{"meta":{"query_hash":"7fc2721caaed","filters":{"venue":"Sensing and Instrumentation for Food Quality and Safety"},"cohort_total":7,"direct_labels_cover":0,"predictions_cover":7,"exported":7,"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/7fc2721caaed","api":"https://metacan.xera.ac/api/v1/cohort?venue=Sensing+and+Instrumentation+for+Food+Quality+and+Safety"},"results":[{"id":"W1971332480","doi":"10.1007/s11694-010-9104-2","title":"Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples","year":2010,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"University of Manitoba","keywords":"Hyperspectral imaging; Linear discriminant analysis; Principal component analysis; Quadratic classifier; Moisture; Remote sensing; Mathematics; Near-infrared spectroscopy; Environmental science; Pattern recognition (psychology); Geography; Artificial intelligence; Statistics; Computer science; Support vector machine; Physics; Meteorology; Optics","score_opus":0.057170545226089484,"score_gpt":0.3337119666151768,"score_spread":0.2765414213890873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971332480","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":[0.9915085,0.00010056701,0.007237301,0.000015028181,0.000006387386,0.000011569944,0.00028627654,0.000059680606,0.0007746713],"genre_scores_gemma":[0.98871,0.00011448318,0.009186367,0.000033470416,0.000005367016,0.000016328911,0.000663688,0.00001915636,0.0012511498],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999609,0.0000028458342,0.0000023892853,0.000017571518,0.0000099244335,0.0000063701514],"domain_scores_gemma":[0.99994385,0.000011705937,0.000010722526,0.000005235384,0.000020253012,0.000008246939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008332956,0.0002480841,0.00016645378,0.00036157196,0.00012244529,0.00025820255,0.000087222834,0.00018200043,0.0006724656],"category_scores_gemma":[0.00013291196,0.00011636078,0.00009576585,0.00019406025,0.00011251946,0.00023615765,0.00009935954,0.0001969064,0.00022803832],"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":[0.00026261163,0.000030477824,0.008004475,0.000023467672,0.000008207923,0.000018148841,0.000054153916,0.000096920594,0.98136246,0.000033767545,0.0000713912,0.010033898],"study_design_scores_gemma":[0.000021228963,0.0003364861,0.5443107,0.0000072313514,0.00007169205,0.00036675448,0.00033365728,0.008646385,0.44420406,0.00023675982,0.0014474827,0.000017551492],"about_ca_topic_score_codex":0.00071172195,"about_ca_topic_score_gemma":0.0017573542,"teacher_disagreement_score":0.00071172195,"about_ca_system_score_codex":0.000064748725,"about_ca_system_score_gemma":0.000039121176,"threshold_uncertainty_score":0.0022495985},"labels":[],"label_agreement":null},{"id":"W1990743281","doi":"10.1007/s11694-007-9022-0","title":"Near-infrared spectroscopy and imaging in food quality and safety","year":2007,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":167,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Hyperspectral imaging; Food safety; Quality (philosophy); Chemometrics; Computer science; Identification (biology); Software; Agriculture; Food quality; Traceability; Systems engineering; Agricultural engineering; Biochemical engineering; Biotechnology; Risk analysis (engineering); Business; Artificial intelligence; Engineering; Machine learning; Geography; Food science; Software engineering","score_opus":0.039466054438937755,"score_gpt":0.34378653279914423,"score_spread":0.3043204783602065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990743281","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.104425155,0.4326112,0.36456302,0.013935527,0.0024334912,0.000101448364,0.00020737271,0.00067135337,0.0810514],"genre_scores_gemma":[0.62915784,0.14145833,0.1572141,0.003686881,0.0031528214,0.00013398244,0.00014683185,0.00013303298,0.064916156],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99959415,0.00013323147,0.000016663807,0.000072901086,0.00015751427,0.000025505997],"domain_scores_gemma":[0.9995233,0.0002202461,0.000065524764,0.000037848862,0.00012330675,0.000029818384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076422316,0.00047587563,0.00046922578,0.00084008847,0.00040821178,0.0009931239,0.00069596234,0.0018155791,0.0028139467],"category_scores_gemma":[0.0007907611,0.00027259398,0.00026157085,0.00094806793,0.0014264508,0.0017490892,0.0005522454,0.0011727643,0.00092060835],"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":[0.0003181475,0.0003455277,0.002365731,0.0014012075,0.000057422378,0.0004730809,0.0003431587,0.0042664534,0.37896016,0.16417317,0.010862537,0.43643346],"study_design_scores_gemma":[0.000046547255,0.00078546477,0.008492562,0.00034797992,0.000095974705,0.0034291826,0.00079037907,0.040708523,0.4274612,0.21290453,0.30476347,0.00017425857],"about_ca_topic_score_codex":0.00051818124,"about_ca_topic_score_gemma":0.00051792443,"teacher_disagreement_score":0.0028139467,"about_ca_system_score_codex":0.0005105313,"about_ca_system_score_gemma":0.00038273825,"threshold_uncertainty_score":0.00941354},"labels":[],"label_agreement":null},{"id":"W2046944046","doi":"10.1007/s11694-007-9014-0","title":"Encapsulation of quantum dots and carbon nanotubes with polypyrrole in a syringe needle for automated molecularly imprinted solid phase pre-concentration of ochratoxin A in red wine analysis","year":2007,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Polypyrrole; Molecularly imprinted polymer; Materials science; Elution; Ethylene glycol dimethacrylate; Carbon nanotube; Solid phase extraction; Ochratoxin A; Chromatography; Ethylene glycol; Chemical engineering; Nanotechnology; Polymer; High-performance liquid chromatography; Polymerization; Chemistry; Composite material; Organic chemistry; Selectivity","score_opus":0.02631924912351914,"score_gpt":0.3547322500400084,"score_spread":0.3284130009164893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046944046","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":[0.9402319,0.00042472646,0.057084236,0.000096963384,0.00004548563,0.00009365089,0.00017244824,0.0004954898,0.0013550104],"genre_scores_gemma":[0.95787317,0.00022690212,0.03902153,0.00003532317,0.000009822144,0.00004792931,0.00013125438,0.00005099057,0.0026030065],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979,0.000024351584,0.000015150641,0.00007285504,0.00007686664,0.000020808484],"domain_scores_gemma":[0.99983215,0.000040828636,0.000048908652,0.000034040575,0.000028662691,0.000015491023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002636334,0.00020750129,0.00013900001,0.00013860162,0.00016177996,0.0002581905,0.00033305358,0.00035285414,0.0003997413],"category_scores_gemma":[0.00025970218,0.00026362058,0.00020366919,0.000107012085,0.0001882842,0.00034381685,0.00020317479,0.00027033608,0.00025138038],"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":[0.000013963024,0.0000058428564,0.00004418475,0.000007376299,0.0000014917103,0.000011287788,0.0000071305058,0.00009546619,0.9988023,0.00007092037,0.000012133819,0.0009278805],"study_design_scores_gemma":[0.0000020821292,0.000026159732,0.00024869016,5.8508334e-7,0.0000021945782,0.000019415602,0.0000015785532,0.0016988504,0.99778557,0.000010462071,0.00020236983,0.0000021125654],"about_ca_topic_score_codex":0.0005328684,"about_ca_topic_score_gemma":0.0012416105,"teacher_disagreement_score":0.0005328684,"about_ca_system_score_codex":0.0003681396,"about_ca_system_score_gemma":0.00021389,"threshold_uncertainty_score":0.0026710033},"labels":[],"label_agreement":null},{"id":"W2049634071","doi":"10.1007/s11694-010-9099-8","title":"Sprouting detection at early stages in individual CWAD and CWRS wheat kernels using SWIR spectroscopy","year":2010,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sprouting; Linear discriminant analysis; Germination; Amylase; Logistic regression; Mathematics; Chemistry; Food science; Biology; Botany; Statistics; Enzyme; Biochemistry","score_opus":0.05289556866494902,"score_gpt":0.3374625773336971,"score_spread":0.28456700866874807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049634071","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":[0.9965115,0.00013859886,0.0025856178,0.000006959102,0.0000038687313,0.00000627002,0.00013079097,0.000050396815,0.00056592503],"genre_scores_gemma":[0.9939715,0.00008357666,0.0040276945,0.000018860632,0.000002695812,0.000011544126,0.00028514568,0.000019534415,0.0015793568],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99994993,0.0000036387094,0.0000023968976,0.000019809402,0.000012720623,0.000011435002],"domain_scores_gemma":[0.9998623,0.000034976398,0.00002389698,0.0000093060935,0.00003888997,0.00003074426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103413964,0.00019422425,0.00015293027,0.00027655522,0.00017538147,0.00027001253,0.00013542804,0.00019620378,0.00055558584],"category_scores_gemma":[0.00012595502,0.00014927739,0.00015896525,0.00012748427,0.00012109823,0.00025605306,0.00013487221,0.0003393505,0.00021978776],"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":[0.00017630395,0.000015641823,0.005879924,0.000012681675,0.0000036587787,0.000012880867,0.000060810533,0.00004760631,0.99175256,0.000016750926,0.000020253436,0.0020009824],"study_design_scores_gemma":[0.000009127862,0.00032334382,0.44240984,0.000005063539,0.000040316594,0.00015509235,0.00027632504,0.0036246441,0.5523696,0.000064010266,0.00070736546,0.000015376885],"about_ca_topic_score_codex":0.0035040195,"about_ca_topic_score_gemma":0.007674146,"teacher_disagreement_score":0.0035040195,"about_ca_system_score_codex":0.00015903256,"about_ca_system_score_gemma":0.00009545059,"threshold_uncertainty_score":0.0069672465},"labels":[],"label_agreement":null},{"id":"W2053795316","doi":"10.1007/s11694-009-9087-z","title":"Using a Short Wavelength Infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual Canadian western wheat kernels","year":2009,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Amylase; Sprouting; Germination; Kernel (algebra); Alpha-amylase; Food science; Environmental science; Remote sensing; Chemistry; Biological system; Horticulture; Agronomy; Mathematics; Biology; Enzyme; Biochemistry; Geology","score_opus":0.057330787547892156,"score_gpt":0.3327567328606009,"score_spread":0.27542594531270875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053795316","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":[0.99865913,0.000022914392,0.0008134854,0.0000067360475,0.0000011668014,0.000007723661,0.00011073888,0.00001867249,0.00035944898],"genre_scores_gemma":[0.9943785,0.00006207517,0.0038076034,0.000013236596,7.485052e-7,0.0000068043496,0.00027285007,0.000014406883,0.0014438147],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99991035,0.0000026170064,0.0000019754652,0.000030700936,0.0000393653,0.000014938418],"domain_scores_gemma":[0.9998665,0.00001176017,0.000012168776,0.000003983409,0.000086764485,0.000018774967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013295976,0.0003319922,0.00019046398,0.0003606129,0.00075937813,0.0005357821,0.0003077322,0.00019495343,0.00032206785],"category_scores_gemma":[0.00021199144,0.00017699643,0.0001531455,0.0004311615,0.0002589004,0.0001605195,0.00014389002,0.00024261976,0.000100860765],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006640887,0.00016289334,0.18188143,0.000042268137,0.00006523763,0.00010446739,0.0006825409,0.0032498,0.7853096,0.000100412915,0.00036365347,0.027373526],"study_design_scores_gemma":[0.000015533695,0.00011703109,0.8967048,0.0000047983017,0.0000787519,0.00011296472,0.00047694438,0.01827119,0.08335295,0.000030879066,0.0008081263,0.000025979827],"about_ca_topic_score_codex":0.8460379,"about_ca_topic_score_gemma":0.92912525,"teacher_disagreement_score":0.15396208,"about_ca_system_score_codex":0.0025599843,"about_ca_system_score_gemma":0.002033453,"threshold_uncertainty_score":0.30973756},"labels":[],"label_agreement":null},{"id":"W2059840627","doi":"10.1007/s11694-008-9068-7","title":"Use of spectroscopic data for automation in food processing industry","year":2009,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Canada Research Chairs","keywords":"Automation; Chemometrics; Spectroscopy; Food industry; Data processing; Food processing; Computer science; Process engineering; Chemistry; Analytical Chemistry (journal); Biological system; Environmental science; Engineering; Food science; Environmental chemistry; Physics; Machine learning; Mechanical engineering; Database","score_opus":0.14514272514292387,"score_gpt":0.37989336688483316,"score_spread":0.2347506417419093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059840627","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":[0.33778408,0.0057027256,0.6226949,0.0026580016,0.00040487715,0.000243389,0.002176045,0.004772637,0.023563411],"genre_scores_gemma":[0.8189566,0.002341854,0.17503706,0.00039548517,0.00014067133,0.00008875829,0.00070140476,0.00021787248,0.002120177],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981517,0.0005362943,0.00010527711,0.00020541222,0.00094017974,0.00006107189],"domain_scores_gemma":[0.9952114,0.001490771,0.00061473344,0.0010784388,0.0014718645,0.0001328054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020019924,0.0005602316,0.00036164123,0.0024419217,0.0005755893,0.0013114479,0.00084963493,0.0007432122,0.0015195957],"category_scores_gemma":[0.004746986,0.00035635623,0.00032771018,0.0022327555,0.0006410082,0.0014769174,0.00091172464,0.0009196198,0.0011039939],"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":[0.0005665642,0.00029982036,0.01613661,0.0005712619,0.00007322449,0.00029166593,0.00036250774,0.0037627516,0.58609664,0.009497699,0.00338396,0.3789574],"study_design_scores_gemma":[0.00004968703,0.000340083,0.016050663,0.000098187156,0.000119812976,0.0011331685,0.00023540603,0.03422552,0.88934606,0.014422849,0.04388114,0.00009744303],"about_ca_topic_score_codex":0.0005210663,"about_ca_topic_score_gemma":0.00077556865,"teacher_disagreement_score":0.0024419217,"about_ca_system_score_codex":0.00041382093,"about_ca_system_score_gemma":0.0005817935,"threshold_uncertainty_score":0.010587633},"labels":[],"label_agreement":null},{"id":"W2061301323","doi":"10.1007/s11694-009-9072-6","title":"Application of RFID technologies in the temperature mapping of the pineapple supply chain","year":2009,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Food Supply Chain Traceability","field":"Agricultural and Biological Sciences","cited_by":100,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Foundation for Dietetic Research","keywords":"Pallet; Supply chain; Cold chain; Instrumentation (computer programming); Computer science; Process engineering; Temperature measurement; Environmental science; Real-time computing; Automotive engineering; Engineering; Mechanical engineering; Operating system; Business","score_opus":0.024212827006605535,"score_gpt":0.25498899550217846,"score_spread":0.23077616849557292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061301323","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":[0.7331854,0.0068876124,0.24902663,0.00043771768,0.0001558029,0.000042289765,0.00023446942,0.00063768105,0.009392323],"genre_scores_gemma":[0.9650392,0.0011047975,0.030488126,0.000043976805,0.000029348415,0.00000831471,0.00005423858,0.000010730256,0.003221331],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99980956,0.000048970316,0.000007515509,0.00004883488,0.0000658482,0.00001932147],"domain_scores_gemma":[0.99978095,0.00008349451,0.000034621557,0.000023803514,0.000065661276,0.000011506347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023406507,0.00018844064,0.0001340345,0.00049752946,0.00026688696,0.00044292936,0.00024794933,0.00031714363,0.0008632799],"category_scores_gemma":[0.00049267616,0.00012500856,0.00014674578,0.0005838444,0.00018049,0.000389576,0.00019956332,0.0001892309,0.0001995131],"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":[0.00057985936,0.00010223385,0.029011695,0.00022757621,0.000046752488,0.00077220844,0.00043247297,0.012887429,0.6495939,0.0019801422,0.0007088576,0.30365682],"study_design_scores_gemma":[0.000030801355,0.00074956304,0.07853019,0.00006372087,0.00013883425,0.0033496027,0.0008511655,0.1020464,0.78790826,0.0022376538,0.02400607,0.0000877454],"about_ca_topic_score_codex":0.0025952712,"about_ca_topic_score_gemma":0.0028580695,"teacher_disagreement_score":0.0025952712,"about_ca_system_score_codex":0.00019141159,"about_ca_system_score_gemma":0.00021773264,"threshold_uncertainty_score":0.005160272},"labels":[],"label_agreement":null}]}