{"meta":{"query_hash":"958637c6b147","filters":{"venue":"Geomatics"},"cohort_total":21,"direct_labels_cover":0,"predictions_cover":21,"exported":21,"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/958637c6b147","api":"https://metacan.xera.ac/api/v1/cohort?venue=Geomatics"},"results":[{"id":"W3116164275","doi":"10.3390/geomatics1010001","title":"Geomatics—An Open Access Journal","year":2020,"lang":"en","type":"article","venue":"Geomatics","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Geomatics; Data science; Computer science; World Wide Web; Geography; Library science; Remote sensing","score_opus":0.11482566743011538,"score_gpt":0.3496529051387068,"score_spread":0.2348272377085914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116164275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16803518,0.000055279117,0.8235239,0.0015728163,0.00026058865,0.00039351147,0.000020346633,0.00047018827,0.0056682066],"genre_scores_gemma":[0.8332017,0.00005109025,0.16510698,0.0009613504,0.0004842743,0.00004989701,0.000030078985,0.00008084002,0.000033759698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909556,0.000016048098,0.00036090892,0.00011657786,0.00018462597,0.00022625622],"domain_scores_gemma":[0.99924725,0.00003325457,0.00006289146,0.0003136979,0.00007409571,0.00026881995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001357462,0.00013347424,0.00018054392,0.00003430384,0.00013059199,0.0009310083,0.0017813249,0.000058880407,0.00023657287],"category_scores_gemma":[0.00006428338,0.00013444359,0.000028178996,0.00020515997,0.00001737896,0.0007768288,0.0003694835,0.00022151154,0.00024200995],"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":[0.0000035030284,0.00006390767,0.00036476835,0.00016973563,0.00007678227,0.000009462184,0.0027661186,0.9466179,0.0011899102,0.0018662399,0.029628294,0.017243396],"study_design_scores_gemma":[0.00020697262,0.00003685352,0.00035525358,0.000020281612,0.00002703372,0.000016443584,0.0000859273,0.9837602,0.000477875,0.009087489,0.005725259,0.00020043094],"about_ca_topic_score_codex":0.000014677488,"about_ca_topic_score_gemma":0.000014280909,"teacher_disagreement_score":0.66516656,"about_ca_system_score_codex":0.000034763878,"about_ca_system_score_gemma":0.000039940867,"threshold_uncertainty_score":0.89777344},"labels":[],"label_agreement":null},{"id":"W3118731078","doi":"10.3390/geomatics1010004","title":"Train Fast While Reducing False Positives: Improving Animal Classification Performance Using Convolutional Neural Networks","year":2021,"lang":"en","type":"article","venue":"Geomatics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Computer Research Institute of Montréal; Université de Sherbrooke","funders":"Mitacs","keywords":"Computer science; False positive paradox; Artificial intelligence; Classifier (UML); False positives and false negatives; Machine learning; Convolutional neural network; Pattern recognition (psychology); Binary classification; Data mining; Support vector machine","score_opus":0.04118448344027419,"score_gpt":0.253090894192409,"score_spread":0.2119064107521348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118731078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3783615,0.00010027004,0.6205074,0.00019351799,0.00022726019,0.00006823663,0.0000017062,0.00009096811,0.00044912234],"genre_scores_gemma":[0.89264286,0.0000063858424,0.10684684,0.00021590991,0.00014215746,0.0000047757358,0.00002303112,0.000012149744,0.00010592071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854845,0.00012694523,0.00035030406,0.0003410072,0.00028756697,0.00034575583],"domain_scores_gemma":[0.9991024,0.00010592755,0.0002014296,0.0003024963,0.00018941693,0.000098317934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034536314,0.00014331758,0.00015237223,0.000072272494,0.00040810704,0.0003086912,0.00027874025,0.00007393533,0.000025075296],"category_scores_gemma":[0.00008109168,0.00015928519,0.000059827973,0.0004341254,0.00005679522,0.0007076628,0.00013653564,0.00023592354,0.000011886829],"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":[0.000026263353,0.00018852878,0.0036499447,0.00015481052,0.000060690723,0.000057961402,0.007965855,0.61539966,0.082647294,0.0373847,0.0001409503,0.25232333],"study_design_scores_gemma":[0.00022427748,0.000036099973,0.024673492,0.000047007983,0.000009106579,0.00009817654,0.0007848797,0.97348243,0.00033951402,0.000052088704,0.00007392663,0.00017897072],"about_ca_topic_score_codex":0.0000077351,"about_ca_topic_score_gemma":0.0000021342032,"teacher_disagreement_score":0.51428133,"about_ca_system_score_codex":0.00010218283,"about_ca_system_score_gemma":0.00015858754,"threshold_uncertainty_score":0.6495462},"labels":[],"label_agreement":null},{"id":"W3119783950","doi":"10.3390/geomatics1010003","title":"Assessing the Potential of Artificial Intelligence (Artificial Neural Networks) in Predicting the Spatiotemporal Pattern of Wildfire-Generated PM2.5 Concentration","year":2021,"lang":"en","type":"article","venue":"Geomatics","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Government of Alberta; University of Calgary","funders":"","keywords":"Artificial neural network; Environmental science; Multilayer perceptron; Term (time); Linear regression; Satellite; Predictive modelling; Regression analysis; Meteorology; Computer science; Remote sensing; Machine learning; Geography; Engineering","score_opus":0.0468424555075828,"score_gpt":0.29114152809278443,"score_spread":0.24429907258520162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119783950","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90469134,0.000027192005,0.09442917,0.00028122863,0.00040788,0.00011194383,0.0000029767755,0.000011458299,0.00003682357],"genre_scores_gemma":[0.9990225,0.0000035476812,0.00069288915,0.00003153977,0.00021901084,0.0000034141942,0.000015169933,0.000008086345,0.0000038286666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998371,0.00027213653,0.0006593016,0.0001536692,0.00033254188,0.00021135659],"domain_scores_gemma":[0.9992212,0.00018415159,0.0003416985,0.00020567892,0.000022735154,0.000024558569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000783589,0.00009706122,0.00014428777,0.000010003373,0.00018380972,0.0000749252,0.00017426445,0.000060797323,0.00004314055],"category_scores_gemma":[0.0001728999,0.00006885388,0.000048800266,0.00034844186,0.00023277038,0.00016585265,0.00011417671,0.0001871058,0.0000019818872],"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":[0.0000061824,0.00007269535,0.10339462,0.000015896527,0.0000088732,0.000005188978,0.0024835702,0.7022576,0.009153961,0.00003501173,0.000009210098,0.18255723],"study_design_scores_gemma":[0.000027423252,0.000020303934,0.052545592,0.000043354365,0.000016266737,0.0000040121613,0.0032776664,0.92545426,0.018154874,0.00038922002,0.0000014162231,0.00006559895],"about_ca_topic_score_codex":0.00067428604,"about_ca_topic_score_gemma":0.00019131733,"teacher_disagreement_score":0.2231967,"about_ca_system_score_codex":0.000040144838,"about_ca_system_score_gemma":0.000023968269,"threshold_uncertainty_score":0.28077796},"labels":[],"label_agreement":null},{"id":"W3130545654","doi":"10.3390/geomatics1010007","title":"Application of Multimodel Superensemble Technique on the TIGGE Suite of Operational Models","year":2021,"lang":"en","type":"article","venue":"Geomatics","topic":"Climate variability and models","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Meteorology; Geopotential height; Climatology; Predictability; Environmental science; Numerical weather prediction; Model output statistics; Downscaling; Range (aeronautics); Radiosonde; Percentile; Precipitation; Geography; Statistics; Mathematics; Geology","score_opus":0.02772531547299718,"score_gpt":0.2434540875762328,"score_spread":0.21572877210323563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130545654","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7175099,0.000004441754,0.2759237,0.00038824626,0.000011006335,0.0003080668,0.000041840587,0.000010707713,0.0058020907],"genre_scores_gemma":[0.9794851,0.00000924014,0.020211933,0.00012367514,0.000004653042,0.00005360132,0.000021682732,0.0000052794685,0.00008481313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993111,0.000039593935,0.00022732181,0.00012420623,0.00021238426,0.000085405554],"domain_scores_gemma":[0.99940497,0.00013789073,0.000058104273,0.00035083818,0.00002824979,0.000019960531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029269123,0.000060232334,0.000099820536,0.000009783484,0.000047706726,0.000005723912,0.00012900992,0.000048589274,0.00035170335],"category_scores_gemma":[0.000044924494,0.000044987533,0.000037778933,0.00011388741,0.00008634321,0.00007185803,0.000096638345,0.000053373886,0.000022558277],"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":[0.000007726362,0.0003451251,0.0015408573,0.0000511543,0.000009308554,2.9991867e-7,0.00084719853,0.6239445,0.30430943,0.067929,0.00023666659,0.0007787209],"study_design_scores_gemma":[0.000078596364,0.000016873893,0.0005848131,0.000012872133,0.0000071434683,0.0000016526908,0.00006154222,0.87351286,0.094063066,0.0315235,0.000084488434,0.000052567517],"about_ca_topic_score_codex":0.00014069973,"about_ca_topic_score_gemma":0.000034600696,"teacher_disagreement_score":0.26197523,"about_ca_system_score_codex":0.000030268971,"about_ca_system_score_gemma":0.00001743108,"threshold_uncertainty_score":0.3850905},"labels":[],"label_agreement":null},{"id":"W3139149656","doi":"10.3390/geomatics1020010","title":"S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices","year":2021,"lang":"en","type":"article","venue":"Geomatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Dead reckoning; Computer science; Inertial measurement unit; Real-time computing; Inertial navigation system; Pedestrian; Mobile device; Navigation system; Reliability (semiconductor); Step detection; Fuse (electrical); Noise (video); Artificial intelligence; Computer vision; Embedded system; Simulation; Global Positioning System; Engineering; Telecommunications; Inertial frame of reference","score_opus":0.013897757348680222,"score_gpt":0.22798957074716636,"score_spread":0.21409181339848612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139149656","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037504096,0.00071644643,0.9587223,0.00013008843,0.00048618243,0.00019136997,0.000054649023,0.0011257338,0.0010691277],"genre_scores_gemma":[0.41650072,0.00007995273,0.58213866,0.0002706572,0.0002486739,0.000112747555,0.00019379942,0.00011352321,0.00034124096],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904644,0.000013665811,0.0003034319,0.00016001098,0.00014779552,0.00032862218],"domain_scores_gemma":[0.9992132,0.00018576352,0.000052241223,0.00038584074,0.00011404187,0.00004891611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013400264,0.00016998585,0.00021663637,0.000096445314,0.00015226522,0.00011075919,0.00023006328,0.00017919033,0.00004601736],"category_scores_gemma":[0.00035465206,0.00018004118,0.000086520304,0.00027589474,0.000034540102,0.00010394894,0.00005006075,0.0001270438,0.000013951051],"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":[0.000014830459,0.00016791152,0.014561202,0.0037173126,0.00045315258,0.00016213978,0.0015446763,0.12695043,0.002878883,0.003813983,0.022168355,0.8235671],"study_design_scores_gemma":[0.0007854769,0.000036446952,0.00036604295,0.00019773614,0.000053690783,0.000006491432,0.0007004692,0.8899275,0.09686381,0.001829968,0.008897018,0.00033534397],"about_ca_topic_score_codex":0.000013674953,"about_ca_topic_score_gemma":0.00014200585,"teacher_disagreement_score":0.82323176,"about_ca_system_score_codex":0.000055860874,"about_ca_system_score_gemma":0.000053933632,"threshold_uncertainty_score":0.73418665},"labels":[],"label_agreement":null},{"id":"W3139632910","doi":"10.3390/geomatics1020012","title":"Target Based 2D Digital Image Correlation Deflection Monitoring to Analyze the Environmental Effect on Variations of Deflection on Structures","year":2021,"lang":"en","type":"article","venue":"Geomatics","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Deflection (physics); Digital image correlation; Perpendicular; Deflection angle; Optics; Structural engineering; Computer science; Environmental science; Engineering; Physics; Mathematics; Geometry","score_opus":0.009434989527050464,"score_gpt":0.21950321130132192,"score_spread":0.21006822177427145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139632910","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99251544,0.000023689328,0.0052644094,0.0000537351,0.00044052413,0.00012802973,0.000084564184,0.000025347166,0.0014642373],"genre_scores_gemma":[0.9981361,0.0000016765039,0.0014431066,0.00003544472,0.000077777324,0.0000015406448,0.00021498771,0.0000032251876,0.00008614439],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992313,0.00012257393,0.00015032415,0.00013400764,0.00024090735,0.00012089077],"domain_scores_gemma":[0.999424,0.00031322654,0.00006644042,0.00013684428,0.000016507638,0.00004301719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014894508,0.0000959577,0.0000960926,0.000039583483,0.0002271739,0.00008614314,0.00005857478,0.00004359701,0.00023973463],"category_scores_gemma":[0.00011811468,0.00006545459,0.000053829594,0.000205221,0.000018255225,0.00011298618,0.000004525664,0.000100953395,0.00008667709],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007337192,0.000042917643,0.7301998,0.000023193295,0.000034453835,0.000004269761,0.00044281958,0.23340414,0.004227329,0.000026543832,0.000107280954,0.03141385],"study_design_scores_gemma":[0.00013777896,0.00034688073,0.9535272,0.000023711855,0.00001974235,0.000003884009,0.00014811217,0.03358649,0.011752802,0.0002834258,0.00007710522,0.00009286939],"about_ca_topic_score_codex":0.00008037405,"about_ca_topic_score_gemma":0.00008393994,"teacher_disagreement_score":0.22332737,"about_ca_system_score_codex":0.000016050468,"about_ca_system_score_gemma":0.00001397571,"threshold_uncertainty_score":0.26691607},"labels":[],"label_agreement":null},{"id":"W3165511578","doi":"10.3390/geomatics1020015","title":"Ultra-Low-Cost Tightly Coupled Triple-Constellation GNSS PPP/MEMS-Based INS Integration for Land Vehicular Applications","year":2021,"lang":"en","type":"article","venue":"Geomatics","topic":"GNSS positioning and interference","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"GNSS applications; Inertial measurement unit; Computer science; Global Positioning System; Satellite system; Precise Point Positioning; Kalman filter; Constellation; Real-time computing; Telecommunications; Artificial intelligence; Physics","score_opus":0.013199271392661844,"score_gpt":0.2304510511727326,"score_spread":0.21725177978007076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165511578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13758771,0.00015081823,0.85766184,0.00009746902,0.00025342678,0.0005984117,0.000081734484,0.00026527917,0.0033033113],"genre_scores_gemma":[0.9918603,0.00003103255,0.006710082,0.00006248672,0.00009007945,0.00017984145,0.0009540916,0.000025404097,0.000086726955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999275,0.0000144721525,0.00028718536,0.00014181106,0.00011314437,0.00016833919],"domain_scores_gemma":[0.9993545,0.00011573879,0.000050582796,0.0002471122,0.00017860418,0.000053460248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009143253,0.00013112742,0.0001538782,0.00006131961,0.00011396755,0.000090512985,0.00008410803,0.00010102928,0.000039486786],"category_scores_gemma":[0.000049629707,0.00013574921,0.00006661064,0.00019268351,0.00002186192,0.00008469755,0.0000047692492,0.000108410524,0.000042400978],"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":[0.000042636246,0.00043387356,0.0021949806,0.0017469301,0.00024345507,0.0000059940044,0.0015522295,0.6959371,0.23617595,0.0048696687,0.004700442,0.052096736],"study_design_scores_gemma":[0.00062971585,0.000028809607,0.0005930147,0.00017046618,0.00004962411,0.0000058988185,0.00008429356,0.87637854,0.118675366,0.0005055063,0.002678496,0.00020027402],"about_ca_topic_score_codex":0.0000072343464,"about_ca_topic_score_gemma":0.000035008794,"teacher_disagreement_score":0.85427254,"about_ca_system_score_codex":0.00007335018,"about_ca_system_score_gemma":0.00005013146,"threshold_uncertainty_score":0.55356926},"labels":[],"label_agreement":null},{"id":"W4205525589","doi":"10.3390/geomatics2010003","title":"Evaluating Scaling Frameworks for Multiscale Geomorphometric Analysis","year":2022,"lang":"en","type":"article","venue":"Geomatics","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Division of Earth Sciences; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Scaling; Computer science; Convolution (computer science); Resampling; Gaussian; Sizing; Interpolation (computer graphics); Scale (ratio); Algorithm; Quadratic equation; Mathematical optimization; Mathematics; Artificial intelligence; Physics","score_opus":0.024848342247363903,"score_gpt":0.3046671004596846,"score_spread":0.2798187582123207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205525589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9251943,0.00008972177,0.07318505,0.00014344855,0.00020768066,0.00029397392,0.000043350938,0.000046743266,0.0007957015],"genre_scores_gemma":[0.9400001,0.0000061463747,0.05881611,0.00020427328,0.00003059263,0.00008264094,0.000055346838,0.000014411202,0.0007903557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987334,0.000050659157,0.00025513046,0.00021704205,0.00046924807,0.00027453608],"domain_scores_gemma":[0.9993423,0.00022570613,0.00012135512,0.00023485473,0.000009089228,0.00006668971],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008057829,0.00009237747,0.00018100631,0.00013009124,0.0005634071,0.000033875636,0.00021772995,0.00008766609,0.005491717],"category_scores_gemma":[0.00015182616,0.00007952166,0.00016802597,0.0017992952,0.000035008266,0.000044838845,0.0003124775,0.00029007375,0.000056186153],"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":[0.000011114158,0.00007478196,0.031767137,0.000008996172,0.00017204003,0.0000027167655,0.000539785,0.93371946,0.00019639733,0.000025222655,0.0011848651,0.032297485],"study_design_scores_gemma":[0.00035334297,0.00008596751,0.014816094,0.0000028461243,0.0004707133,0.0000044453914,0.00031366633,0.9795696,0.00005573652,0.0010040688,0.0031666902,0.00015681003],"about_ca_topic_score_codex":0.000095277486,"about_ca_topic_score_gemma":0.0000111500685,"teacher_disagreement_score":0.045850158,"about_ca_system_score_codex":0.0001512465,"about_ca_system_score_gemma":0.0000068866666,"threshold_uncertainty_score":0.9954174},"labels":[],"label_agreement":null},{"id":"W4281760711","doi":"10.3390/geomatics2020013","title":"Automated Modeling of Road Networks for High-Definition Maps in OpenDRIVE Format Using Mobile Mapping Measurements","year":2022,"lang":"en","type":"article","venue":"Geomatics","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Point cloud; Interpolation (computer graphics); Road map; Data mining; Artificial intelligence; Geography; Cartography","score_opus":0.05982840189822466,"score_gpt":0.2611526710773143,"score_spread":0.20132426917908966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281760711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8297637,0.000015242691,0.1686314,0.000016200369,0.000079005054,0.0005951271,0.00002068147,0.00006160305,0.0008170343],"genre_scores_gemma":[0.94541276,0.0000026467833,0.054402687,0.00003463597,0.0000092557875,0.000036784146,0.00007985985,0.000013242936,0.0000081544085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990128,0.000043927794,0.00035527136,0.00014245046,0.00024465457,0.00020088283],"domain_scores_gemma":[0.9996049,0.000021045795,0.00015004478,0.00018360041,0.000012546182,0.000027879645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041649182,0.000083116975,0.00014049462,0.00005267283,0.00022750451,0.000014116404,0.00013187437,0.00003225543,0.00005024775],"category_scores_gemma":[0.00001149495,0.00009310166,0.00003453144,0.00027780244,0.000023818076,0.00009063267,0.00014533516,0.000080512225,0.0000055396404],"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":[0.0000047222225,0.00004943818,0.00030472234,0.000012791319,0.0000058471473,2.8682416e-7,0.00054956507,0.9948935,0.0018430543,0.000033270328,0.00012278576,0.0021800196],"study_design_scores_gemma":[0.00031584632,0.000025725196,0.00046795726,0.000023184426,0.000011578103,0.0000049816213,0.0006952861,0.9968486,0.00013165557,0.0013152531,0.00006266259,0.000097312586],"about_ca_topic_score_codex":0.0006758469,"about_ca_topic_score_gemma":0.000034380857,"teacher_disagreement_score":0.11564903,"about_ca_system_score_codex":0.00024196914,"about_ca_system_score_gemma":0.000011372857,"threshold_uncertainty_score":0.37965757},"labels":[],"label_agreement":null},{"id":"W4293235227","doi":"10.3390/geomatics2020011","title":"A Practical Algorithm for the Viewpoint Planning of Terrestrial Laser Scanners","year":2022,"lang":"en","type":"article","venue":"Geomatics","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Viewpoints; Polygon (computer graphics); Computer science; Laser scanning; Quality (philosophy); Algorithm; Mathematical optimization; Product (mathematics); Laser; Mathematics; Optics; Geology","score_opus":0.06406519091590984,"score_gpt":0.29210459069045913,"score_spread":0.2280393997745493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293235227","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9364584,0.0014049461,0.0433008,0.006321712,0.0045783147,0.0017136161,0.0016334725,0.00014265707,0.0044460925],"genre_scores_gemma":[0.96660507,0.000009651475,0.03207985,0.00042099654,0.0002319234,0.0000151907025,0.00017892277,0.0000046473046,0.00045374368],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929386,0.00009247491,0.00016637237,0.000081550745,0.00021118991,0.00015453994],"domain_scores_gemma":[0.99915105,0.0006179752,0.00008292017,0.0001014932,0.000012018742,0.000034523677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060120155,0.000057406665,0.00010089441,0.000013670494,0.00030583516,0.000021389058,0.00012840673,0.000017805529,0.0005728791],"category_scores_gemma":[0.00010348416,0.000036119476,0.000055377666,0.00009675214,0.000039075374,0.00004791373,0.000015856433,0.0001137798,0.000008747477],"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":[0.00032046132,0.00014192742,0.045926943,0.000105057996,0.0002509264,0.00005766526,0.0061118463,0.08747986,0.000031435586,0.00012039175,0.09742163,0.76203185],"study_design_scores_gemma":[0.0008090256,0.00051779585,0.028621983,0.000021398148,0.00007117237,0.000056446086,0.009135803,0.8999555,0.00007061888,0.0008971584,0.05962139,0.00022167439],"about_ca_topic_score_codex":0.00024941255,"about_ca_topic_score_gemma":0.00006583357,"teacher_disagreement_score":0.8124757,"about_ca_system_score_codex":0.0000029053276,"about_ca_system_score_gemma":0.0000407806,"threshold_uncertainty_score":0.6272624},"labels":[],"label_agreement":null},{"id":"W4295886818","doi":"10.3390/geomatics2030021","title":"Classification of Multispectral Airborne LiDAR Data Using Geometric and Radiometric Information","year":2022,"lang":"en","type":"article","venue":"Geomatics","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Multispectral image; Remote sensing; Point cloud; Histogram; Ranging; Feature (linguistics); Geography; Environmental science; Computer science; Artificial intelligence; Geodesy","score_opus":0.03870479429923484,"score_gpt":0.2637486229524273,"score_spread":0.22504382865319245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295886818","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97226846,0.000040825074,0.02607001,0.00009620862,0.00004962484,0.00017636878,0.000055675042,0.000024073346,0.0012187408],"genre_scores_gemma":[0.97174805,0.000012820584,0.028045686,0.000029150477,0.000009970739,0.0000014416963,0.00012526171,0.0000051825195,0.000022438353],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991496,0.000035227316,0.00025760735,0.0001268443,0.00031304974,0.00011766082],"domain_scores_gemma":[0.99922574,0.000060213453,0.00018865109,0.0004757977,0.0000083995255,0.000041221076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036989112,0.00006155094,0.000092080525,0.0002727289,0.00020966116,0.000023083878,0.00024505577,0.000021707703,0.0001407809],"category_scores_gemma":[0.0000972545,0.000065229106,0.00001342295,0.0020086912,0.00007227355,0.0003037919,0.00036909664,0.000086418935,0.000022687937],"study_design_candidate":"design_other","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":[0.000024268798,0.00042163965,0.078019775,0.00012668216,0.000060627714,0.000002147307,0.003816321,0.031697996,0.023513684,0.00082013896,0.0042947126,0.857202],"study_design_scores_gemma":[0.00021407836,0.000034608896,0.42527446,0.0000025753313,0.000028040999,0.000033803728,0.00049406756,0.56469434,0.00022870667,0.00018142244,0.008707999,0.000105878215],"about_ca_topic_score_codex":0.00036447475,"about_ca_topic_score_gemma":0.0000053113836,"teacher_disagreement_score":0.85709614,"about_ca_system_score_codex":0.00012422133,"about_ca_system_score_gemma":0.000013785465,"threshold_uncertainty_score":0.26599658},"labels":[],"label_agreement":null},{"id":"W4297426717","doi":"10.3390/geomatics2040022","title":"Modelling the Impact of Temperature under Climate Change Scenarios on Native and Invasive Vascular Vegetation on the Antarctic Peninsula and Surrounding Islands","year":2022,"lang":"en","type":"article","venue":"Geomatics","topic":"Polar Research and Ecology","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Peninsula; Climate change; Biodiversity; Vegetation (pathology); Environmental science; Global warming; Oceanography; Ecology; Biology; Geology","score_opus":0.03391365001942592,"score_gpt":0.26660646289111023,"score_spread":0.2326928128716843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297426717","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9988978,0.00008730916,0.000029564184,0.00037015308,0.00002646709,0.00033006075,0.000018593159,0.0000042799197,0.00023578097],"genre_scores_gemma":[0.99950314,0.00020508157,0.000029575054,0.00019255035,0.000012479182,0.00003325282,0.000007751291,0.0000071010827,0.000009048812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991258,0.00019853644,0.000090666814,0.00012399412,0.00025134016,0.00020967748],"domain_scores_gemma":[0.9993595,0.00040171252,0.000061865874,0.00013405917,0.0000062068866,0.00003666572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006058992,0.000081586266,0.00009432771,0.00003161933,0.00054169336,0.00003278045,0.000110454894,0.000024484465,0.00011872083],"category_scores_gemma":[0.000059893748,0.000044839595,0.000032512246,0.00013372293,0.00012377894,0.00008248925,0.00018753234,0.00023071835,0.000005833812],"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":[0.00015766306,0.00022689346,0.08906226,0.0001038235,0.00019106793,0.000016163764,0.04675823,0.8562541,0.0042390013,0.0022175715,0.0002490459,0.0005241543],"study_design_scores_gemma":[0.00062368007,0.001172954,0.12775037,0.000052307587,0.000032383196,0.000028545282,0.005757425,0.8572458,0.000289972,0.0068482105,0.000021713538,0.00017661693],"about_ca_topic_score_codex":0.0004058027,"about_ca_topic_score_gemma":0.00008231053,"teacher_disagreement_score":0.041000806,"about_ca_system_score_codex":0.00012775349,"about_ca_system_score_gemma":0.000010430729,"threshold_uncertainty_score":0.41663224},"labels":[],"label_agreement":null},{"id":"W4389538220","doi":"10.3390/geomatics3040029","title":"Evaluating OSM Building Footprint Data Quality in Québec Province, Canada from 2018 to 2023: A Comparative Study","year":2023,"lang":"en","type":"article","venue":"Geomatics","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"","keywords":"Footprint; Volunteered geographic information; Completeness (order theory); Quality (philosophy); Data quality; Computer science; Geography; Ecological footprint; Environmental science; Cartography; Operations management; Engineering; Mathematics; Sustainable development; Political science","score_opus":0.33370783339904964,"score_gpt":0.4847742749970433,"score_spread":0.15106644159799365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389538220","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99288386,0.000028588096,0.00013808723,0.0012903134,0.0004207556,0.0013654478,0.00012500977,0.000081826256,0.0036661047],"genre_scores_gemma":[0.9973759,0.0000021571232,0.0018138025,0.00009249381,0.00009521043,0.0000900846,0.000025899175,0.000005681541,0.00049875275],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9970108,0.0004485072,0.0007544307,0.00028225846,0.001102046,0.00040191703],"domain_scores_gemma":[0.99801683,0.0008540191,0.00026094803,0.0005861085,0.0001817039,0.00010040912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004421968,0.00013078122,0.00034949617,0.0001901472,0.0006036909,0.000101301746,0.00070041267,0.00003669768,0.000022316006],"category_scores_gemma":[0.0013309224,0.00012874427,0.000019638104,0.0014214596,0.00006777657,0.00017183379,0.0006908322,0.000119191754,0.000080669786],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001836645,0.0001336312,0.4779114,0.00008138275,0.00021879222,0.000012961983,0.48183566,0.0033475198,0.000028912422,0.0022150956,0.029408254,0.0047880057],"study_design_scores_gemma":[0.00047317593,0.00005438906,0.40660933,0.000081444065,0.000020646203,1.0397362e-7,0.56692934,0.00471084,0.000004327584,0.0005225916,0.020290857,0.00030294605],"about_ca_topic_score_codex":0.9959552,"about_ca_topic_score_gemma":0.9997143,"teacher_disagreement_score":0.08509371,"about_ca_system_score_codex":0.000480285,"about_ca_system_score_gemma":0.0018791935,"threshold_uncertainty_score":0.5250039},"labels":[],"label_agreement":null},{"id":"W4400280976","doi":"10.3390/geomatics4030013","title":"Classification of Coastal Benthic Substrates Using Supervised and Unsupervised Machine Learning Models on North Shore of the St. Lawrence Maritime Estuary (Canada)","year":2024,"lang":"en","type":"article","venue":"Geomatics","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"The Interdisciplinary Centre for the Development of Ocean Mapping","funders":"","keywords":"Benthic zone; Estuary; Shore; Benchmark (surveying); Intuition; Computer science; Machine learning; Artificial intelligence; Oceanography; Geography; Geology; Cartography; Cognitive science","score_opus":0.051572329392627965,"score_gpt":0.23752103021479237,"score_spread":0.18594870082216441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400280976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99637145,0.0002489908,0.0023161895,0.00013862675,0.000056699966,0.00015800205,0.00044028036,0.000014175497,0.0002555784],"genre_scores_gemma":[0.99860823,0.000060894257,0.0011082075,0.000013515679,0.0000136396575,4.3837954e-7,0.00016251949,0.0000057974607,0.000026750295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987306,0.00009962551,0.0002563352,0.00016351535,0.0005556077,0.0001943189],"domain_scores_gemma":[0.9992974,0.00035983062,0.00004712849,0.00016261851,0.00007102284,0.000062030755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022651398,0.000106936386,0.00014644754,0.00006818765,0.00012020171,0.000056647583,0.00020103727,0.000045412893,0.000118664364],"category_scores_gemma":[0.000041119038,0.000075584176,0.000027627091,0.00027086143,0.00016054799,0.00011566389,0.000032597214,0.00023855227,0.0000011586078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018485676,0.000018363988,0.44221276,0.00043188958,0.000034644603,0.000007314038,0.0006661095,0.5510531,0.0011097015,0.000075080716,0.000014295693,0.0043582306],"study_design_scores_gemma":[0.00006986186,0.00005222349,0.16606894,0.00009727364,0.000019628522,0.000003517739,0.0002790317,0.83257294,0.00030274742,0.00046440473,0.0000048438324,0.000064561296],"about_ca_topic_score_codex":0.052439045,"about_ca_topic_score_gemma":0.3495415,"teacher_disagreement_score":0.29710242,"about_ca_system_score_codex":0.000008338582,"about_ca_system_score_gemma":0.00030553926,"threshold_uncertainty_score":0.95387083},"labels":[],"label_agreement":null},{"id":"W4404991343","doi":"10.3390/geomatics4040023","title":"Advancements in Ocean Mapping and Nautical Cartography","year":2024,"lang":"en","type":"article","venue":"Geomatics","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Nautical chart; Cartography; Geography; Remote sensing; Oceanography; Geology","score_opus":0.007784067812650083,"score_gpt":0.1991732975215901,"score_spread":0.19138922970894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404991343","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9875223,0.007284892,0.0010530768,0.00017856815,0.00020016369,0.000070320195,0.000012288988,0.00006755094,0.0036108342],"genre_scores_gemma":[0.9940351,0.00045002834,0.0053070094,0.000114594935,0.000031297634,1.8750949e-7,0.000013711306,0.0000020851605,0.000045967565],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99941754,0.000010867323,0.000137059,0.00013730608,0.000113467286,0.00018376499],"domain_scores_gemma":[0.9997978,0.00006987874,0.000011656598,0.000057469464,0.000006193856,0.000056992558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011228062,0.00007520455,0.000088447305,0.000042115793,0.000043214368,0.00006666964,0.000060034014,0.000029800107,0.00024012907],"category_scores_gemma":[0.000013499181,0.00006022129,0.000019588648,0.0004836929,0.00006368665,0.00014254672,0.000007825684,0.00007895829,0.00002019451],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021389833,0.000007552509,0.89718807,0.00025244022,0.000013513058,0.000040188053,0.00050748844,0.00010691273,0.000001349882,0.00029275857,0.00024483833,0.10134277],"study_design_scores_gemma":[0.00023456814,0.00008885473,0.7967056,0.00035184357,0.000019095633,0.000029354687,0.0013046394,0.14905511,0.0000105887875,0.023103228,0.028810715,0.00028642954],"about_ca_topic_score_codex":0.00006599459,"about_ca_topic_score_gemma":0.00011418117,"teacher_disagreement_score":0.1489482,"about_ca_system_score_codex":9.259647e-7,"about_ca_system_score_gemma":0.000015543239,"threshold_uncertainty_score":0.2629245},"labels":[],"label_agreement":null},{"id":"W4406087312","doi":"10.3390/geomatics5010003","title":"Mapping Spatial Variability of Sugarcane Foliar Nitrogen, Phosphorus, Potassium and Chlorophyll Concentrations Using Remote Sensing","year":2025,"lang":"en","type":"article","venue":"Geomatics","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Phosphorus; Nitrogen; Potassium; Chlorophyll; Environmental science; Chlorophyll a; Agronomy; Spatial variability; Remote sensing; Horticulture; Chemistry; Botany; Biology; Geography; Mathematics","score_opus":0.007870293199984963,"score_gpt":0.21634471988887444,"score_spread":0.20847442668888946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406087312","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82249117,0.000031286294,0.17447132,0.00018229186,0.000205535,0.00024275658,0.000010178619,0.000030568714,0.002334879],"genre_scores_gemma":[0.86883277,0.000013582858,0.13093555,0.00009818961,0.000030450808,4.453074e-8,0.000006264746,0.000007770357,0.00007538341],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987401,0.00009685633,0.00038885028,0.00027085652,0.0002393077,0.0002640315],"domain_scores_gemma":[0.9992879,0.00012304845,0.00016788268,0.00031761747,0.000035423498,0.000068144785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003055887,0.00015892881,0.00024045291,0.000028812212,0.00018741233,0.000037477643,0.000103902516,0.000110995934,0.00003573804],"category_scores_gemma":[0.00021440318,0.00014247092,0.000050349667,0.00035391282,0.00027549898,0.0000720781,0.00018410149,0.00013987448,0.000006253746],"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":[0.000060917348,0.00021583996,0.15660407,0.00055364764,0.00026211122,0.00005023556,0.006091139,0.018330725,0.5877137,0.0010170264,0.0016569616,0.22744362],"study_design_scores_gemma":[0.00054341916,0.00003133144,0.08166909,0.0002611781,0.00012304535,0.000058312093,0.0005364502,0.8511792,0.040496834,0.023493243,0.0012135461,0.00039435536],"about_ca_topic_score_codex":0.0024171132,"about_ca_topic_score_gemma":0.00013231026,"teacher_disagreement_score":0.8328485,"about_ca_system_score_codex":0.00019621334,"about_ca_system_score_gemma":0.000034809153,"threshold_uncertainty_score":0.5809795},"labels":[],"label_agreement":null},{"id":"W4407193566","doi":"10.3390/geomatics5010009","title":"Advances in Remote Sensing and Deep Learning in Coastal Boundary Extraction for Erosion Monitoring","year":2025,"lang":"en","type":"article","venue":"Geomatics","topic":"Coastal and Marine Dynamics","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Computer science; Deep learning; Coastal erosion; Context (archaeology); Vulnerability (computing); Erosion; Environmental resource management; Environmental science; Artificial intelligence; Geology","score_opus":0.007311521141262804,"score_gpt":0.24239106504413685,"score_spread":0.23507954390287406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407193566","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9570434,0.0006528663,0.04022136,0.00006447593,0.0003446715,0.00013240501,0.0000036965707,0.000022679127,0.0015144293],"genre_scores_gemma":[0.9847153,0.0007380079,0.014309088,0.000013019542,0.00002931712,1.1943386e-7,0.00003094088,0.000001786944,0.00016242369],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995177,0.000019960762,0.00014937582,0.00010789936,0.000058423553,0.00014664904],"domain_scores_gemma":[0.9997219,0.00017218528,0.000036470577,0.000039644474,0.000012776629,0.000017058665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017239123,0.00006252869,0.000092674505,0.000097723016,0.00009314281,0.000043864784,0.000026685359,0.00003516539,0.0000031742882],"category_scores_gemma":[0.00008114718,0.00006142097,0.000012478156,0.00013930452,0.000020751604,0.00018722992,0.000017313823,0.0001142897,9.4800095e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000214494,0.0000021148871,0.20356257,0.00006848756,6.1821044e-7,0.0000030547828,0.000109339155,0.0065262397,0.000054337375,0.0000045258184,5.393701e-7,0.7896467],"study_design_scores_gemma":[0.00022271369,0.000030333451,0.30700806,0.00012802902,0.000003174027,0.0000047019316,0.0005746478,0.6863841,0.00002376537,0.003939015,0.0016186534,0.000062866944],"about_ca_topic_score_codex":0.0019201285,"about_ca_topic_score_gemma":0.06004144,"teacher_disagreement_score":0.78958386,"about_ca_system_score_codex":0.0000057376365,"about_ca_system_score_gemma":0.000017387414,"threshold_uncertainty_score":0.95711035},"labels":[],"label_agreement":null},{"id":"W4410239071","doi":"10.3390/geomatics5020020","title":"Integrating Sustainability Reflection in a Geographic Information Science Capstone Project Course","year":2025,"lang":"en","type":"article","venue":"Geomatics","topic":"Sustainability in Higher Education","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Sustainability; Reflection (computer programming); Course (navigation); Capstone; Capstone course; Sustainability science; Engineering ethics; Sociology; Knowledge management; Engineering; Computer science; Sustainability organizations","score_opus":0.012404557422374266,"score_gpt":0.3988834950295723,"score_spread":0.38647893760719804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410239071","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97609407,0.000029669542,0.0012562762,0.004118475,0.00061836955,0.0012508022,0.0000011001187,0.0001313536,0.016499868],"genre_scores_gemma":[0.99795264,0.0000115971,0.0015237012,0.00012176547,0.000035628076,0.00013828528,0.0000052193795,0.0000027869696,0.00020840281],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99817145,0.0002436664,0.00044018208,0.00019181434,0.0004954353,0.00045745607],"domain_scores_gemma":[0.9979521,0.00022559206,0.00014511055,0.00031646204,0.0013121319,0.00004861852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045755194,0.00010096547,0.00013325187,0.0008600014,0.00077323226,0.00025738103,0.00033214575,0.00010544574,0.000011397912],"category_scores_gemma":[0.0077637592,0.00010588866,0.000036556,0.005299724,0.0009504322,0.0017443902,0.00006424788,0.00021702115,0.0000040079353],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023590128,0.0003713899,0.4898527,0.00045414595,0.000010071764,9.2054603e-7,0.20719664,0.00035947625,0.000030854826,0.2605314,0.00043600056,0.040732823],"study_design_scores_gemma":[0.00036744415,0.000053859472,0.33894694,0.00011867316,0.000027634187,8.4090266e-7,0.52540714,0.0025451025,0.00009750341,0.115592465,0.016576005,0.0002663887],"about_ca_topic_score_codex":0.01898872,"about_ca_topic_score_gemma":0.006388505,"teacher_disagreement_score":0.31821048,"about_ca_system_score_codex":0.0024268506,"about_ca_system_score_gemma":0.005896771,"threshold_uncertainty_score":0.9997389},"labels":[],"label_agreement":null},{"id":"W4411281768","doi":"10.3390/geomatics5020024","title":"Review of the Problem of the Earth Shape","year":2025,"lang":"en","type":"article","venue":"Geomatics","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Grantová Agentura České Republiky","keywords":"Earth (classical element); Astrobiology; Geology; Physics; Astronomy","score_opus":0.016523477698417863,"score_gpt":0.21480426982234338,"score_spread":0.19828079212392552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411281768","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87055606,0.022712277,0.00003721676,0.0048935045,0.00081982417,0.0015887336,0.00018411649,0.00001555383,0.09919271],"genre_scores_gemma":[0.9964618,0.0006609472,0.0007037179,0.0013949752,0.000012664198,7.7189884e-7,0.0000053055182,0.0000010945855,0.00075874216],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994553,0.000048552913,0.00017871194,0.000048264246,0.00019367278,0.00007553804],"domain_scores_gemma":[0.9995564,0.000030290397,0.00011244117,0.00023528181,0.000055286517,0.000010253235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024748084,0.000043461765,0.00010466786,0.000007965037,0.000053999258,0.00000451758,0.00027397685,0.00001431111,0.00016429223],"category_scores_gemma":[0.000031536114,0.000020774247,0.0000691625,0.00026310614,0.000048721082,0.000017353585,0.000022015865,0.000048944185,0.000010876489],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042284573,0.00007726454,0.82829916,0.01489679,0.000094886345,1.5966192e-7,0.0002599298,0.00038170748,0.0002769268,0.0026382452,0.011760205,0.14131048],"study_design_scores_gemma":[0.000106370346,0.000020310314,0.9550592,0.0056766463,0.00006444381,4.5904443e-7,0.000024848732,0.0010672617,0.0010670298,0.017661134,0.019195847,0.000056453395],"about_ca_topic_score_codex":0.00018499927,"about_ca_topic_score_gemma":0.00020210078,"teacher_disagreement_score":0.14125402,"about_ca_system_score_codex":5.041568e-7,"about_ca_system_score_gemma":0.000054524906,"threshold_uncertainty_score":0.17988847},"labels":[],"label_agreement":null},{"id":"W4414923838","doi":"10.3390/geomatics5040052","title":"Visual Foundation Models for Archaeological Remote Sensing: A Zero-Shot Approach","year":2025,"lang":"en","type":"article","venue":"Geomatics","topic":"Archaeological Research and Protection","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Foundation (evidence); Workflow; Drone; Feature (linguistics); Adaptation (eye); Key (lock); Scripting language","score_opus":0.07607259391349981,"score_gpt":0.31408912823013074,"score_spread":0.23801653431663095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414923838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04477015,0.0000667622,0.9422773,0.00052208215,0.000102054684,0.0006035928,0.000010202102,0.0000732321,0.011574601],"genre_scores_gemma":[0.70304257,0.00003385125,0.29589376,0.00024051641,0.00005220016,0.0000013333138,0.00017351138,0.0000019288473,0.0005603451],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881035,0.00011573713,0.00021776705,0.00024586203,0.00022799976,0.00038229485],"domain_scores_gemma":[0.99919033,0.00044886934,0.00004882676,0.00013333737,0.000077613724,0.00010103841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006134207,0.00011249804,0.00016419972,0.0001026288,0.00029006455,0.000035948873,0.0001434078,0.00011946392,0.00011854348],"category_scores_gemma":[0.00048710636,0.00008347108,0.000068782785,0.00024296541,0.00021197996,0.00010841801,0.000040353985,0.00017309742,0.00003908037],"study_design_candidate":"design_other","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":[0.00022184405,0.000010826633,0.0006080364,0.00011009084,0.000024693174,0.0000027383505,0.00019495511,0.015994323,0.000028998218,0.0038980169,0.00021215026,0.9786933],"study_design_scores_gemma":[0.00014483709,0.00015883638,0.0018928187,0.00000872555,0.000005402003,0.00000397178,0.000042479132,0.58202624,0.000037280366,0.4148297,0.0007915676,0.000058137357],"about_ca_topic_score_codex":0.0004550603,"about_ca_topic_score_gemma":0.00020958291,"teacher_disagreement_score":0.9786352,"about_ca_system_score_codex":0.000008598611,"about_ca_system_score_gemma":0.000068186375,"threshold_uncertainty_score":0.34038517},"labels":[],"label_agreement":null},{"id":"W4417050844","doi":"10.3390/geomatics5040071","title":"On the Accurate Determination of the Orthometric Correction to Levelled Height Differences—A Case Study in Hong Kong","year":2025,"lang":"en","type":"article","venue":"Geomatics","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of New Brunswick","funders":"Hong Kong Government; Hong Kong Polytechnic University; Grantová Agentura České Republiky; Západočeská Univerzita v Plzni","keywords":"Levelling; Geoid; Undulation of the geoid; Geodetic datum; Dynamic height; Terrain","score_opus":0.041632179716399376,"score_gpt":0.25575771230550237,"score_spread":0.214125532589103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417050844","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99776673,0.000007634997,0.00030055392,0.000110300316,0.00070138794,0.00050485693,0.0000067739165,0.000004301577,0.00059746526],"genre_scores_gemma":[0.9996525,9.86814e-7,0.000031725132,0.000070779104,0.0000079745405,0.0000030674987,0.0000016338489,9.579645e-7,0.00023037335],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99919707,0.00014901462,0.00019522969,0.000105178806,0.00024282228,0.0001107086],"domain_scores_gemma":[0.9992615,0.00039620142,0.000082238425,0.00019474974,0.000046687062,0.00001861477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041645038,0.000073879695,0.00010618798,0.00014595252,0.0001542907,0.000037029353,0.0001663032,0.00001877661,0.00004087801],"category_scores_gemma":[0.0002803086,0.000039351955,0.000028502322,0.0011968327,0.000014797894,0.00003761578,0.000017776158,0.00009451028,0.000013245438],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007653314,0.000080503014,0.9666269,0.0000099538265,0.000007553799,0.000010326239,0.00084231823,0.00071243633,0.000007106259,0.000021048358,0.00006068309,0.031613547],"study_design_scores_gemma":[0.0001520719,0.00009250851,0.9764388,0.000041134845,0.000013004312,0.0000031632555,0.0015644934,0.020767739,0.00007482195,0.00080340536,0.0000033390832,0.00004552529],"about_ca_topic_score_codex":0.0028209107,"about_ca_topic_score_gemma":0.021845223,"teacher_disagreement_score":0.03156802,"about_ca_system_score_codex":0.000006270321,"about_ca_system_score_gemma":0.000030980842,"threshold_uncertainty_score":0.99600357},"labels":[],"label_agreement":null}]}