.23399741451930825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998437255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0211733,0.00072852976,0.97122496,0.0010890716,0.00013465206,0.00004536948,0.0000980307,0.00057932263,0.0049267597],"genre_scores_gemma":[0.564279,0.0014576856,0.4208747,0.0013408482,0.00040657964,0.00031357456,0.00044847152,0.0005626027,0.010316543],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986331,0.00048377967,0.00008572312,0.00027891988,0.0003955716,0.00012292768],"domain_scores_gemma":[0.9956175,0.002725703,0.00032996503,0.0006415325,0.0005530888,0.00013226163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032187416,0.0007715005,0.0006875251,0.00047501884,0.00059102016,0.0019242832,0.0016801418,0.0014124856,0.0036281436],"category_scores_gemma":[0.014892583,0.0005060119,0.00047678984,0.0006006695,0.0024077408,0.004497274,0.0020496363,0.002928161,0.0007320058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005375075,0.0001299842,0.002887641,0.00030930227,0.000063844935,0.0003443396,0.00063127093,0.2915656,0.014610214,0.38545847,0.007675481,0.29578626],"study_design_scores_gemma":[0.000018904122,0.000060716346,0.00041554603,0.0000425803,0.000013329854,0.00009338664,0.00003483984,0.84548074,0.008255226,0.14217012,0.003395769,0.000018756758],"about_ca_topic_score_codex":0.00373676,"about_ca_topic_score_gemma":0.003699021,"teacher_disagreement_score":0.00373676,"about_ca_system_score_codex":0.0012256912,"about_ca_system_score_gemma":0.0011968133,"threshold_uncertainty_score":0.01702255},"labels":[],"label_agreement":null},{"id":"W2998692308","doi":"","title":"STeW: Real-time Video Facial Emotion Classification via a Compact Sliding Temporal Windowed Deep Neural Network","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Facial expression; Deep learning; Artificial neural network; Task (project management); Speech recognition; Pattern recognition (psychology); Engineering","score_opus":0.020518992289991725,"score_gpt":0.31005014550760684,"score_spread":0.28953115321761513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998692308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32126257,0.0015551266,0.6636448,0.00049374,0.00046226435,0.00018126027,0.0010990915,0.006252308,0.0050488487],"genre_scores_gemma":[0.86697227,0.0005709969,0.118804485,0.00025097938,0.00006807934,0.00016431525,0.0026469007,0.00012010147,0.01040185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984646,0.000018639414,0.000006195095,0.000050266317,0.000044521603,0.000033793072],"domain_scores_gemma":[0.9998752,0.000026298501,0.000013230857,0.000022379569,0.00004764851,0.0000152128305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039200496,0.0007800299,0.0004697478,0.00034669077,0.0001869046,0.00037061353,0.0009809157,0.00048917934,0.0023028266],"category_scores_gemma":[0.0007836355,0.00023303075,0.00036814925,0.00025107665,0.00020606321,0.0008067087,0.00081112166,0.0007942928,0.0005809314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086919055,0.00041407882,0.0043784315,0.00011994453,0.00019064463,0.0001952561,0.0001112332,0.12862357,0.06397234,0.0019638154,0.015183607,0.78397787],"study_design_scores_gemma":[0.0000118634425,0.000093499824,0.001221646,0.00000542312,0.000016881413,0.000035664478,0.000016608916,0.9899823,0.0073332675,0.0005225062,0.0007515512,0.000008712852],"about_ca_topic_score_codex":0.0075134533,"about_ca_topic_score_gemma":0.012584077,"teacher_disagreement_score":0.0075134533,"about_ca_system_score_codex":0.0005002567,"about_ca_system_score_gemma":0.0004815244,"threshold_uncertainty_score":0.014939487},"labels":[],"label_agreement":null},{"id":"W2998711095","doi":"","title":"Automated Image Classification for Post-Earthquake Reconnaissance Images","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Metadata; Computer science; Convolutional neural network; Ground truth; Damages; Artificial intelligence; Field (mathematics); Data mining; World Wide Web","score_opus":0.014269557597644063,"score_gpt":0.28931614619116247,"score_spread":0.2750465885935184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998711095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36498067,0.0020475911,0.5729306,0.0006597978,0.00030417417,0.0007540411,0.007352954,0.041995212,0.008974982],"genre_scores_gemma":[0.5757616,0.00064008223,0.4005319,0.00023217377,0.00011907515,0.00024543778,0.014789486,0.0005884838,0.0070918417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923277,0.00006997202,0.000052871117,0.00024483548,0.000248846,0.00015074945],"domain_scores_gemma":[0.9988532,0.00016657737,0.00015559047,0.00027956543,0.0004974486,0.00004749379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005912244,0.00076774583,0.00057324907,0.004887961,0.0004538554,0.0010950749,0.0010993812,0.00086771837,0.0022489524],"category_scores_gemma":[0.0014296257,0.00028866864,0.000618066,0.0018535587,0.0003163238,0.0012006979,0.0006394193,0.00057511666,0.0022610743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003576704,0.0003546503,0.01237424,0.0001804677,0.000062892796,0.00030461536,0.00019358273,0.011069239,0.08222129,0.00080163626,0.016988717,0.87509096],"study_design_scores_gemma":[0.0000463505,0.0002834929,0.06076304,0.00008575832,0.000094158895,0.000647852,0.00068942143,0.71822625,0.18827322,0.003664089,0.027138868,0.00008755148],"about_ca_topic_score_codex":0.008141353,"about_ca_topic_score_gemma":0.014036657,"teacher_disagreement_score":0.008141353,"about_ca_system_score_codex":0.0007797286,"about_ca_system_score_gemma":0.00070806296,"threshold_uncertainty_score":0.016187906},"labels":[],"label_agreement":null},{"id":"W3090870145","doi":"10.15353/jcvis.v6i1.3539","title":"Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities","year":2021,"lang":"en","type":"preprint","venue":"Journal of Computational Vision and Imaging Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Leverage (statistics); Computer science; Oracle; Deep learning; Artificial intelligence; Artificial neural network; Trustworthiness; Computational trust; Deep neural networks; Set (abstract data type); Metric (unit); Data science; Computer security; Political science; Reputation; Business","score_opus":0.008017419130933056,"score_gpt":0.292259089003264,"score_spread":0.28424166987233096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090870145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079990335,0.0007513948,0.91314906,0.0025844204,0.00005347054,0.000060931823,0.00015516071,0.00017204396,0.0030832433],"genre_scores_gemma":[0.96442235,0.00040417627,0.033456877,0.00014347733,0.000059885533,0.00006697683,0.00008751309,0.00006900879,0.0012898111],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937168,0.0031811579,0.00033899015,0.0010236987,0.0012529959,0.00048638185],"domain_scores_gemma":[0.9503368,0.0341982,0.0064197113,0.0036522625,0.0038602701,0.0015326397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009458561,0.0008696155,0.001183136,0.0014353469,0.0009821684,0.0036199703,0.0015845288,0.0018604632,0.002534428],"category_scores_gemma":[0.07750756,0.00090319896,0.00096343487,0.00097000884,0.006081905,0.0121093495,0.0032075348,0.004509018,0.00031835688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006050545,0.00008841956,0.007985642,0.0002627386,0.00023380516,0.00044501823,0.0014664596,0.29475406,0.0037253692,0.64118856,0.0028696295,0.046375282],"study_design_scores_gemma":[0.000012064906,0.000045786255,0.00090497686,0.000039724287,0.000025653993,0.00007360441,0.00012112988,0.7136319,0.0010357049,0.2834048,0.0006662392,0.00003833243],"about_ca_topic_score_codex":0.005517013,"about_ca_topic_score_gemma":0.0026741999,"teacher_disagreement_score":0.009458561,"about_ca_system_score_codex":0.003839796,"about_ca_system_score_gemma":0.0015090198,"threshold_uncertainty_score":0.050022244},"labels":[],"label_agreement":null},{"id":"W3091206037","doi":"10.15353/jcvis.v6i1.3534","title":"Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling","year":2021,"lang":"en","type":"preprint","venue":"Journal of Computational Vision and Imaging Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Large margin nearest neighbor; Metric (unit); Margin (machine learning); MNIST database; k-nearest neighbors algorithm; Computer science; Artificial intelligence; Subspace topology; Nearest neighbor graph; Pattern recognition (psychology); Nearest neighbor search; Acceleration; Sampling (signal processing); Machine learning; Scalability; Deep learning; Physics; Filter (signal processing); Engineering; Computer vision","score_opus":0.07010865272972298,"score_gpt":0.35527759555818356,"score_spread":0.28516894282846056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091206037","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016899448,0.00025761637,0.9795554,0.00014575315,0.00007389045,0.00011929778,0.00014986929,0.001974512,0.0008242065],"genre_scores_gemma":[0.22774306,0.00016581669,0.76723087,0.00021797293,0.000102241014,0.00030901772,0.002103599,0.00035650187,0.0017708344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99718285,0.00093760696,0.00019487391,0.0005468565,0.0009452302,0.00019261973],"domain_scores_gemma":[0.9956995,0.0013847542,0.00029538674,0.0012343286,0.0011403888,0.0002455903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030108157,0.0014821958,0.0022348578,0.0015761775,0.00075528974,0.001230809,0.0026978527,0.0014035037,0.0031070432],"category_scores_gemma":[0.012101361,0.00054879923,0.0015208501,0.0020783956,0.00077607,0.003017971,0.002607965,0.0023740726,0.002276864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041262782,0.00064968487,0.004560108,0.00015983275,0.00015555996,0.00013537974,0.00021412862,0.260164,0.012065522,0.013333488,0.014152075,0.6939976],"study_design_scores_gemma":[0.000013252952,0.000040568153,0.00024594791,0.0000033436027,0.0000047404983,0.000034922872,0.000015313291,0.99358785,0.0012853731,0.0041718003,0.0005902221,0.0000065860695],"about_ca_topic_score_codex":0.0059769405,"about_ca_topic_score_gemma":0.00830936,"teacher_disagreement_score":0.0059769405,"about_ca_system_score_codex":0.0010853921,"about_ca_system_score_gemma":0.0015823066,"threshold_uncertainty_score":0.015922844},"labels":[],"label_agreement":null},{"id":"W3115695223","doi":"","title":"Semi-supervised Anomaly Detection using AutoEncoders","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Artificial intelligence; Computer science; Segmentation; Pattern recognition (psychology); Anomaly (physics); Process (computing); Residual; Task (project management); Encoder; Computer vision; Engineering; Algorithm","score_opus":0.008693236621937717,"score_gpt":0.2616070934454122,"score_spread":0.2529138568234745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115695223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11075964,0.00059227354,0.88216496,0.00022512177,0.00009607513,0.00007884646,0.00023261414,0.004671427,0.0011789904],"genre_scores_gemma":[0.8229438,0.0002977611,0.17105922,0.00019297052,0.000104228326,0.00011451555,0.0013162716,0.00014979365,0.0038214612],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992085,0.00016012257,0.000054233973,0.00028075548,0.00018516938,0.00011117693],"domain_scores_gemma":[0.9971967,0.0014464554,0.00024735893,0.0003351575,0.00069417676,0.000080076876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011921613,0.0012567969,0.0011645949,0.0007943185,0.00033235893,0.0007142894,0.0014632466,0.0010342386,0.00090902654],"category_scores_gemma":[0.002704652,0.0006328986,0.00079057686,0.0005013391,0.00056464324,0.0009478597,0.000892431,0.0015734911,0.0005443354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037475946,0.00041439067,0.0040045157,0.00015521537,0.00026242214,0.00021488631,0.00012190689,0.432612,0.025161508,0.0015962089,0.0037856689,0.5312965],"study_design_scores_gemma":[0.000003817075,0.000022249022,0.0004392444,0.000004240448,0.000006801519,0.000024366678,0.0000047870635,0.9964629,0.0023372944,0.00053241797,0.00015739026,0.000004476338],"about_ca_topic_score_codex":0.0070372666,"about_ca_topic_score_gemma":0.010063103,"teacher_disagreement_score":0.0070372666,"about_ca_system_score_codex":0.00066070876,"about_ca_system_score_gemma":0.0010382066,"threshold_uncertainty_score":0.013992608},"labels":[],"label_agreement":null},{"id":"W3121213459","doi":"10.15353/jcvis.v6i1.3563","title":"Evaluation of Solving Methods for the Fundamental Matrix Computation","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Christie (Canada); University of Waterloo","funders":"","keywords":"Projector; Fundamental matrix (linear differential equation); Computer science; Computation; Matrix (chemical analysis); Calibration; Key (lock); Essential matrix; Computer vision; Nonlinear system; Artificial intelligence; Algorithm; Mathematics; State-transition matrix; Symmetric matrix","score_opus":0.0795183532844082,"score_gpt":0.45011968175881206,"score_spread":0.37060132847440386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121213459","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14433435,0.0023712665,0.8303644,0.001241503,0.00043204048,0.00042116322,0.0006075231,0.0030940066,0.017133705],"genre_scores_gemma":[0.35485667,0.00070559944,0.6378097,0.00021531535,0.000110099194,0.00028781057,0.0011482735,0.0008588467,0.00400762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965412,0.0015146064,0.00020492946,0.00039192775,0.0011507001,0.00019664824],"domain_scores_gemma":[0.95426375,0.035809588,0.0010461671,0.002167383,0.006134535,0.00057858287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006565814,0.0016428587,0.0010746543,0.0014120021,0.0009855228,0.0017674809,0.0017539999,0.001972269,0.006834511],"category_scores_gemma":[0.048749093,0.00033814769,0.000601772,0.0010928769,0.0011081821,0.002285398,0.0018698596,0.001444771,0.0011952394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013834452,0.0005913066,0.0032955313,0.000757893,0.00016589015,0.00010219103,0.0002753979,0.5800435,0.0066559124,0.03018901,0.0075214677,0.36901844],"study_design_scores_gemma":[0.000053791202,0.00011917733,0.00030208033,0.000022421884,0.000008950404,0.000029721059,0.000055279244,0.99368566,0.0025747472,0.002408182,0.0007291205,0.000010871402],"about_ca_topic_score_codex":0.008443798,"about_ca_topic_score_gemma":0.0077752117,"teacher_disagreement_score":0.008443798,"about_ca_system_score_codex":0.0014809974,"about_ca_system_score_gemma":0.0027688649,"threshold_uncertainty_score":0.03472376},"labels":[],"label_agreement":null},{"id":"W3121479196","doi":"10.15353/jcvis.v6i1.3542","title":"A Tool for Annotating Homographies from Hockey Broadcast Video","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Mitacs","keywords":"Computer science; Frame (networking); Computer vision; Ground truth; Artificial intelligence; Overhead (engineering); Point (geometry); Ice hockey; Reference frame; Computer graphics (images); Telecommunications; Mathematics","score_opus":0.0076050378153431865,"score_gpt":0.2551083343398905,"score_spread":0.2475032965245473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121479196","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016942937,0.0009024673,0.805424,0.00016028737,0.00026724386,0.0006737855,0.06585087,0.102611855,0.0071665877],"genre_scores_gemma":[0.077470474,0.00086241984,0.7451191,0.0001523761,0.00011802763,0.0010485263,0.1635339,0.006609405,0.005085866],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986551,0.00015134805,0.00009544966,0.0005948169,0.0003775309,0.00012570409],"domain_scores_gemma":[0.9982022,0.00040193103,0.00024364406,0.0006519687,0.00040853204,0.00009165731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009927361,0.0020439685,0.0010474005,0.0062242234,0.0008520282,0.0014743522,0.0013480695,0.001541388,0.012281081],"category_scores_gemma":[0.0042743855,0.00074492174,0.0010085175,0.003922136,0.0006204584,0.0022706718,0.00260297,0.0015865142,0.009610909],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006608402,0.00028167156,0.0038782256,0.0019946333,0.00029973435,0.00093938573,0.0013038411,0.009095275,0.04961826,0.007826232,0.18362692,0.74047494],"study_design_scores_gemma":[0.00023208602,0.0004915117,0.03748203,0.0010548419,0.00032911086,0.0031459166,0.003670667,0.26263297,0.09844245,0.03670551,0.55542,0.00039294673],"about_ca_topic_score_codex":0.008220844,"about_ca_topic_score_gemma":0.016421791,"teacher_disagreement_score":0.012281081,"about_ca_system_score_codex":0.00055527483,"about_ca_system_score_gemma":0.0009878818,"threshold_uncertainty_score":0.04108435},"labels":[],"label_agreement":null},{"id":"W3121592807","doi":"10.15353/jcvis.v6i1.3550","title":"Seeing the Forest from the Trees: A Novel Deep Learning-Driven Aggregate Embedding for Group-Level Analysis of Public Health Data","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Cardiovascular Health and Risk Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"Institute of Population and Public Health; Institute of Nutrition, Metabolism and Diabetes; Canadian Institutes of Health Research; Health Canada","keywords":"Compass; Aggregate (composite); Embedding; Computer science; Aggregate data; Data science; Artificial intelligence; Deep learning; Architecture; Machine learning; Psychology; Mathematics education; Geography; Statistics; Cartography; Mathematics","score_opus":0.07437437679974387,"score_gpt":0.36139581937602633,"score_spread":0.2870214425762825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121592807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036993593,0.0002753582,0.96047664,0.00043415668,0.000051486255,0.000058231235,0.00036805414,0.0007034563,0.000638943],"genre_scores_gemma":[0.628201,0.00033422143,0.36381352,0.0005370892,0.00014151892,0.00024821694,0.002225951,0.00020098,0.0042974697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993969,0.00022223752,0.000028954582,0.00015524759,0.00010825962,0.00008837022],"domain_scores_gemma":[0.99887484,0.0006372197,0.00009109606,0.00014731147,0.00015942742,0.00009012684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014908123,0.00078093994,0.00071192137,0.00077750674,0.00037343215,0.000873848,0.0016129139,0.0010692114,0.001560112],"category_scores_gemma":[0.003826248,0.00038752987,0.000794454,0.00092259713,0.00060906314,0.0013870972,0.0022758024,0.002336237,0.00048543685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030881655,0.00032802328,0.012064899,0.00014580174,0.00022018417,0.00016085675,0.00046669392,0.5030823,0.0062366985,0.017850941,0.008755525,0.45037922],"study_design_scores_gemma":[0.000004573275,0.000027145144,0.00038931269,0.0000058902447,0.0000054822017,0.0000083976975,0.000017110206,0.99354786,0.00033794635,0.0052079554,0.00044403976,0.0000042727897],"about_ca_topic_score_codex":0.008327513,"about_ca_topic_score_gemma":0.014280329,"teacher_disagreement_score":0.008327513,"about_ca_system_score_codex":0.00082262896,"about_ca_system_score_gemma":0.0009178144,"threshold_uncertainty_score":0.01655811},"labels":[],"label_agreement":null},{"id":"W3121753179","doi":"10.15353/jcvis.v6i1.3552","title":"Time-Series Causality with Missing Data","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"ATS Automation Tooling Systems (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Missing data; Series (stratigraphy); Causality (physics); Multivariate statistics; Time series; Pairwise comparison; Granger causality; Statistics; Causal model; Computer science; Data mining; Econometrics; Mathematics; Algorithm","score_opus":0.026076913886251413,"score_gpt":0.29610166233717805,"score_spread":0.2700247484509266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121753179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019171312,0.00045639407,0.97883636,0.0003873438,0.00007749717,0.000034408913,0.00022993538,0.0004005768,0.000406055],"genre_scores_gemma":[0.7152809,0.0017889047,0.27751172,0.00048857054,0.00030862063,0.0002439477,0.0020569598,0.00021587066,0.0021046188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99600035,0.0014294337,0.0004343261,0.0011932031,0.0007061335,0.00023652351],"domain_scores_gemma":[0.9727365,0.01927284,0.0029296277,0.003253384,0.0014998949,0.00030775007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012542304,0.0012425783,0.0018485201,0.0024324975,0.00083245576,0.0019263093,0.002270865,0.0017855646,0.0032057741],"category_scores_gemma":[0.047108766,0.0009907765,0.0017655258,0.0036196331,0.001770673,0.0042437566,0.0020466908,0.0035969506,0.000577059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005674618,0.00025262265,0.03837196,0.001074082,0.000805419,0.0014920858,0.00086081703,0.48797897,0.0038376234,0.13878863,0.0046851668,0.32128522],"study_design_scores_gemma":[0.00003225354,0.00006250735,0.00395199,0.00009068714,0.000096357704,0.00026547047,0.00012982791,0.8484786,0.001983313,0.14199463,0.002864418,0.00004994697],"about_ca_topic_score_codex":0.0036861296,"about_ca_topic_score_gemma":0.003176899,"teacher_disagreement_score":0.012542304,"about_ca_system_score_codex":0.0008859775,"about_ca_system_score_gemma":0.0018236161,"threshold_uncertainty_score":0.06633085},"labels":[],"label_agreement":null},{"id":"W3121838925","doi":"10.15353/jcvis.v6i1.3562","title":"Constraints for Time-Multiplexed Structured Light with a Hand-held Camera","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Christie (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reprojection error; Homography; Computer vision; Pinhole camera model; Artificial intelligence; Camera matrix; Projector; Computer science; Structured light; Camera auto-calibration; Camera resectioning; Pinhole camera; Pixel; Frame (networking); Point (geometry); Computer graphics (images); Mathematics; Image (mathematics); Projective test; Projective space; Optics","score_opus":0.012473033172349188,"score_gpt":0.2696571182916737,"score_spread":0.2571840851193245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121838925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0882551,0.00035658572,0.9054287,0.00015766079,0.000033863154,0.00011937088,0.00041034017,0.00041925765,0.0048189852],"genre_scores_gemma":[0.7605115,0.00028449376,0.23467067,0.000102497295,0.000027922315,0.00029394758,0.00041204251,0.00011963131,0.0035772652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99853516,0.00025537738,0.000065101696,0.0002880242,0.0007479284,0.00010846977],"domain_scores_gemma":[0.99756455,0.0012296714,0.00047942856,0.00026495502,0.0003610323,0.000100305726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090869796,0.0007944002,0.0005552383,0.00038939092,0.00041550113,0.0010244225,0.00093308656,0.00064180867,0.0040797787],"category_scores_gemma":[0.0050739674,0.0004495483,0.00029651518,0.00036827038,0.0006562662,0.0014888992,0.0011864799,0.00082263915,0.00067650824],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004845406,0.00018847492,0.0018558175,0.0005840844,0.000058143345,0.00069858006,0.00038567904,0.51741004,0.329168,0.033482842,0.0025749286,0.11310881],"study_design_scores_gemma":[0.000059705955,0.0004140417,0.002842968,0.00005947194,0.000017303244,0.00042239483,0.00014151329,0.8823415,0.09781058,0.010512835,0.005318267,0.000059333932],"about_ca_topic_score_codex":0.0036673674,"about_ca_topic_score_gemma":0.005160994,"teacher_disagreement_score":0.0040797787,"about_ca_system_score_codex":0.0006303209,"about_ca_system_score_gemma":0.0012002444,"threshold_uncertainty_score":0.013648272},"labels":[],"label_agreement":null},{"id":"W3121880205","doi":"10.15353/jcvis.v6i1.3535","title":"BenderNet and RingerNet: Highly Efficient Line Segmentation Deep Neural Network Architectures for Ice Rink Localization","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Artificial neural network; Ice hockey; Market segmentation; Deep neural networks; Line (geometry); Architecture; Computer vision; Geography","score_opus":0.008292615145022532,"score_gpt":0.2582707516403677,"score_spread":0.2499781364953452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121880205","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06538318,0.0020902075,0.91519344,0.000912973,0.00043282297,0.00013785492,0.001303303,0.008144717,0.0064014723],"genre_scores_gemma":[0.50554186,0.0013075412,0.45303997,0.0007873083,0.00019971123,0.00031364473,0.007097767,0.0007659139,0.030946225],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981815,0.00003351242,0.000008772872,0.00006464723,0.00003987391,0.00003495608],"domain_scores_gemma":[0.9997671,0.00007123309,0.00002481313,0.000038299826,0.000077103046,0.00002139723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000439396,0.0012181927,0.00055488094,0.000653656,0.00036081322,0.0007621508,0.0012534442,0.0011226011,0.002414548],"category_scores_gemma":[0.0013543804,0.0005095119,0.00055281154,0.00074218,0.00036078054,0.0012121779,0.0007966829,0.0016718777,0.0011872794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005236545,0.00017864633,0.0016305397,0.00016217654,0.00022030779,0.00015979246,0.0001362806,0.3785957,0.021477079,0.0071544345,0.034369167,0.5553922],"study_design_scores_gemma":[0.000017826198,0.000051920426,0.00033792015,0.00000972,0.000017897844,0.000020390391,0.000018347702,0.99012685,0.0048929034,0.0023806149,0.0021156871,0.000009866198],"about_ca_topic_score_codex":0.010053745,"about_ca_topic_score_gemma":0.018269729,"teacher_disagreement_score":0.010053745,"about_ca_system_score_codex":0.0007721886,"about_ca_system_score_gemma":0.00086411193,"threshold_uncertainty_score":0.019990444},"labels":[],"label_agreement":null},{"id":"W3122179705","doi":"10.15353/jcvis.v6i1.3549","title":"Improved Deep Convolutional Neural Network with Age Augmentation for Facial Emotion Recognition in Social Companion Robotics","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Face recognition and analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley","funders":"","keywords":"Overfitting; Convolutional neural network; Artificial intelligence; Robotics; Classifier (UML); Computer science; Emotion recognition; Deep learning; Facial expression; Machine learning; Robot; Artificial neural network","score_opus":0.0199856891256367,"score_gpt":0.2756868129623147,"score_spread":0.255701123836678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122179705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4762226,0.0033211873,0.50271547,0.0009948502,0.0006705122,0.00017069899,0.0012648639,0.0034587635,0.0111811105],"genre_scores_gemma":[0.93290144,0.00050755683,0.056969196,0.00034118447,0.00006832702,0.00009365274,0.0010837083,0.00006365608,0.007971382],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998092,0.000039618688,0.000008393369,0.00005537395,0.00004268745,0.000044811597],"domain_scores_gemma":[0.99985766,0.000037275247,0.000015948272,0.000020090085,0.000056988527,0.000012025502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003980068,0.000628891,0.0003327825,0.00023402939,0.0001689763,0.0002559676,0.00053266576,0.0003918419,0.0018902996],"category_scores_gemma":[0.00096116797,0.00015651125,0.00034558133,0.00016244665,0.000145831,0.00049545587,0.00051399105,0.0006406814,0.00064496166],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007302829,0.00058111124,0.012627198,0.00015046744,0.0001489387,0.0003031646,0.00023846731,0.09289009,0.050465126,0.0020558375,0.016123502,0.8236859],"study_design_scores_gemma":[0.000012609597,0.00018028983,0.0066035236,0.000026981379,0.0000554064,0.00012552767,0.00007325589,0.9757357,0.0123743,0.0015233747,0.003268085,0.000020940182],"about_ca_topic_score_codex":0.005307664,"about_ca_topic_score_gemma":0.0093568275,"teacher_disagreement_score":0.005307664,"about_ca_system_score_codex":0.0004267915,"about_ca_system_score_gemma":0.00034743192,"threshold_uncertainty_score":0.010553539},"labels":[],"label_agreement":null},{"id":"W3122447812","doi":"10.15353/jcvis.v6i1.3533","title":"2D Positional Embedding-based Transformer for Scene Text Recognition","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"ATS Automation Tooling Systems (Canada); University of Waterloo","funders":"Ontario Centres of Excellence","keywords":"Computer science; Transformer; Artificial intelligence; Encoder; Embedding; Leverage (statistics); Architecture; Pattern recognition (psychology); Text recognition; Computer vision; Image (mathematics); Engineering; Geography","score_opus":0.015430532486448805,"score_gpt":0.2989395937905886,"score_spread":0.2835090613041398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122447812","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0586727,0.0007939282,0.92301166,0.00017996632,0.00025776427,0.00013754326,0.0012175934,0.009513135,0.006215719],"genre_scores_gemma":[0.6820721,0.0008447901,0.29445475,0.00030692873,0.00013690097,0.00015786881,0.00587086,0.00044409075,0.015711706],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980634,0.000020899633,0.0000118643375,0.00006654551,0.00006916228,0.000025239213],"domain_scores_gemma":[0.9997869,0.00003587522,0.00002571188,0.000059397298,0.00007186697,0.000020168329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013839468,0.00077555585,0.0005135679,0.0007999307,0.00017016583,0.0004497765,0.0008753334,0.00035063626,0.0042184736],"category_scores_gemma":[0.00076878397,0.00017776564,0.00044877225,0.00084844814,0.0002729508,0.0015317459,0.0006425336,0.000646989,0.002820025],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038054583,0.00012516655,0.0009291716,0.00019823734,0.000040695664,0.00024222645,0.000066306275,0.03073575,0.08959122,0.005838114,0.012562062,0.85929054],"study_design_scores_gemma":[0.000034435598,0.00028221155,0.0013562532,0.00001761867,0.000046492645,0.0004899084,0.00008796877,0.9016439,0.08007947,0.0058100354,0.010117692,0.000034108492],"about_ca_topic_score_codex":0.0022090487,"about_ca_topic_score_gemma":0.0043360367,"teacher_disagreement_score":0.0042184736,"about_ca_system_score_codex":0.0002633307,"about_ca_system_score_gemma":0.0004304188,"threshold_uncertainty_score":0.0141121745},"labels":[],"label_agreement":null},{"id":"W3122915047","doi":"10.15353/jcvis.v6i1.3557","title":"Real-time Quantitative Visual Inspection using Extended Reality","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Headset; Computer science; Computer vision; Polygon mesh; Artificial intelligence; Virtual reality; Augmented reality; Process (computing); Pixel; Workflow; Computer graphics (images); Triangulation","score_opus":0.011622709787955673,"score_gpt":0.30722162923166707,"score_spread":0.2955989194437114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122915047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020253519,0.00016933579,0.9766975,0.00006573514,0.00003202881,0.000037284615,0.00007648024,0.0015919121,0.0010761046],"genre_scores_gemma":[0.48640886,0.0004420498,0.51004755,0.00015957837,0.000048275124,0.000105122075,0.00031096794,0.00016363303,0.0023139452],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909437,0.00018409797,0.0000407574,0.00021047718,0.0003969552,0.00007341708],"domain_scores_gemma":[0.99900717,0.0002674634,0.00017502412,0.0002321556,0.0002670166,0.000051126375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060861226,0.0007176224,0.0005408234,0.0009204895,0.00016133276,0.0010949794,0.00095385296,0.0008310264,0.001996842],"category_scores_gemma":[0.0020994414,0.00043912468,0.0005782455,0.0004504624,0.00055135606,0.0013099512,0.0014319427,0.0007104689,0.00064648513],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061809225,0.0001902039,0.0033742639,0.00052922237,0.000118123025,0.00051484903,0.00055932935,0.11999692,0.2803284,0.006181162,0.002994678,0.58459485],"study_design_scores_gemma":[0.000035940408,0.00045848585,0.0058930833,0.00005526528,0.000044549848,0.0008264446,0.00014821722,0.91292065,0.06869057,0.003412584,0.0074082655,0.00010600864],"about_ca_topic_score_codex":0.0010306402,"about_ca_topic_score_gemma":0.0012424641,"teacher_disagreement_score":0.001996842,"about_ca_system_score_codex":0.00032700298,"about_ca_system_score_gemma":0.00030241872,"threshold_uncertainty_score":0.006680131},"labels":[],"label_agreement":null},{"id":"W3123505098","doi":"10.15353/jcvis.v6i1.3546","title":"COVID-19 Detection from Chest X-Ray Images Using Deep Convolutional Neural Networks with Weights Imprinting Approach","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo; National Research Council Canada; University of Ottawa","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Convolutional neural network; Deep learning; Artificial intelligence; Computer science; Sensitivity (control systems); Pattern recognition (psychology); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Artificial neural network; Virology; Engineering; Medicine","score_opus":0.020031737229309003,"score_gpt":0.30241814209254475,"score_spread":0.28238640486323574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123505098","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26489478,0.0027887162,0.720415,0.0010460659,0.00033655876,0.00017540749,0.00088615116,0.0042263735,0.0052309856],"genre_scores_gemma":[0.889867,0.0009864499,0.10039659,0.0005368646,0.00017781991,0.000080551785,0.001830656,0.00010310868,0.00602102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999592,0.000052187766,0.000025308465,0.0001379009,0.00010157815,0.00009109133],"domain_scores_gemma":[0.9996208,0.00010216214,0.00006404555,0.000052058436,0.00012425907,0.000036674766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000658404,0.0013649396,0.0007207041,0.0011355577,0.00033154056,0.00072664995,0.0010822136,0.0008959097,0.00085389183],"category_scores_gemma":[0.0016241001,0.0004046635,0.0007287866,0.00046321604,0.00028609097,0.0010583234,0.0010093956,0.001288574,0.0005004656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006482874,0.00048709693,0.030834435,0.00017811244,0.00036384971,0.00062405836,0.00013007455,0.1936171,0.049381822,0.001914009,0.010010749,0.7118104],"study_design_scores_gemma":[0.000009820084,0.00008064868,0.002671096,0.000013353594,0.000052457784,0.00018670209,0.000020817091,0.9809383,0.013517317,0.0014158828,0.0010772608,0.000016294489],"about_ca_topic_score_codex":0.0070590544,"about_ca_topic_score_gemma":0.01120852,"teacher_disagreement_score":0.0070590544,"about_ca_system_score_codex":0.0005984155,"about_ca_system_score_gemma":0.0007681107,"threshold_uncertainty_score":0.01403594},"labels":[],"label_agreement":null},{"id":"W3123507393","doi":"10.15353/jcvis.v6i1.3555","title":"Temporally Consistent Edge-Informed Video Super-Resolution (Edge-VSR)","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Superresolution; Frame (networking); Computer vision; Convolutional neural network; Optical flow; Resolution (logic); Low resolution; Pattern recognition (psychology); Image (mathematics); High resolution; Remote sensing; Telecommunications; Geography","score_opus":0.014505128671240897,"score_gpt":0.30297975714935815,"score_spread":0.2884746284781173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123507393","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006344989,0.00037086222,0.9917603,0.00009685014,0.000033638065,0.000028753675,0.0001169665,0.00045562658,0.0007921115],"genre_scores_gemma":[0.14061828,0.0008221819,0.85328335,0.0002618085,0.0000657515,0.000054275646,0.00055069645,0.00018185432,0.004161826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995346,0.000097323056,0.000021148715,0.00012564452,0.00017890193,0.00004246749],"domain_scores_gemma":[0.99928623,0.00023614998,0.00012916549,0.0001853633,0.00012745096,0.000035629317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008043373,0.00085656357,0.0009294912,0.0006513663,0.00020781156,0.0005940186,0.0014699285,0.0010031234,0.0018084568],"category_scores_gemma":[0.0018669883,0.00046140375,0.0008086074,0.00075240294,0.0005381246,0.0013788765,0.0012486689,0.0017382446,0.0007386777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002700213,0.000117532916,0.000989676,0.00031743274,0.00023453591,0.0005099913,0.00011944552,0.22218774,0.18376566,0.021657996,0.0072769914,0.56255305],"study_design_scores_gemma":[0.000008893349,0.00007813004,0.0006062477,0.000020046251,0.000027690721,0.00044551582,0.000013556316,0.9297714,0.058649607,0.0061657242,0.0041866507,0.000026470441],"about_ca_topic_score_codex":0.0011608768,"about_ca_topic_score_gemma":0.0027739133,"teacher_disagreement_score":0.0018084568,"about_ca_system_score_codex":0.0004159392,"about_ca_system_score_gemma":0.0005193363,"threshold_uncertainty_score":0.006049931},"labels":[],"label_agreement":null},{"id":"W3123806511","doi":"10.15353/jcvis.v6i1.3556","title":"Locally Adaptive Thresholding for Single-Shot Structured Light Patterns","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Christie (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thresholding; Artificial intelligence; Computer science; Balanced histogram thresholding; Projector; Image (mathematics); Computer vision; Sensitivity (control systems); Pattern recognition (psychology); Image processing; Engineering","score_opus":0.021682651122192236,"score_gpt":0.286774309870284,"score_spread":0.26509165874809176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123806511","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041681856,0.00028365955,0.95616364,0.00006761437,0.000028688843,0.00003801049,0.000027370203,0.00046823424,0.0012410162],"genre_scores_gemma":[0.44448134,0.00042526968,0.5518644,0.00010655891,0.00003243605,0.00009481397,0.00013232628,0.00030136041,0.0025615743],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946934,0.000080544916,0.000032933167,0.000118286036,0.00026270875,0.00003620286],"domain_scores_gemma":[0.9990646,0.0003606388,0.0001477826,0.00016127949,0.00022126478,0.000044400826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004646234,0.0003215524,0.00044849407,0.00059485994,0.00019621388,0.00071355904,0.0006630392,0.00045914896,0.001216841],"category_scores_gemma":[0.002043595,0.00025477482,0.00027415287,0.0005112628,0.0004995041,0.0006414509,0.0005912024,0.00059066794,0.00042631678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022178293,0.00006534687,0.00075077626,0.0003389781,0.000041107767,0.00016200119,0.00019748842,0.025357442,0.6829537,0.005469875,0.0011052791,0.28333622],"study_design_scores_gemma":[0.000026445947,0.00019930043,0.0030298447,0.00005164402,0.000042739208,0.00070312497,0.00010397694,0.60549015,0.3776063,0.008243846,0.0044606393,0.00004197872],"about_ca_topic_score_codex":0.00032233354,"about_ca_topic_score_gemma":0.0006906896,"teacher_disagreement_score":0.001216841,"about_ca_system_score_codex":0.00030839478,"about_ca_system_score_gemma":0.00029303096,"threshold_uncertainty_score":0.004070699},"labels":[],"label_agreement":null},{"id":"W3123980720","doi":"10.15353/jcvis.v6i1.3554","title":"PlasticNet: Deep Learning for Automatic Microplastic Recognition via FT-IR Spectroscopy","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Discriminative model; Pattern recognition (psychology); Deep learning; Convolutional neural network; Computer science; Convolution (computer science); Spectroscopy; Machine learning; Artificial neural network; Speech recognition; Physics","score_opus":0.005962170591127816,"score_gpt":0.23660786602477016,"score_spread":0.23064569543364233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123980720","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15516576,0.0033501114,0.78038317,0.0009579117,0.00061263103,0.00034707595,0.004799101,0.043046918,0.011337416],"genre_scores_gemma":[0.5874844,0.0014343722,0.36688304,0.0011637749,0.0001345595,0.00047850472,0.014276176,0.0007926494,0.027352545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978465,0.000021601409,0.000010106221,0.000081902996,0.00005849348,0.000043234577],"domain_scores_gemma":[0.9998097,0.00005751068,0.000023327315,0.000032811186,0.00005764675,0.00001892129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000566205,0.0015845621,0.0005805588,0.0007183378,0.0003318457,0.00069204654,0.0015976796,0.0010485292,0.0055790152],"category_scores_gemma":[0.00093412737,0.0004805562,0.00058843824,0.0006836304,0.0003744465,0.0012689208,0.0009747317,0.0010957589,0.002353281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071329606,0.0005502256,0.0031113003,0.00054237334,0.00025881405,0.00036396558,0.00007545985,0.16409065,0.063581616,0.004800396,0.044259995,0.71765184],"study_design_scores_gemma":[0.000024120382,0.00013846118,0.00064403086,0.000021411088,0.00002266462,0.0000834744,0.00002009844,0.968527,0.023762025,0.0017616112,0.0049775583,0.000017507566],"about_ca_topic_score_codex":0.0064812005,"about_ca_topic_score_gemma":0.0103698205,"teacher_disagreement_score":0.0064812005,"about_ca_system_score_codex":0.00083286763,"about_ca_system_score_gemma":0.0010658675,"threshold_uncertainty_score":0.018663704},"labels":[],"label_agreement":null},{"id":"W3124175555","doi":"10.15353/jcvis.v6i1.3538","title":"Where Should We Begin? A Low-Level Exploration of Weight Initialization Impact on Quantized Behaviour of Deep Neural Networks","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Quantization (signal processing); Convolutional neural network; Computer science; Inference; Artificial intelligence; Deep neural networks; Artificial neural network; Deep learning; Algorithm","score_opus":0.04322401672907961,"score_gpt":0.3389066658185412,"score_spread":0.29568264908946157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124175555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43915898,0.002738608,0.54230654,0.0030799087,0.00023967386,0.00007547677,0.00028108887,0.0018995508,0.010220103],"genre_scores_gemma":[0.952173,0.0005619145,0.045390658,0.00022119687,0.000014420504,0.000025940502,0.00009041061,0.00032192303,0.0012004145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954754,0.00010738939,0.000020988367,0.00011901532,0.00013796358,0.00006712506],"domain_scores_gemma":[0.9980235,0.0010084207,0.00020713375,0.0003506719,0.00031873546,0.00009156315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011169129,0.00053443684,0.00039289048,0.00030282582,0.00040638572,0.0015021318,0.0008176535,0.00072362245,0.0025088154],"category_scores_gemma":[0.011604618,0.0004061625,0.0002707258,0.00024952166,0.001086734,0.003147424,0.0009310259,0.0017820895,0.00057568535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010470429,0.0002781697,0.013111354,0.0007113793,0.00015419467,0.0006573698,0.0011030885,0.51959807,0.18677706,0.07666476,0.0061889426,0.19370864],"study_design_scores_gemma":[0.000039664254,0.00031677537,0.0047346097,0.00023466378,0.000051828632,0.0001663828,0.00044253594,0.84267026,0.08218855,0.06315569,0.005919786,0.000079207595],"about_ca_topic_score_codex":0.0019432312,"about_ca_topic_score_gemma":0.0026445766,"teacher_disagreement_score":0.0025088154,"about_ca_system_score_codex":0.0007382013,"about_ca_system_score_gemma":0.00048299244,"threshold_uncertainty_score":0.008392811},"labels":[],"label_agreement":null},{"id":"W3124178031","doi":"10.15353/jcvis.v6i1.3536","title":"Pal-GAN: Palette-conditioned Generative Adversarial Networks","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Palette (painting); Artificial intelligence; Adversarial system; Generative grammar; Image translation; Intersection (aeronautics); Image (mathematics); Variety (cybernetics); Segmentation; Discriminative model; Class (philosophy); Machine learning","score_opus":0.006727752864173138,"score_gpt":0.2368859368188145,"score_spread":0.23015818395464138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124178031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057575405,0.00028737792,0.9899093,0.00021594031,0.000054182,0.000039290637,0.00013423148,0.0009854181,0.0026167154],"genre_scores_gemma":[0.6287704,0.0007626213,0.35397395,0.001058177,0.00016761692,0.00034584844,0.0012876219,0.00078887615,0.01284485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947757,0.00019888888,0.000016547738,0.00014354175,0.00011461949,0.00004885029],"domain_scores_gemma":[0.9988085,0.00080728595,0.00008002935,0.00014946306,0.00010616382,0.000048529146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011509889,0.001308301,0.00094082905,0.00043967523,0.00024575368,0.0007222074,0.0015618942,0.0011776828,0.0046126796],"category_scores_gemma":[0.0033547156,0.00054799457,0.0008272215,0.00041537714,0.0010902715,0.00094431033,0.001687484,0.0027188396,0.0011273994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010917187,0.000038557588,0.0004174924,0.00006631638,0.000050330273,0.00008982734,0.000040210645,0.9222648,0.004566769,0.019262962,0.0041878787,0.048905704],"study_design_scores_gemma":[0.000004671966,0.000011466776,0.000034331817,0.0000058231235,0.0000035353703,0.000020723588,0.000001840139,0.9921669,0.0006745102,0.0065166997,0.0005553568,0.0000040906643],"about_ca_topic_score_codex":0.0017219712,"about_ca_topic_score_gemma":0.0026474092,"teacher_disagreement_score":0.0046126796,"about_ca_system_score_codex":0.00069308904,"about_ca_system_score_gemma":0.00054378936,"threshold_uncertainty_score":0.015430987},"labels":[],"label_agreement":null},{"id":"W3124624839","doi":"10.15353/jcvis.v6i1.3561","title":"Rectification Based Single-Shot Structured Light for Accurate and Dense 3D Reconstruction","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Christie (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Computer science; Quadrilateral; Projector; Rectification; Shot (pellet); 3D reconstruction; Structured light; Single shot; Pixel; Point cloud; One shot; Computer graphics (images); Optics; Physics","score_opus":0.017748171247847687,"score_gpt":0.2836573336853735,"score_spread":0.2659091624375258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124624839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009247181,0.000041600088,0.98961335,0.00003180947,0.000010396234,0.000028266442,0.00006212284,0.0004764909,0.000488682],"genre_scores_gemma":[0.11427927,0.00012744992,0.8838832,0.000047094458,0.000015126589,0.00007412694,0.00039285017,0.0001622364,0.0010187165],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944013,0.000071851566,0.000021832055,0.00009247135,0.00033802848,0.000035661014],"domain_scores_gemma":[0.999253,0.00017643251,0.00010157895,0.0002353238,0.00020417072,0.000029490342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046938728,0.0007175215,0.0006280967,0.0009058793,0.00021293282,0.00092202466,0.0009309261,0.00071867945,0.0023030974],"category_scores_gemma":[0.001589984,0.0006200715,0.0007271875,0.00092334114,0.00047514177,0.0010559276,0.0010911885,0.0011656743,0.001414839],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002473185,0.00011907748,0.0017597746,0.0004208145,0.00008867538,0.0002440349,0.0004673149,0.17039962,0.37562686,0.013767803,0.0039002416,0.43295848],"study_design_scores_gemma":[0.00001600291,0.00006527975,0.00073197595,0.00001780306,0.000012783162,0.00040560638,0.000061425395,0.9183695,0.07372238,0.0033791398,0.0031820778,0.000036105648],"about_ca_topic_score_codex":0.0011543725,"about_ca_topic_score_gemma":0.0021805398,"teacher_disagreement_score":0.0023030974,"about_ca_system_score_codex":0.00036387096,"about_ca_system_score_gemma":0.00070706964,"threshold_uncertainty_score":0.0077046156},"labels":[],"label_agreement":null},{"id":"W3124984453","doi":"10.15353/jcvis.v6i1.3558","title":"Methods of Evaluating 3D Perception Systems for Unstructured Autonomous Logistics","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perception; Computer science; Unstructured data; Human–computer interaction; Artificial intelligence; Data mining; Big data","score_opus":0.046520028491680875,"score_gpt":0.3664535542490187,"score_spread":0.31993352575733786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124984453","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014606163,0.0002031465,0.9818205,0.00006178,0.000027502958,0.0003082438,0.00020231398,0.00068390043,0.0020863959],"genre_scores_gemma":[0.22630972,0.00022137348,0.77107775,0.00005157719,0.000027092989,0.0005676394,0.0005443215,0.00021574978,0.0009847322],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9929892,0.0022961327,0.00046742245,0.00088979094,0.0031442717,0.00021321911],"domain_scores_gemma":[0.98669666,0.0062404214,0.0016922134,0.0013066942,0.003770191,0.00029376688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005469062,0.0015203854,0.0010097312,0.0050903186,0.0007542472,0.0037820642,0.0018025485,0.0013563347,0.003419188],"category_scores_gemma":[0.02256927,0.00060357124,0.0010707766,0.002335068,0.0012511499,0.0020157066,0.002368535,0.0009351919,0.00091362465],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048577422,0.00026858717,0.010692191,0.0005832232,0.00023344539,0.000106664236,0.000826943,0.22268677,0.027830074,0.016777532,0.0024393774,0.71706945],"study_design_scores_gemma":[0.0000442257,0.0002861193,0.0053621205,0.00007687449,0.000033838016,0.00012896245,0.000447803,0.9677912,0.014158246,0.008206849,0.0033621422,0.00010155778],"about_ca_topic_score_codex":0.0062283897,"about_ca_topic_score_gemma":0.005768147,"teacher_disagreement_score":0.0062283897,"about_ca_system_score_codex":0.0017071221,"about_ca_system_score_gemma":0.0014718244,"threshold_uncertainty_score":0.028923571},"labels":[],"label_agreement":null},{"id":"W3125004740","doi":"10.15353/jcvis.v6i1.3551","title":"Why Can’t Neural Networks Forecast Pandemics Better","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Pandemic; Computer science; Artificial neural network; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Time series; Econometrics; Machine learning; Economics; Infectious disease (medical specialty)","score_opus":0.018971177220830762,"score_gpt":0.30941631562937877,"score_spread":0.290445138408548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125004740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2591728,0.02753401,0.47689208,0.18944672,0.008375158,0.00021573655,0.004625913,0.0036445898,0.030093018],"genre_scores_gemma":[0.89989233,0.00516081,0.07557871,0.009928576,0.002001032,0.00008976516,0.0016583189,0.00030703415,0.005383473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861836,0.00061984535,0.000108791275,0.00030520814,0.0002209269,0.00012678248],"domain_scores_gemma":[0.9891557,0.0069366917,0.00076018414,0.00076168886,0.0020101434,0.00037558522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006538642,0.000975488,0.001032627,0.001250724,0.000490203,0.0027617444,0.0011922392,0.0028369615,0.0031518945],"category_scores_gemma":[0.03338894,0.00044954894,0.00068315555,0.0008105885,0.00094682217,0.0053730574,0.0007288665,0.0030451366,0.0018972614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075208367,0.00021486213,0.055971354,0.00055181846,0.00088382565,0.00019728679,0.0003551865,0.5586702,0.0021356612,0.020810919,0.056703135,0.30275357],"study_design_scores_gemma":[0.00005493973,0.00013353863,0.005688825,0.00031617502,0.00008897499,0.000071939896,0.00027604745,0.9237655,0.0014215772,0.060312238,0.00778976,0.00008063404],"about_ca_topic_score_codex":0.015628375,"about_ca_topic_score_gemma":0.014009771,"teacher_disagreement_score":0.015628375,"about_ca_system_score_codex":0.0010464755,"about_ca_system_score_gemma":0.000819399,"threshold_uncertainty_score":0.034580052},"labels":[],"label_agreement":null},{"id":"W3125162820","doi":"10.15353/jcvis.v6i1.3540","title":"Comparison of Foveated Downsampling Techniques in Image Recognition","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Upsampling; Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Image (mathematics); Variable (mathematics); Deep learning; Object (grammar); Mathematics","score_opus":0.019868269116079387,"score_gpt":0.33031646995598446,"score_spread":0.3104482008399051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125162820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39353484,0.032145835,0.5401006,0.0012654074,0.0011305434,0.00041894347,0.0010942502,0.010397972,0.019911667],"genre_scores_gemma":[0.63049734,0.007008221,0.35154116,0.00055132335,0.0002566272,0.00017350311,0.0023419391,0.00061282486,0.0070170923],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99829835,0.00032578496,0.00013276862,0.00051036704,0.00053493766,0.00019771808],"domain_scores_gemma":[0.99788946,0.000946441,0.00012432014,0.00045792153,0.00047395038,0.000107964865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023344406,0.0012654641,0.0011216108,0.0016537919,0.00038351896,0.0013840055,0.0018763329,0.0014469712,0.003221308],"category_scores_gemma":[0.0063932682,0.00047115912,0.0009707254,0.0011489925,0.00061364233,0.0026076783,0.0014979229,0.0011277276,0.00099752],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019193396,0.00044577493,0.0021316663,0.0005415967,0.00060618564,0.00015740722,0.00013588206,0.09443135,0.024042413,0.0048008803,0.008134436,0.862653],"study_design_scores_gemma":[0.00015065551,0.0014235299,0.0058892057,0.00012095828,0.00027210396,0.00057996745,0.00015522435,0.91937596,0.057259697,0.0062103993,0.008450009,0.00011229332],"about_ca_topic_score_codex":0.0062279706,"about_ca_topic_score_gemma":0.0068046725,"teacher_disagreement_score":0.0062279706,"about_ca_system_score_codex":0.0009756999,"about_ca_system_score_gemma":0.00074602437,"threshold_uncertainty_score":0.012383461},"labels":[],"label_agreement":null},{"id":"W3125451872","doi":"10.15353/jcvis.v6i1.3548","title":"Where do Clinical Language Models Break Down? A Critical Behavioural Exploration of the ClinicalBERT Deep Transformer Model","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Computer science; Transformer; Inference; Artificial intelligence; Language model; Leverage (statistics); Realm; Deep learning; Encoder; Language understanding; Machine learning; Data science; Natural language processing; Engineering","score_opus":0.17653239938079687,"score_gpt":0.48687585443965586,"score_spread":0.31034345505885896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125451872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4018157,0.004939382,0.5268959,0.035287306,0.0006103718,0.0003624835,0.0034688986,0.003886194,0.022733733],"genre_scores_gemma":[0.91736484,0.0009417251,0.07141751,0.0027866669,0.0001080049,0.00016567347,0.002280644,0.00051611924,0.004418711],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99597627,0.0022374697,0.0001420032,0.0008717852,0.0004761257,0.00029636626],"domain_scores_gemma":[0.9869435,0.009624465,0.0005266718,0.0014505459,0.00093837205,0.0005164592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071251015,0.0010825869,0.0009163181,0.0006932502,0.00078428915,0.003031757,0.0016093182,0.001870488,0.004535473],"category_scores_gemma":[0.041630484,0.0007104276,0.00070621376,0.00054011913,0.0020737387,0.00650134,0.0028528436,0.0061167777,0.0016382555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002131675,0.00077017513,0.05527911,0.000982892,0.00051765086,0.0012808,0.003896421,0.30297047,0.022025185,0.13833544,0.04558307,0.42622715],"study_design_scores_gemma":[0.0000682085,0.00031583325,0.0026574049,0.00013644568,0.000051468116,0.00033449716,0.00060165784,0.85679525,0.005110386,0.12834838,0.005509609,0.00007091182],"about_ca_topic_score_codex":0.00816421,"about_ca_topic_score_gemma":0.01311792,"teacher_disagreement_score":0.00816421,"about_ca_system_score_codex":0.0018188738,"about_ca_system_score_gemma":0.0025255375,"threshold_uncertainty_score":0.03768158},"labels":[],"label_agreement":null},{"id":"W3125452826","doi":"10.15353/jcvis.v6i1.3544","title":"A Hybrid Landmark and Contour-Matching Image Registration Model","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Landmark; Artificial intelligence; Computer vision; Image registration; Computer science; Matching (statistics); Thin plate spline; Similarity (geometry); Image (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.01069668156503452,"score_gpt":0.29288845605806013,"score_spread":0.2821917744930256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125452826","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020587863,0.00009425006,0.99662673,0.00006384191,0.00001993928,0.000026334714,0.000029966704,0.00033236065,0.000747779],"genre_scores_gemma":[0.23185301,0.0005292203,0.75838965,0.00017866,0.00010159828,0.0003510194,0.00035462505,0.0003476615,0.007894525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901104,0.0001757861,0.000056183486,0.0002604422,0.00044868139,0.000047765137],"domain_scores_gemma":[0.99951684,0.00013312387,0.00007674977,0.00013305063,0.00011605338,0.000024148707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011019317,0.00054743857,0.0008020355,0.0011747266,0.00031860193,0.0011764097,0.002925235,0.0015438234,0.002509269],"category_scores_gemma":[0.0023271057,0.00059882255,0.0010969323,0.0016272815,0.0007997711,0.0019717126,0.0012713446,0.00094457495,0.0016041084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021459584,0.00008034063,0.0010813772,0.00024768803,0.00009542388,0.00030577864,0.0002191362,0.5397186,0.032778665,0.09198759,0.0041154847,0.32915527],"study_design_scores_gemma":[0.000011537396,0.00006718228,0.00018046731,0.000009885309,0.000020924723,0.00025433325,0.00000970607,0.9815284,0.0042656283,0.008806615,0.004822521,0.000022873626],"about_ca_topic_score_codex":0.0015888816,"about_ca_topic_score_gemma":0.0010809072,"teacher_disagreement_score":0.002925235,"about_ca_system_score_codex":0.00069038227,"about_ca_system_score_gemma":0.0008967252,"threshold_uncertainty_score":0.008394361},"labels":[],"label_agreement":null},{"id":"W3125624062","doi":"10.15353/jcvis.v6i1.3541","title":"Image Scale Estimation Using Surface Textures for Quantitative Visual Inspection","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Scale (ratio); Convolutional neural network; Texture (cosmology); Computer science; Pixel; Pattern recognition (psychology); Surface (topology); Computer vision; Dimension (graph theory); Artificial neural network; Image (mathematics); Mathematics; Geography; Cartography; Geometry","score_opus":0.008696595690346774,"score_gpt":0.30552709056600663,"score_spread":0.2968304948756599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125624062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05556712,0.0003193775,0.9416868,0.00006206978,0.00004044778,0.000040177474,0.00010479257,0.0009849484,0.0011943443],"genre_scores_gemma":[0.7246827,0.0004541271,0.27342546,0.00005638921,0.000060990642,0.000042060932,0.0002239995,0.00016822224,0.00088604126],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964535,0.00004552753,0.00001527489,0.000081608654,0.00017880832,0.00003352213],"domain_scores_gemma":[0.99924386,0.00022764498,0.00016274827,0.00013176438,0.00020729585,0.000026719312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044762553,0.0005566852,0.00039174609,0.0012114419,0.00013439362,0.00062490907,0.0003731471,0.00035094563,0.00095035497],"category_scores_gemma":[0.0021561987,0.00021763655,0.00029831062,0.00089077785,0.00037329886,0.00080518355,0.0004990306,0.0004886115,0.00037200892],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031567478,0.00009294957,0.00655908,0.00034292586,0.000064540196,0.00014465855,0.00016429301,0.052430842,0.41310436,0.003127509,0.0016715941,0.5219816],"study_design_scores_gemma":[0.000015283385,0.00016260913,0.017698226,0.000031093925,0.000044931297,0.0003751577,0.00010026144,0.86427647,0.110386536,0.0028761923,0.003976285,0.000056909845],"about_ca_topic_score_codex":0.0009947503,"about_ca_topic_score_gemma":0.0011528214,"teacher_disagreement_score":0.0012114419,"about_ca_system_score_codex":0.00024283314,"about_ca_system_score_gemma":0.00019948286,"threshold_uncertainty_score":0.0031793118},"labels":[],"label_agreement":null},{"id":"W3125751664","doi":"10.15353/jcvis.v6i1.3537","title":"Deep Residual Transform for Multi-scale Image Decomposition","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canada Research Chairs","keywords":"Computer science; Residual; Artificial intelligence; Leverage (statistics); Granularity; Transformation (genetics); Representation (politics); Pattern recognition (psychology); Hierarchy; Image (mathematics); Decomposition; Computer vision; Algorithm","score_opus":0.019688764249585215,"score_gpt":0.3453194604014362,"score_spread":0.325630696151851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125751664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009332314,0.00069131446,0.9867692,0.00021869787,0.000059064612,0.000031696127,0.00026711097,0.0013066228,0.0013240039],"genre_scores_gemma":[0.32956612,0.0019239417,0.65810454,0.00041206833,0.00015277663,0.00017640984,0.0028719157,0.00046132744,0.006330927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997607,0.000032297405,0.000013303102,0.000063021405,0.00009710788,0.000033567394],"domain_scores_gemma":[0.999728,0.000083759005,0.00004024577,0.000063276726,0.00006250221,0.000022236214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042258826,0.0008850688,0.00068985246,0.0008094677,0.0001781707,0.00072162715,0.0009804729,0.00081380963,0.002966372],"category_scores_gemma":[0.0013453119,0.00027127357,0.00097091636,0.0010792166,0.0004888447,0.0011636679,0.0009843924,0.0019732972,0.0016179234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023502272,0.00017639628,0.001154712,0.00036466628,0.00015150462,0.00030861644,0.00013045888,0.31461215,0.074144736,0.033106823,0.015071697,0.56054324],"study_design_scores_gemma":[0.000009205785,0.00003913979,0.0003095523,0.000011870314,0.000016427746,0.000072225936,0.00001567107,0.9798916,0.0074467943,0.008548551,0.0036281943,0.0000107821725],"about_ca_topic_score_codex":0.0028911629,"about_ca_topic_score_gemma":0.004493559,"teacher_disagreement_score":0.002966372,"about_ca_system_score_codex":0.00053378846,"about_ca_system_score_gemma":0.0005935531,"threshold_uncertainty_score":0.009923518},"labels":[],"label_agreement":null},{"id":"W3125777994","doi":"10.15353/jcvis.v6i1.3547","title":"COVIDNet-CT: Detection of COVID-19 from Chest CT Images using a Tailored Deep Convolutional Neural Network Architecture","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Convolutional neural network; Leverage (statistics); Coronavirus disease 2019 (COVID-19); Artificial intelligence; Radiology; Medicine; Computer science; Computed tomography; Deep learning; Pattern recognition (psychology); Disease; Pathology; Infectious disease (medical specialty)","score_opus":0.021088514307811015,"score_gpt":0.3195234304092317,"score_spread":0.2984349161014207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125777994","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48880196,0.0045828368,0.4448636,0.004018122,0.00092075736,0.0009242249,0.0192225,0.024261236,0.012404782],"genre_scores_gemma":[0.7861579,0.000844026,0.1767649,0.0011848839,0.00016059152,0.00032486196,0.02629025,0.00031202126,0.007960539],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972254,0.000033605,0.000020444644,0.0001127002,0.000053796695,0.0000569245],"domain_scores_gemma":[0.9995302,0.00016271914,0.00005412389,0.000065510656,0.00012878,0.000058633726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006606288,0.0016614136,0.00054290507,0.0011307525,0.00037814424,0.00081483426,0.0017181622,0.001460308,0.0026983235],"category_scores_gemma":[0.0024930467,0.0004706911,0.00068633,0.0005704459,0.00036359337,0.0010104756,0.0011046886,0.0014557233,0.0010205256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013340584,0.0010430381,0.054304983,0.0006897621,0.0005556991,0.0013077022,0.00019548534,0.33102804,0.03421053,0.0043680333,0.08338645,0.4875762],"study_design_scores_gemma":[0.000037901904,0.00011688767,0.0032890546,0.000038951173,0.000032926917,0.00022674917,0.000025036867,0.9844893,0.00732834,0.0018125435,0.0025806841,0.000021658183],"about_ca_topic_score_codex":0.017053956,"about_ca_topic_score_gemma":0.028616851,"teacher_disagreement_score":0.017053956,"about_ca_system_score_codex":0.0012574022,"about_ca_system_score_gemma":0.0014961183,"threshold_uncertainty_score":0.03390938},"labels":[],"label_agreement":null},{"id":"W3126145728","doi":"10.15353/jcvis.v6i1.3553","title":"InnovFaceNet: Deep Face Recognition for Industrial Environments","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Facial recognition system; Computer science; Face (sociological concept); Enhanced Data Rates for GSM Evolution; Computer security; Artificial intelligence; Human–computer interaction; Pattern recognition (psychology)","score_opus":0.02784139377514414,"score_gpt":0.2711113300196239,"score_spread":0.24326993624447976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126145728","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02285942,0.0029286873,0.9264665,0.00080744433,0.0005221425,0.00021597082,0.0022660426,0.035799317,0.008134316],"genre_scores_gemma":[0.26955494,0.0023351058,0.6765526,0.0013906894,0.00022415786,0.00076795666,0.010774038,0.0015992705,0.036801215],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952245,0.000057025627,0.000016174741,0.00015359287,0.0001745392,0.00007619528],"domain_scores_gemma":[0.99983835,0.00003136466,0.00001274181,0.000043774355,0.000059009864,0.000014737659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007059126,0.0012272204,0.00078891066,0.0008904923,0.00047588785,0.00075785385,0.0018541074,0.0012536847,0.013030508],"category_scores_gemma":[0.0008783018,0.00051756616,0.00083583896,0.00064949546,0.00034766798,0.001439851,0.0014551377,0.001571612,0.0064811017],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021688521,0.00017394523,0.00085912633,0.00013551545,0.00011365181,0.00010591917,0.000046878333,0.02668085,0.016014824,0.0039104642,0.058118626,0.89362335],"study_design_scores_gemma":[0.00003970798,0.00016944647,0.0013995515,0.000040981613,0.000033651195,0.0002647965,0.00003730771,0.9275707,0.024291446,0.010004844,0.036105234,0.00004239047],"about_ca_topic_score_codex":0.008024836,"about_ca_topic_score_gemma":0.010749503,"teacher_disagreement_score":0.013030508,"about_ca_system_score_codex":0.0007165663,"about_ca_system_score_gemma":0.0009042434,"threshold_uncertainty_score":0.04359138},"labels":[],"label_agreement":null},{"id":"W3127551618","doi":"10.15353/jcvis.v6i1.3543","title":"Challenges of Deep Learning-based Text Detection in the Wild","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"ATS Automation Tooling Systems (Canada); University of Waterloo","funders":"","keywords":"Benchmark (surveying); Computer science; Artificial intelligence; Distortion (music); Deep learning; Perspective (graphical); Machine learning; Text detection; Function (biology); Pattern recognition (psychology); Image (mathematics)","score_opus":0.01399929453120768,"score_gpt":0.2751410523217244,"score_spread":0.26114175779051674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127551618","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18640986,0.022808779,0.7193404,0.016418068,0.0018348899,0.00051464234,0.011727229,0.023148738,0.017797425],"genre_scores_gemma":[0.5798803,0.0061617233,0.3780626,0.0024924001,0.00077539845,0.00036175005,0.020257395,0.00086840504,0.01114003],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9941856,0.0012636475,0.00054339546,0.001576051,0.0020427932,0.00038851687],"domain_scores_gemma":[0.99012774,0.0045102243,0.0008069379,0.0016364916,0.0026102301,0.0003082096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005122295,0.001743622,0.002212402,0.003150873,0.0010414093,0.003982675,0.0048617385,0.0033742094,0.0019395163],"category_scores_gemma":[0.019455051,0.0007347059,0.0007489492,0.002609136,0.0018709655,0.0093666315,0.0025922356,0.0028313084,0.003990866],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004041301,0.00037016356,0.005848932,0.0013139938,0.00016351878,0.00046927974,0.00026631932,0.06556796,0.019768728,0.007267703,0.06018364,0.8383756],"study_design_scores_gemma":[0.000046415862,0.00016894325,0.004846439,0.00024790197,0.00006981562,0.0006314529,0.00066155405,0.8874001,0.03795841,0.045233406,0.022656456,0.00007920205],"about_ca_topic_score_codex":0.008497889,"about_ca_topic_score_gemma":0.008215713,"teacher_disagreement_score":0.008497889,"about_ca_system_score_codex":0.0018800739,"about_ca_system_score_gemma":0.0012341782,"threshold_uncertainty_score":0.027089596},"labels":[],"label_agreement":null}]}