{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":144,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":144,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"512e9c477960","filters":{"venue":"Computers & Geosciences"}},"results":[{"id":"W2063987149","doi":"10.1016/j.cageo.2015.04.007","title":"Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling","year":2015,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":823,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Random forest; Logistic regression; Landslide; Artificial intelligence; Machine learning; Computer science; Receiver operating characteristic; Linear discriminant analysis; Statistics; Statistical model; False positive rate; Data mining; Scale (ratio); Mathematics; Geology; Cartography; Geography; Geomorphology","retraction":null,"screen_n_in":null,"score":{"opus":0.04085002647121699,"gpt":0.3151756414818519,"spread":0.2743256150106349,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00139164,0.00009387091,0.0001129423,0.00002611608,0.0002714273,0.00008139211,0.00012674,0.00005012491,0.00002688453],"category_scores_gemma":[0.0001608306,0.00006727466,0.00002125416,0.0001137545,0.0001874634,0.0001892587,0.0001688839,0.0001131305,0.000004577293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006122849,"about_ca_system_score_gemma":0.00002028849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003787429,"about_ca_topic_score_gemma":0.00005906749,"domain_scores_codex":[0.9988829,0.00006266892,0.0001761632,0.000353686,0.0003003599,0.0002242327],"domain_scores_gemma":[0.9996167,0.00009942719,0.00004841524,0.00007595278,0.00002035611,0.0001391279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000106573,0.00008355881,0.367056,0.0000294176,0.00001276016,0.000003971864,0.001944647,0.1389714,0.001623709,0.0002137371,0.0008293578,0.489125],"study_design_scores_gemma":[0.0002210444,0.0004268794,0.001545692,0.00001368629,0.00001097101,0.000009050026,0.0001170049,0.9935086,0.00005022032,0.002047257,0.001955487,0.00009405642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.43475,0.00007439353,0.5641539,0.0001134922,0.000195604,0.0002057682,0.00001201073,0.00009973635,0.0003951703],"genre_scores_gemma":[0.8592263,0.00002092504,0.1405557,0.00004135321,0.00005033216,0.00001083087,0.00001512345,0.000005163932,0.00007431336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8545373,"threshold_uncertainty_score":0.2743381,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2001555409","doi":"10.1016/j.cageo.2013.09.011","title":"An Excel spreadsheet to classify chemical analyses of amphiboles following the IMA 2012 recommendations","year":2013,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":447,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Amphibole; Microsoft excel; Prefix; Orthorhombic crystal system; Computer science; Wollastonite; Mineralogy; Database; Chemistry; Geology; Crystallography; Crystal structure; Organic chemistry","retraction":null,"screen_n_in":null,"score":{"opus":0.0418556032836355,"gpt":0.3141429906150016,"spread":0.2722873873313661,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000465603,0.0001676516,0.0002176386,0.0001042414,0.0003107951,0.0003346944,0.002603182,0.00005589876,0.00006324772],"category_scores_gemma":[0.00009735792,0.0001134645,0.0001240235,0.0009447562,0.0002134877,0.0010069,0.0005271357,0.0001164767,0.00003554512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000169114,"about_ca_system_score_gemma":0.00006350931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003012608,"about_ca_topic_score_gemma":0.00001046558,"domain_scores_codex":[0.9983119,0.0000984135,0.0003293685,0.0005243352,0.0003343871,0.0004015337],"domain_scores_gemma":[0.9985327,0.0002629916,0.0001403665,0.000733738,0.0001416388,0.0001885342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006238828,0.0005795234,0.01103682,0.00004957384,0.0001187556,0.00001144558,0.005936892,0.005894227,0.6092651,0.01188311,0.09408262,0.2611357],"study_design_scores_gemma":[0.000643393,0.0004945109,0.07088257,0.0002749824,0.00006604851,0.00005696906,0.00236873,0.6711539,0.1763393,0.02772125,0.04853097,0.00146738],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4466612,0.0001166473,0.5129976,0.03572748,0.001127012,0.0003204294,0.000003574751,0.0001728817,0.002873165],"genre_scores_gemma":[0.9292149,0.000003568033,0.06964169,0.000852678,0.00007681797,0.00002566184,0.000005267842,0.000001894362,0.0001775057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6652597,"threshold_uncertainty_score":0.4837403,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2004803051","doi":"10.1016/j.cageo.2010.07.005","title":"Quantile regression neural networks: Implementation in R and application to precipitation downscaling","year":2010,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":402,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Environment and Climate Change Canada","funders":"Division of Ocean Sciences","keywords":"Quantile regression; Quantile; Overfitting; Cumulative distribution function; Artificial neural network; Downscaling; Statistics; Computer science; Mathematics; Probability density function; Precipitation; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01082930111063916,"gpt":0.2858230807523144,"spread":0.2749937796416753,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004725959,0.00008366223,0.00007703635,0.00005309358,0.0001754198,0.00007680762,0.000196066,0.00003936453,0.00004760161],"category_scores_gemma":[0.00003511981,0.00006673031,0.00001252794,0.0004209531,0.0001536357,0.000239284,0.0001632466,0.0001094168,0.00001827782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002541023,"about_ca_system_score_gemma":0.000003107169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006252537,"about_ca_topic_score_gemma":0.0009204732,"domain_scores_codex":[0.999011,0.00004074778,0.0001697494,0.0003663785,0.0001881259,0.0002239772],"domain_scores_gemma":[0.9996533,0.00007479822,0.0000672478,0.0001102545,0.000005401449,0.00008900921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000901041,0.00002930839,0.1925806,0.000002186031,5.00257e-7,0.000001133452,0.001189725,0.2857034,0.02399424,0.00008544469,0.0001991349,0.4962053],"study_design_scores_gemma":[0.00007414327,0.00005643259,0.2673339,0.000006545447,7.15113e-7,0.00000285855,0.00003901094,0.7316698,0.0001457265,0.0002985954,0.0002980661,0.00007417225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9451988,0.000006636699,0.05363202,0.0005004241,0.0003396833,0.0002203424,5.745588e-7,0.00003794007,0.00006355743],"genre_scores_gemma":[0.9878266,0.000001756692,0.01182082,0.0002818966,0.00003874671,0.00001742481,0.000004991887,0.00000328969,0.000004425827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4961312,"threshold_uncertainty_score":0.2721183,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2070142384","doi":"10.1016/j.cageo.2003.09.006","title":"AgeDisplay: an EXCEL workbook to evaluate and display univariate geochronological data using binned frequency histograms and probability density distributions","year":2003,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":360,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Histogram; Frequency distribution; Univariate; Kernel density estimation; Probability distribution; Statistics; Computer science; Probability density function; Joint probability distribution; Skew normal distribution; Algorithm; Mathematics; Skewness; Multivariate statistics; Artificial intelligence; Estimator","retraction":null,"screen_n_in":null,"score":{"opus":0.05768150800346178,"gpt":0.2877659379496448,"spread":0.2300844299461831,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001100544,0.0001766179,0.0001834729,0.00003456452,0.0007534314,0.0001932575,0.0004772156,0.0000547483,0.00004459777],"category_scores_gemma":[0.0002552136,0.0001478634,0.00001514174,0.0003671535,0.001035306,0.0004407224,0.0007932176,0.000112439,0.000008281641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001258106,"about_ca_system_score_gemma":0.00004596929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002259405,"about_ca_topic_score_gemma":0.0007895246,"domain_scores_codex":[0.9980112,0.0001667421,0.0002194977,0.000904009,0.000279137,0.000419435],"domain_scores_gemma":[0.9989281,0.0001145836,0.0000805509,0.0005370255,0.00001804963,0.0003216486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004604955,0.0008319938,0.7486656,0.00006435797,0.00004510189,0.0001124056,0.002668928,0.004063992,0.01638151,0.05810487,0.0008440398,0.1681711],"study_design_scores_gemma":[0.0003024028,0.0002279956,0.7757014,0.00003480432,0.00004298825,0.0000585523,0.0001031563,0.1935665,0.0000292198,0.02761134,0.001885497,0.0004361608],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7593985,0.00004498153,0.2397064,0.0001328367,0.0002217066,0.0002398754,0.00004093986,0.00003105463,0.0001836587],"genre_scores_gemma":[0.8585596,0.0000109196,0.1412351,0.0001236435,0.00001516805,0.000004764383,0.00003388321,0.000004144087,0.00001271756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1895025,"threshold_uncertainty_score":0.6029696,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2074120853","doi":"10.1016/j.cageo.2015.03.013","title":"Predictive lithological mapping of Canada's North using Random Forest classification applied to geophysical and geochemical data","year":2015,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":225,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Lithology; Geology; Geologic map; Random forest; Field (mathematics); Remote sensing; Geomorphology; Geochemistry; Machine learning; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.06963460871077687,"gpt":0.2477315266992174,"spread":0.1780969179884405,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005539879,0.0001603245,0.0002817386,0.00006129508,0.0001647427,0.0000960253,0.001786027,0.00005602791,6.741773e-7],"category_scores_gemma":[0.0002881715,0.0001336736,0.00002015506,0.0006096165,0.0002888821,0.0002602168,0.001610221,0.0001256726,7.294699e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005023642,"about_ca_system_score_gemma":0.0005056509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005781026,"about_ca_topic_score_gemma":0.002587274,"domain_scores_codex":[0.9980115,0.00005389519,0.000296025,0.0007830244,0.0004808213,0.0003746966],"domain_scores_gemma":[0.9985058,0.0001973839,0.0001512996,0.0006679696,0.0001638721,0.0003136228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000828973,0.001437071,0.5102785,0.0005680502,0.000294683,0.0002899391,0.02107205,0.1702859,0.0490907,0.0459083,0.04255828,0.1573876],"study_design_scores_gemma":[0.0004165969,0.00006466341,0.1064226,0.00002557267,0.000006587892,0.00002829689,0.0002848547,0.8881948,0.0002925337,0.001969172,0.002086826,0.0002075084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4854919,0.00002557353,0.5121741,0.001174811,0.0002171158,0.0001670391,0.00001088173,0.0000403202,0.0006982544],"genre_scores_gemma":[0.9481218,0.000001304897,0.05154876,0.0001977184,0.00008709125,0.000006364232,0.00001732206,0.000001477249,0.00001820392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7179089,"threshold_uncertainty_score":0.873922,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1989390169","doi":"10.1016/j.cageo.2006.05.013","title":"RockFall analyst: A GIS extension for three-dimensional and spatially distributed rockfall hazard modeling","year":2006,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":219,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Rockfall; Geographic information system; Polygon (computer graphics); Raster data; Raster graphics; Geology; Geospatial analysis; Hazard; Remote sensing; Computer science; Geotechnical engineering; Landslide; Computer graphics (images)","retraction":null,"screen_n_in":null,"score":{"opus":0.01317428055074842,"gpt":0.214964285220379,"spread":0.2017900046696306,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003291626,0.0001886857,0.0002181709,0.00006493854,0.0007080632,0.0000673363,0.0002818112,0.0000556095,0.00002138609],"category_scores_gemma":[0.00002198584,0.0001516971,0.0000680033,0.0002197899,0.0003524734,0.0002628237,0.0005129399,0.00006310567,0.00001729525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000373291,"about_ca_system_score_gemma":0.000009542161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001604984,"about_ca_topic_score_gemma":0.004695458,"domain_scores_codex":[0.9983952,0.00002706094,0.0002454042,0.0006235039,0.0002898226,0.0004190242],"domain_scores_gemma":[0.9995834,0.00007339389,0.00008213792,0.0001680101,0.00001877486,0.0000743012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003731995,0.00008033276,0.06725677,0.00001133284,0.00001598042,0.00001118979,0.00008470611,0.9224781,0.0005237067,0.0001394821,0.004561135,0.004799952],"study_design_scores_gemma":[0.0003155103,0.0001105077,0.07015435,0.00001441602,0.00003338585,0.000004557903,0.00001140019,0.9219925,0.00002318268,0.005468276,0.001674934,0.0001969708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.600745,0.00009983893,0.3974706,0.001098951,0.0001991772,0.0002126166,0.00001033319,0.00005059642,0.0001128784],"genre_scores_gemma":[0.9910641,0.000008914941,0.008282884,0.000429113,0.00005830888,0.00001923679,0.00003410065,0.000006427181,0.00009686663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3903191,"threshold_uncertainty_score":0.6186029,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2104992059","doi":"10.1016/j.cageo.2010.03.022","title":"Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study","year":2010,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":185,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Structural risk minimization; Support vector machine; Artificial neural network; Margin (machine learning); Curse of dimensionality; Empirical risk minimization; Computer science; Regression; Kernel method; Statistical learning theory; Mathematical optimization; Mathematics; Algorithm; Artificial intelligence; Machine learning; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.01956511950930807,"gpt":0.2735884071594239,"spread":0.2540232876501158,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004002987,0.0001530401,0.0002524651,0.0001954224,0.0001759703,0.00008538605,0.0003790233,0.00006542531,0.00002153613],"category_scores_gemma":[0.00002005169,0.0001181726,0.0000792881,0.0003554218,0.00008903712,0.0001743423,0.00006381775,0.000219317,0.000008257444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003474242,"about_ca_system_score_gemma":0.00002669647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001734526,"about_ca_topic_score_gemma":0.001849926,"domain_scores_codex":[0.9988336,0.00003804659,0.0002510048,0.0003263725,0.0002524666,0.0002985188],"domain_scores_gemma":[0.999459,0.00009445602,0.00004257945,0.0002584263,0.00004165932,0.0001039059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000064958,0.000633872,0.07512788,0.00007275914,0.0001046145,0.0000541089,0.01362955,0.8946581,0.007208619,0.000006585053,0.004820413,0.003618479],"study_design_scores_gemma":[0.000618943,0.0004230749,0.07090703,0.00003435668,0.00002244499,0.00001229422,0.0005161166,0.921235,0.003422359,0.00005273121,0.00252217,0.0002334448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920184,0.00003369205,0.005900871,0.00007266651,0.001269206,0.0003696741,0.00001088013,0.0001703073,0.0001542857],"genre_scores_gemma":[0.9984293,0.00000371339,0.001265081,0.00001567425,0.0001204844,0.00004938784,0.00001129131,0.000008071877,0.00009698443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02657687,"threshold_uncertainty_score":0.4818938,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2022970735","doi":"10.1016/j.cageo.2008.12.005","title":"Robust factor analysis for compositional data","year":2009,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":165,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Compositional data; Biplot; Computer science; Raw data; Transformation (genetics); Covariance; Factor (programming language); Data mining; Covariance matrix; Principal component analysis; Data transformation; Dimension (graph theory); Dimensionality reduction; Algorithm; Artificial intelligence; Mathematics; Statistics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.0864450217676071,"gpt":0.2762325451152575,"spread":0.1897875233476504,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003605667,0.0001445313,0.0002164358,0.000150324,0.0003812769,0.0003522368,0.003667335,0.00004705012,0.00001908834],"category_scores_gemma":[0.00005706077,0.0001247122,0.000108122,0.001146315,0.0001107455,0.000692263,0.0004593075,0.00007189759,0.000006677855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001521596,"about_ca_system_score_gemma":0.00006912315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001185976,"about_ca_topic_score_gemma":0.000004790571,"domain_scores_codex":[0.9982522,0.00003022502,0.0002207567,0.0008131846,0.0003129728,0.0003707062],"domain_scores_gemma":[0.9985166,0.0001895322,0.0001122791,0.0009382208,0.0001227852,0.0001206324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005055331,0.001349075,0.03225305,0.00009988253,0.0008700891,0.00008548552,0.002640795,0.2752003,0.008170461,0.1975753,0.07240385,0.4093011],"study_design_scores_gemma":[0.0001456776,0.00009145393,0.04513637,0.000007551548,0.00002625845,0.000009601733,0.00001458279,0.9326558,0.0002434271,0.005629965,0.01582286,0.0002164581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005454677,0.00006667651,0.9840258,0.008725743,0.0003852457,0.0001176901,0.00004169529,0.0001393498,0.001043059],"genre_scores_gemma":[0.745946,0.00000334576,0.2525874,0.001015259,0.0001082879,0.000003267034,0.0001025768,6.98546e-7,0.0002331334],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7404913,"threshold_uncertainty_score":0.6814882,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2041377425","doi":"10.1016/j.cageo.2009.05.011","title":"Using the fuzzy majority approach for GIS-based multicriteria group decision-making","year":2009,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":136,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Group decision-making; Operationalization; Decision maker; Fuzzy logic; Group (periodic table); Computer science; Data mining; Operations research; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1901081355967842,"gpt":0.4395995889815037,"spread":0.2494914533847195,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.009207637,0.0004716994,0.0006864769,0.0007524439,0.001913234,0.003185953,0.005045492,0.000162487,0.00006071304],"category_scores_gemma":[0.004618745,0.0002894908,0.0004580717,0.00251694,0.0006715941,0.001042105,0.0004807658,0.0002702308,0.00002449728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001174549,"about_ca_system_score_gemma":0.0001810628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003524375,"about_ca_topic_score_gemma":0.00001542417,"domain_scores_codex":[0.9921975,0.0005013728,0.001447278,0.001789236,0.003053663,0.001011019],"domain_scores_gemma":[0.9883765,0.0084173,0.0006708344,0.001673092,0.000613023,0.0002492655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002719788,0.000406341,0.002091408,0.00001170203,0.00001318615,0.000019833,0.001281615,0.04397622,0.003761437,0.003159945,0.005297401,0.9397089],"study_design_scores_gemma":[0.0006903065,0.0001687893,0.01038309,0.00009977633,0.00001855977,0.00002977555,0.0006503368,0.9407441,0.00006344078,0.0413238,0.005403518,0.0004244723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1386886,0.0002108298,0.8562835,0.0005335141,0.002967254,0.0008488456,0.00002896517,0.0001140995,0.0003244129],"genre_scores_gemma":[0.5295947,0.000001057557,0.4686649,0.001446058,0.0002504511,0.00001218787,0.000002468052,0.00001090602,0.00001733924],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9392844,"threshold_uncertainty_score":0.9999557,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2014093012","doi":"10.1016/j.cageo.2004.06.014","title":"A distributed geospatial infrastructure for Sensor Web","year":2004,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"York University; National Aeronautics and Space Administration","keywords":"Sensor web; Geospatial analysis; Computer science; Wireless sensor network; Default gateway; Web service; Distributed computing; Key distribution in wireless sensor networks; Wireless; Remote sensing; Real-time computing; Computer network; Wireless network; Telecommunications; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.007547716406691307,"gpt":0.21951068851274,"spread":0.2119629721060487,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002995468,0.0003029327,0.0003012737,0.0001841492,0.0005353635,0.0005099083,0.002263581,0.0001265697,0.000003843282],"category_scores_gemma":[0.00006649994,0.0002628632,0.0001592978,0.001150712,0.0003061574,0.0005428402,0.0004495786,0.0001796349,0.00001424401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001215236,"about_ca_system_score_gemma":0.000285075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005371619,"about_ca_topic_score_gemma":0.00004678324,"domain_scores_codex":[0.9972869,0.00005038329,0.0003616067,0.0009348939,0.0005462302,0.0008199932],"domain_scores_gemma":[0.998508,0.0002118514,0.0001933445,0.00067335,0.0001713886,0.0002421115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009368513,0.0000858469,0.0004503051,0.00001284852,0.00001612761,0.00002321407,0.000618966,0.932337,0.0006730277,0.04152831,0.00192647,0.02231851],"study_design_scores_gemma":[0.001255579,0.0002966796,0.004418967,0.00006203123,0.000009672144,0.00006199184,0.00005924369,0.9709896,0.0009067391,0.004894065,0.01649421,0.0005511681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1445072,0.00007381375,0.8502437,0.001618078,0.002746694,0.0002833655,0.00002999571,0.0004116097,0.0000855227],"genre_scores_gemma":[0.684275,0.00001084006,0.3147466,0.0006015247,0.0002784366,0.00002165438,0.00002338644,0.00001138116,0.00003110747],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5397679,"threshold_uncertainty_score":0.9999824,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4256206706","doi":"10.1016/j.cageo.2008.09.012","title":"ALLUVSIM: A program for event-based stochastic modeling of fluvial depositional systems","year":2009,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geological formations and processes","field":"Earth and Planetary Sciences","cited_by":125,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; Canadian Natural Resources","funders":"","keywords":"Fluvial; Event (particle physics); Scale (ratio); Computer science; Crevasse; Geology; Algorithm; Process (computing); Data mining; Geomorphology; Cartography; Structural basin; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.02173747538469022,"gpt":0.2467404556722374,"spread":0.2250029802875472,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002407159,0.00009082478,0.0001376415,0.00007914361,0.0002490295,0.0001005084,0.0002754166,0.00003621658,0.00002748164],"category_scores_gemma":[0.00002872162,0.00006553371,0.0000577085,0.0002562253,0.00009311509,0.0002106441,0.00000470277,0.00004092032,0.000005621723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001997187,"about_ca_system_score_gemma":0.00008751233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001974117,"about_ca_topic_score_gemma":0.00004803504,"domain_scores_codex":[0.999064,0.00002342638,0.0002427949,0.0001855706,0.000248516,0.0002357515],"domain_scores_gemma":[0.9995267,0.0001342164,0.00008534036,0.0000711332,0.0001030578,0.00007952105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001775067,0.00003900984,0.0008665919,0.00002198142,0.000002241943,3.5637e-7,0.00004005378,0.9681334,0.000004829027,0.0002796107,0.00003116099,0.03056295],"study_design_scores_gemma":[0.0001619748,0.000782392,0.005805732,0.00003796017,0.000006934599,0.000005200212,0.00005498189,0.9914323,0.000006310198,0.001529102,0.00008474186,0.00009236419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2987429,0.0003435657,0.6997159,0.0002224088,0.0003798302,0.00036449,0.00003935518,0.00005275819,0.0001388458],"genre_scores_gemma":[0.9803599,0.000001700425,0.01935855,0.0001255855,0.00006242391,0.000005742091,0.00007937592,6.428341e-7,0.000006130823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.681617,"threshold_uncertainty_score":0.2672387,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1981537987","doi":"10.1016/j.cageo.2004.05.002","title":"Segmentation of petrographic images by integrating edge detection and region growing","year":2004,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Brock University","keywords":"Petrography; Pixel; Edge detection; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Computer vision; Computer science; Noise (video); Image gradient; Segmentation; Image segmentation; Canny edge detector; Range segmentation; Image (mathematics); Pattern recognition (psychology); Geology; Image processing; Image texture; Mineralogy","retraction":null,"screen_n_in":null,"score":{"opus":0.006974944809332075,"gpt":0.2259577024423952,"spread":0.2189827576330631,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002847516,0.0001169961,0.0001254804,0.0002836981,0.0002689652,0.0002212395,0.0003914531,0.00003837193,3.358424e-7],"category_scores_gemma":[0.00002422193,0.0001043309,0.00004997131,0.0007930327,0.0002074589,0.001661508,0.0001260599,0.00009562475,6.466072e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003770949,"about_ca_system_score_gemma":0.00002741174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003499703,"about_ca_topic_score_gemma":0.00001442042,"domain_scores_codex":[0.998966,0.00004800866,0.000216819,0.0003563067,0.0002250288,0.0001878519],"domain_scores_gemma":[0.9994985,0.00004821685,0.0001533439,0.0001750114,0.00007403163,0.00005093006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003133428,0.00003934938,0.0004567771,0.00002471073,0.000006411355,0.000005441151,0.001026672,0.00003164257,0.199966,0.0009087868,0.00008715061,0.7974439],"study_design_scores_gemma":[0.0002285293,0.0004037114,0.001555153,0.00007370303,0.000005069209,0.00006883284,0.0003597781,0.005076456,0.9875146,0.004422084,0.0001189121,0.0001731289],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1505782,0.0002390747,0.8482786,0.0001961142,0.0003245527,0.0001009325,4.206021e-7,0.0002004666,0.00008157824],"genre_scores_gemma":[0.9210259,0.00006809308,0.07872022,0.0001397769,0.00002511167,0.000008756414,5.94377e-7,0.000003538563,0.000007977179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7972708,"threshold_uncertainty_score":0.425449,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2091719480","doi":"10.1016/j.cageo.2010.03.021","title":"Programs for kriging and sequential Gaussian simulation with locally varying anisotropy using non-Euclidean distances","year":2010,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":91,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"","keywords":"Variogram; Kriging; Covariance; Gaussian; Nonlinear system; Metric (unit); Algorithm; Geostatistics; Anisotropy; Covariance function; Computer science; Mathematics; Mathematical optimization; Covariance matrix; Statistics; Spatial variability; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01743460569970156,"gpt":0.2650339285366871,"spread":0.2475993228369855,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002268354,0.0001318071,0.0001179778,0.0000353565,0.00053746,0.0002853241,0.0002013961,0.00003119907,0.00001508335],"category_scores_gemma":[0.00001352955,0.000104556,0.00002216785,0.0002166481,0.0005309155,0.0003642612,0.0001096277,0.00007839571,0.000001938643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002261254,"about_ca_system_score_gemma":0.00002510891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005036524,"about_ca_topic_score_gemma":0.0003785757,"domain_scores_codex":[0.9988575,0.00001118823,0.0001492717,0.0004056667,0.0002467588,0.0003296112],"domain_scores_gemma":[0.9995695,0.00007920618,0.0001077097,0.0001160115,0.00001498191,0.000112571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008738473,0.0001241667,0.2300893,0.00008239397,0.00002472846,0.00002599628,0.002731548,0.2212004,0.02983113,0.002025528,0.00006931477,0.5137081],"study_design_scores_gemma":[0.0002846211,0.0001282426,0.01094081,0.00003961563,0.00001333882,0.000009602473,0.0001312719,0.9854344,0.0001274603,0.001126571,0.001565647,0.0001984398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4678919,0.000004626333,0.5313951,0.00003513619,0.0002708605,0.0001996619,0.000002176079,0.00001935664,0.0001811693],"genre_scores_gemma":[0.8000212,0.000001334387,0.19983,0.00005481659,0.00006145318,0.000006232044,0.000004509717,0.000006718756,0.00001365832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7642339,"threshold_uncertainty_score":0.426367,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4302009178","doi":"10.1016/j.cageo.2022.105241","title":"A critical review of discontinuity plane extraction from 3D point cloud data of rock mass surfaces","year":2022,"lang":"en","type":"review","venue":"Computers & Geosciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":90,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Point cloud; Discontinuity (linguistics); Computer science; Photogrammetry; Ground truth; Segmentation; Rock mass classification; Orientation (vector space); Geology; Artificial intelligence; Data mining; Remote sensing; Geometry; Mathematics; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.07408391623669114,"gpt":0.3426841909754182,"spread":0.2686002747387271,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009786504,0.0002478977,0.001050594,0.00004902422,0.0001706441,0.00003921699,0.001830641,0.00007401279,0.001062332],"category_scores_gemma":[0.0003427851,0.0001954498,0.0001772044,0.0005859546,0.0005752716,0.0002681446,0.001019556,0.000274586,0.00004850599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007926111,"about_ca_system_score_gemma":0.00009020552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001689819,"about_ca_topic_score_gemma":0.00004196131,"domain_scores_codex":[0.9971061,0.0003320957,0.0007893274,0.0008294542,0.0007009411,0.0002420695],"domain_scores_gemma":[0.9967271,0.001363621,0.0006211192,0.001192981,0.00001201427,0.00008313353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001191642,0.00008802124,0.00001282442,0.005340928,0.00001814086,0.000002688328,0.00006952576,0.00004833491,0.00001233987,0.00002220316,0.006001798,0.988382],"study_design_scores_gemma":[0.00002387895,0.00004172976,0.00006506439,0.01252282,0.0002496888,0.00001518758,0.00004518572,0.003015912,0.000002563232,0.0001061384,0.9836913,0.0002205564],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005292198,0.9857843,0.01116814,0.0001447422,0.001010074,0.0004248702,0.0006606914,0.00002724262,0.0007270037],"genre_scores_gemma":[0.0001203728,0.9852392,0.01406885,0.00004451744,0.00007288702,0.000003937099,0.0003955395,0.00001099484,0.00004371716],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9881614,"threshold_uncertainty_score":0.9998508,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1996344134","doi":"10.1016/j.cageo.2006.12.008","title":"Support vector machine for 3D modelling from sparse geological information of various origins","year":2007,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":85,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Support vector machine; Computer science; Data mining; Visualization; Task (project management); Hyperparameter; Machine learning; Artificial intelligence; Pattern recognition (psychology); Systems engineering; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02709064366674621,"gpt":0.2234299623528251,"spread":0.1963393186860789,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007261994,0.0001223934,0.0002333713,0.0001286712,0.00018522,0.00005866,0.0003836427,0.00007467449,0.0003003512],"category_scores_gemma":[0.00004287576,0.00008432447,0.00009738555,0.0003033893,0.0001570295,0.0003205782,0.00002160675,0.00008565352,0.00005093353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004542865,"about_ca_system_score_gemma":0.00003979059,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008811711,"about_ca_topic_score_gemma":0.0009639438,"domain_scores_codex":[0.9986972,0.00002278136,0.0003897437,0.0002448912,0.0002954224,0.0003499877],"domain_scores_gemma":[0.9991794,0.0003183199,0.0001601417,0.0001322019,0.00008031774,0.0001295769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000425115,0.00002615735,0.1029821,0.000007818119,0.00001447384,0.000003231845,0.0003098148,0.8011215,0.000005390474,0.0002467775,0.00007214637,0.09516809],"study_design_scores_gemma":[0.0001511906,0.000233339,0.03591458,0.000006219761,0.00001924571,0.000002172536,0.00005147598,0.9570981,0.00001927293,0.001797282,0.004588165,0.0001189664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3807686,0.00007124207,0.6182174,0.000102742,0.000347302,0.00007093839,0.00007913011,0.00003067568,0.0003119381],"genre_scores_gemma":[0.8726081,0.00001990335,0.1267724,0.0002586014,0.00008073846,4.565366e-7,0.0002423622,7.458813e-7,0.00001671367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4918395,"threshold_uncertainty_score":0.9977887,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2025234276","doi":"10.1016/j.cageo.2009.02.003","title":"Development of a pit filling algorithm for LiDAR canopy height models","year":2009,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":83,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal; University of Calgary","funders":"","keywords":"Lidar; Computer science; Algorithm; Limit (mathematics); Tree (set theory); Graphical user interface; Pixel; Crown (dentistry); Canopy; Visualization; Remote sensing; Computer graphics (images); Computer vision; Data mining; Geology; Mathematics; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.01920025745063972,"gpt":0.2382572589365679,"spread":0.2190570014859281,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002249996,0.00009699024,0.0001236667,0.00003869272,0.0002950354,0.00003621419,0.0003390779,0.00002944914,0.00001221391],"category_scores_gemma":[0.000003936173,0.00008259931,0.00004455705,0.0002762522,0.000155329,0.0001398259,0.00005458764,0.00003761127,0.00001337279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005096056,"about_ca_system_score_gemma":0.00004211663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007026179,"about_ca_topic_score_gemma":0.00001846402,"domain_scores_codex":[0.9989702,0.00000990322,0.0002185554,0.0003071849,0.0002521948,0.0002419259],"domain_scores_gemma":[0.9996142,0.00003801176,0.00008704648,0.0001644557,0.00001410221,0.0000821684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000134103,0.00005066243,0.00003359068,0.000001788183,0.000002658324,3.38463e-7,0.001873336,0.005684892,0.003965782,0.0001790623,0.0005358385,0.9876707],"study_design_scores_gemma":[0.0001530207,0.00007740931,0.003077219,0.00002473806,0.000005739039,0.000004355863,0.0001381049,0.956172,0.008644578,0.004210366,0.02729184,0.0002006018],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1475469,0.00002783715,0.8501456,0.0002230769,0.000129347,0.0001801099,0.000003350787,0.00003312694,0.001710621],"genre_scores_gemma":[0.3101827,0.000003776061,0.6894912,0.0001592547,0.00002423803,0.000002033841,0.000003501413,0.000002828284,0.0001305238],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9874701,"threshold_uncertainty_score":0.3368302,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2043222924","doi":"10.1016/j.cageo.2006.02.005","title":"GIS modeling for predicting river runoff volume in ungauged drainages in the Greater Toronto Area, Canada","year":2006,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Surface runoff; Precipitation; Hydrology (agriculture); Drainage basin; Structural basin; Runoff curve number; Environmental science; Streamflow; Drainage; Geology; Meteorology; Geomorphology; Geography; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.009737135307760995,"gpt":0.1943679189953197,"spread":0.1846307836875587,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005591408,0.0001231126,0.000128853,0.00002870532,0.0003267171,0.00004100059,0.0004935782,0.000028426,0.00002846771],"category_scores_gemma":[0.00001270925,0.0000860666,0.00002669076,0.0001646434,0.000261534,0.0002951591,0.0001968731,0.0000626952,0.000002545316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000214174,"about_ca_system_score_gemma":0.0000146178,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7822645,"about_ca_topic_score_gemma":0.8849916,"domain_scores_codex":[0.9987463,0.00005731216,0.0001939577,0.000353871,0.0002333403,0.0004151715],"domain_scores_gemma":[0.9997241,0.00007075819,0.00004191404,0.0001378372,0.000004054426,0.00002133637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000964985,0.00003650987,0.6843591,0.000007093719,0.000004767008,0.00002193893,0.003819173,0.3040302,0.00002164117,0.00007928496,0.00619363,0.001417034],"study_design_scores_gemma":[0.0002418666,0.00003955513,0.3405485,0.00001343831,0.000006095555,0.000001293065,0.0009175304,0.654907,0.000004652617,0.001154072,0.002031283,0.0001347022],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988042,0.00006619246,0.006908141,0.00176965,0.0002834632,0.000314281,0.000003872317,0.00001668151,0.002595742],"genre_scores_gemma":[0.9980313,0.000004950444,0.000944558,0.0006137963,0.00003514725,0.00004093064,0.000003655459,0.000003345161,0.0003223777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3508769,"threshold_uncertainty_score":0.3509694,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2079679675","doi":"10.1016/j.cageo.2008.02.007","title":"Sedtrans05: An improved sediment-transport model for continental shelves and coastal waters with a new algorithm for cohesive sediments","year":2008,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Coastal and Marine Dynamics","field":"Earth and Planetary Sciences","cited_by":75,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Geological Survey of Canada; Bedford Institute of Oceanography","funders":"","keywords":"Sediment transport; Bed load; Settling; Flume; Geology; Sediment; Geotechnical engineering; Soil science; Deposition (geology); Seabed; Erosion; Algorithm; Geomorphology; Flow (mathematics); Environmental science; Mechanics; Computer science; Oceanography","retraction":null,"screen_n_in":null,"score":{"opus":0.01236413179688001,"gpt":0.1999811513908443,"spread":0.1876170195939643,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001458776,0.0002389593,0.0002525139,0.00007852064,0.00044042,0.00007028787,0.0002804666,0.0000474052,0.000009017378],"category_scores_gemma":[0.000002203868,0.0001755581,0.00006991879,0.0001216953,0.0003941508,0.0004732745,0.00001977115,0.0000620636,7.470643e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003873906,"about_ca_system_score_gemma":0.0001876399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003272907,"about_ca_topic_score_gemma":0.004702409,"domain_scores_codex":[0.9985004,0.00001025486,0.0002121505,0.0005437136,0.000240048,0.0004934319],"domain_scores_gemma":[0.999339,0.00007322001,0.00007804512,0.0001140324,0.00006138468,0.0003343486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009294766,0.0002237828,0.3452393,0.0001089941,0.0001347749,0.00003045988,0.007445143,0.02502835,0.0006684325,0.00004580491,0.000481737,0.6196637],"study_design_scores_gemma":[0.00167659,0.001213477,0.0469705,0.00001378462,0.00003153202,0.00005091107,0.0002892146,0.9488191,0.00005830145,0.0002075982,0.0003809353,0.0002880359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5552871,0.00002379878,0.4430224,0.0001624587,0.0002775264,0.0006136824,0.0005421478,0.00004103387,0.00002988327],"genre_scores_gemma":[0.8343885,0.00002846779,0.1636637,0.0003788022,0.0001146483,0.00001090599,0.0005564434,0.000008237454,0.000850299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9237908,"threshold_uncertainty_score":0.7159051,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3183521896","doi":"10.1016/j.cageo.2021.104895","title":"Characterization of pore and grain size distributions in porous geological samples – An image processing workflow","year":2021,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Porosity; Characterization (materials science); Grain size; Mineralogy; Scanning electron microscope; Geology; Carbonate; Materials science; Porous medium; Composite material; Nanotechnology; Geomorphology; Geotechnical engineering; Metallurgy","retraction":null,"screen_n_in":null,"score":{"opus":0.01090270756775489,"gpt":0.2347414260130488,"spread":0.2238387184452939,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001408361,0.00008891043,0.000145865,0.00005056966,0.00005390562,0.00007237236,0.0001316086,0.00004778954,0.000006822822],"category_scores_gemma":[0.00007136385,0.0000850798,0.0000159219,0.0004188,0.0001667099,0.0003994905,0.00005271479,0.00007432923,1.87226e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002294408,"about_ca_system_score_gemma":0.00002960247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001399301,"about_ca_topic_score_gemma":0.00004498403,"domain_scores_codex":[0.9993128,0.00003016471,0.0001794106,0.0002030668,0.0001017754,0.0001727779],"domain_scores_gemma":[0.9997221,0.0000545888,0.00003871342,0.00009588388,0.00004577102,0.000042983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000004797264,0.0001068631,0.02039138,0.0001279869,0.000004805851,0.00005330158,0.0007933287,0.0004920696,0.6573267,0.0005552514,0.00002969319,0.3201138],"study_design_scores_gemma":[0.0002088285,0.0001209828,0.7681098,0.0003240693,0.000008033495,0.0000606494,0.0001892402,0.1008717,0.1228941,0.006433772,0.000381875,0.0003970012],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7942455,0.0001038586,0.2052344,0.00005521566,0.0001032311,0.00004932414,0.00001978513,0.0001290365,0.00005965955],"genre_scores_gemma":[0.9339117,0.00009956661,0.06587018,0.00002088982,0.00001796832,0.000007012674,0.00006056873,0.000004727066,0.000007350465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7477184,"threshold_uncertainty_score":0.3469453,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2208553489","doi":"10.1016/j.cageo.2015.11.007","title":"Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration","year":2015,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"China University of Geosciences, Wuhan; National Natural Science Foundation of China","keywords":"Fractal; Wavelet; Scaling; Singularity; Wavelet transform; Geology; Kernel (algebra); Scale (ratio); Transformation (genetics); Fractal analysis; Mineral exploration; Pattern recognition (psychology); Mathematics; Computer science; Artificial intelligence; Geophysics; Fractal dimension; Mathematical analysis; Physics; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.0688514340391407,"gpt":0.2851054961229039,"spread":0.2162540620837632,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001169783,0.0001516547,0.0002983461,0.0003274254,0.00009357246,0.0001356583,0.0008451114,0.00007945261,0.000001960352],"category_scores_gemma":[0.0002331644,0.0001368497,0.0001583499,0.001391469,0.0001400667,0.0005736643,0.00007513753,0.0001014651,7.164607e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000452676,"about_ca_system_score_gemma":0.0001401365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001596267,"about_ca_topic_score_gemma":0.0001049973,"domain_scores_codex":[0.9982395,0.00006210354,0.0003575173,0.0005185961,0.0005014829,0.0003208113],"domain_scores_gemma":[0.9990275,0.0001790829,0.000145444,0.0003164381,0.0002172082,0.0001143402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005224112,0.002863403,0.1214968,0.0005345042,0.0003565326,0.0001158946,0.02448687,0.596552,0.04421154,0.0174193,0.003006905,0.1884338],"study_design_scores_gemma":[0.0004842271,0.0001524137,0.005458585,0.00003146673,0.0000214383,0.00000170003,0.0001471323,0.9759198,0.0102058,0.006935727,0.0004594615,0.0001821854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1945458,0.00002208369,0.8026489,0.001902914,0.0002243991,0.0001446209,0.000003815217,0.00003837032,0.0004691122],"genre_scores_gemma":[0.9233431,6.531264e-7,0.07642862,0.0001341397,0.00003649925,0.00001507226,0.00001756265,0.000001305337,0.00002299625],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7287974,"threshold_uncertainty_score":0.558057,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2102282203","doi":"10.1016/s0098-3004(01)00097-8","title":"A GIS method for reconstruction of late Quaternary landscapes from isobase data and modern topography","year":2002,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":71,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"Quaternary; Landform; Bathymetry; Geology; Geographic information system; Shore; Digital elevation model; Georeference; Remote sensing; Physical geography; Geomorphology; Geography; Paleontology","retraction":null,"screen_n_in":null,"score":{"opus":0.04682453235562861,"gpt":0.2731449333712587,"spread":0.2263204010156301,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004531882,0.00008898411,0.0001863171,0.000128127,0.0001920541,0.00003794592,0.0005106142,0.00006520416,0.0002493954],"category_scores_gemma":[0.00003151194,0.00006970938,0.00003232148,0.0001767007,0.0003361026,0.0002926509,0.00005490556,0.00007834168,0.000009280372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":2.182203e-7,"about_ca_system_score_gemma":0.00001200727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001057691,"about_ca_topic_score_gemma":0.001958271,"domain_scores_codex":[0.998944,0.0001175679,0.0001706601,0.0004108441,0.0001222428,0.0002346547],"domain_scores_gemma":[0.9989303,0.0006380157,0.00008100393,0.0002430613,0.00002780705,0.00007982178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001791811,0.000008359661,0.8953416,0.00001127199,0.00001580637,0.000002853788,0.0002666753,0.0001377693,0.00001102193,0.00001130683,0.0001278474,0.1040476],"study_design_scores_gemma":[0.0002003744,0.0001050565,0.2056214,0.00001348066,0.00001240532,0.00003157956,0.00006420242,0.7879756,0.00001959638,0.005603034,0.0002760812,0.00007715535],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9631274,0.002071423,0.03302777,0.000655276,0.0004415879,0.000124816,0.0002816583,0.00002012469,0.0002499134],"genre_scores_gemma":[0.911507,0.0002837089,0.08794548,0.00009095034,0.00004062206,0.00000101708,0.00009376725,0.000001095584,0.00003641277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7878379,"threshold_uncertainty_score":0.2842666,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2016606626","doi":"10.1016/j.cageo.2011.04.013","title":"Building a geodatabase for mapping hydrogeological features and 3D modeling of groundwater systems: Application to the Saguenay–Lac-St.-Jean region, Canada","year":2011,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":71,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Université du Québec à Chicoutimi","funders":"Ministère de l'Écologie, du Développement Durable et de l'Énergie","keywords":"Spatial database; Hydrogeology; Database; Geographic information system; Relational database management system; Groundwater; Computer science; Spatial analysis; Data mining; Relational database; Geology; Remote sensing","retraction":null,"screen_n_in":null,"score":{"opus":0.03586247767109054,"gpt":0.2069905021834265,"spread":0.171128024512336,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005797268,0.000136213,0.0002181266,0.00008218586,0.0004043129,0.00006947133,0.000497575,0.00004366448,0.000006904138],"category_scores_gemma":[0.00006285101,0.00007783377,0.00004662721,0.0003056596,0.000120118,0.0001173021,0.00005702503,0.00007902928,0.000001547766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005374479,"about_ca_system_score_gemma":0.00004475906,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7130719,"about_ca_topic_score_gemma":0.2929452,"domain_scores_codex":[0.9986268,0.00006865225,0.0002755651,0.0004470766,0.000243661,0.0003382067],"domain_scores_gemma":[0.9992919,0.000174451,0.0001058389,0.0002118201,0.00007828334,0.0001377313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002389663,0.00001669235,0.05753097,0.00004338504,0.00002336155,0.000004910852,0.0004833256,0.9103286,0.00001110039,0.0004058459,0.0005891277,0.03053874],"study_design_scores_gemma":[0.00005961852,0.00007991731,0.008711165,0.00003235955,0.00001739394,0.00001672874,0.000377039,0.9887983,0.000004899007,0.0004577425,0.001318971,0.0001259312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4290566,0.0005088628,0.5691077,0.0008639397,0.0001873537,0.0002101366,0.00001361667,0.00001900139,0.0000327479],"genre_scores_gemma":[0.9851806,0.0000226359,0.01428541,0.0003740553,0.00008148619,0.000008402167,0.00002243992,0.000001617419,0.00002334133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.556124,"threshold_uncertainty_score":0.7199567,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1998071914","doi":"10.1016/j.cageo.2010.02.001","title":"Estimating rock mass properties using Monte Carlo simulation: Ankara andesites","year":2010,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":67,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Waterloo","keywords":"Andesites; Rock mass classification; Geological Strength Index; Monte Carlo method; Discontinuity (linguistics); Geology; Rock mass rating; Parametric statistics; Geotechnical engineering; Statistics; Andesite; Mathematics; Seismology; Volcanic rock; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.02646706950568815,"gpt":0.2446063850702627,"spread":0.2181393155645746,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002214641,0.0001377721,0.0001307181,0.0000426792,0.0006456085,0.0002176769,0.0003268754,0.00003444352,0.00008889753],"category_scores_gemma":[0.00004079512,0.0001053519,0.00003812254,0.0002546111,0.0002514504,0.0006397956,0.0002118212,0.0000968438,0.00005450273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003526762,"about_ca_system_score_gemma":0.00001206323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004642716,"about_ca_topic_score_gemma":0.0001220388,"domain_scores_codex":[0.998819,0.00002811301,0.0001831108,0.0003442039,0.0003547973,0.0002707932],"domain_scores_gemma":[0.9996121,0.00006631244,0.00008096189,0.0001596312,0.00002440884,0.00005658718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003987858,0.00003447291,0.1443262,0.00001027696,0.000008381207,0.000005969619,0.003242884,0.7751822,0.03429922,0.00001729963,0.00007131632,0.04279784],"study_design_scores_gemma":[0.00007569638,0.00002818989,0.04018572,0.0000156059,0.000006796311,0.000005154207,0.0002031836,0.95793,0.0005722569,0.00004428524,0.0007744396,0.0001586482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8092734,0.00002009832,0.1895286,0.00009747498,0.0007984571,0.0001039083,8.42329e-7,0.00005672322,0.0001205746],"genre_scores_gemma":[0.961737,5.000304e-7,0.03758005,0.0001277599,0.00007412251,0.000005546643,2.880935e-7,0.00000511256,0.0004696191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1827478,"threshold_uncertainty_score":0.4965564,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2145476446","doi":"10.1016/j.cageo.2010.04.010","title":"On the simulation of continuous in scale universal multifractals, part I: Spatially continuous processes","year":2010,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":63,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Université du Québec à Montréal","funders":"","keywords":"Multifractal system; Statistical physics; Scale (ratio); Turbulence; Scaling; Physics; Mathematics; Meteorology; Mathematical analysis; Geometry; Fractal","retraction":null,"screen_n_in":null,"score":{"opus":0.01736814982640998,"gpt":0.209620850218229,"spread":0.1922527003918191,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000522383,0.0001094735,0.0003350076,0.0001889299,0.0001250842,0.00009485318,0.0004059169,0.00004721625,0.000210001],"category_scores_gemma":[0.0002001794,0.00008887734,0.00007256774,0.0005773377,0.0002165248,0.0001874323,0.0000640464,0.0001160517,0.00003490579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001493567,"about_ca_system_score_gemma":0.00003482935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002541546,"about_ca_topic_score_gemma":0.002552769,"domain_scores_codex":[0.9989335,0.00001875774,0.0004573556,0.0003182502,0.00007410398,0.000198049],"domain_scores_gemma":[0.998866,0.0003638895,0.0004046042,0.0002510305,0.00007452683,0.00003996973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001131256,0.0009436034,0.6186809,0.0001496873,0.0001208681,0.00001674603,0.007646904,0.1507275,0.0004386267,0.1858933,0.001462216,0.03380663],"study_design_scores_gemma":[0.0008478652,0.0003700414,0.08303081,0.0001076789,0.00001208762,0.000002939587,0.001030102,0.8396042,0.0001946522,0.01056334,0.06371757,0.000518688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894356,0.0001053202,0.0061041,0.0004262431,0.0006619025,0.00019493,0.0000318601,0.00001758588,0.003022455],"genre_scores_gemma":[0.9992106,0.000009393309,0.0003075922,0.00007200069,0.00006265996,0.000004414823,0.000003703455,0.000005458684,0.0003241762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6888767,"threshold_uncertainty_score":0.3842074,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2000725901","doi":"10.1016/j.cageo.2004.12.005","title":"The application of geography markup language (GML) to the geological sciences","year":2005,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":60,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Galdos Systems (Canada)","funders":"Government of the United Kingdom","keywords":"Geospatial analysis; XML; Computer science; Markup language; Geologic map; Geographic information system; Thematic map; Schema (genetic algorithms); Information retrieval; Earth science; Data science; Geology; World Wide Web; Geography; Remote sensing; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.01367931033160369,"gpt":0.2937899483222731,"spread":0.2801106379906694,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.003937338,0.0001013021,0.0001336127,0.0001475669,0.003678922,0.0002389274,0.001586203,0.00004031821,0.000009193188],"category_scores_gemma":[0.000177377,0.00005203445,0.00009188778,0.001655005,0.003092258,0.0002811348,0.0002268771,0.00007807189,0.00005544339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002008085,"about_ca_system_score_gemma":0.00007419334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002923286,"about_ca_topic_score_gemma":0.004146423,"domain_scores_codex":[0.9979435,0.0001708325,0.0003359213,0.0002378855,0.0008769708,0.0004348989],"domain_scores_gemma":[0.9986923,0.0006090982,0.000210504,0.0002499571,0.0001586882,0.00007945847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001304649,0.00007061279,0.1649516,0.00001163654,0.00003684665,5.064022e-7,0.09069709,0.002723208,0.00004771523,0.2914203,0.007339612,0.4426878],"study_design_scores_gemma":[0.0001066094,0.0001153263,0.1815748,0.00001913328,0.000009437731,0.00000252174,0.05511314,0.004333559,0.00003112655,0.001201056,0.7572888,0.000204514],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8408613,0.002242308,0.03262018,0.06670899,0.001866586,0.00168049,0.000009795321,0.0002212195,0.05378916],"genre_scores_gemma":[0.9963198,0.00009288825,0.002272377,0.0008459016,0.0002481065,0.00005796677,5.184997e-7,0.000001689535,0.0001607904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7499492,"threshold_uncertainty_score":0.9996207,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2030157763","doi":"10.1016/j.cageo.2009.10.008","title":"Array processing of teleseismic body waves with the USArray","year":2010,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"CMG Reservoir Simulation Foundation; National Science Foundation","keywords":"Computer science; Residual; Algorithm; Stack (abstract data type); Amplitude; Stacking; Data processing; Geology; Aperture (computer memory); Function (biology); Normal moveout; SIGNAL (programming language); Seismology; Data mining; Acoustics; Database","retraction":null,"screen_n_in":null,"score":{"opus":0.00811069684945011,"gpt":0.2187415434442359,"spread":0.2106308465947857,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005113075,0.0001552907,0.0001954181,0.00009776803,0.0006432264,0.0001632581,0.00181039,0.00004611837,0.000004068046],"category_scores_gemma":[0.00002795063,0.00008334129,0.00004660301,0.0007037084,0.001473352,0.0005069138,0.0001933346,0.0002419143,0.000008544998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003723766,"about_ca_system_score_gemma":0.0001492732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006540211,"about_ca_topic_score_gemma":0.00005173616,"domain_scores_codex":[0.9986395,0.00005261224,0.0001740784,0.0004232187,0.0003448086,0.000365799],"domain_scores_gemma":[0.9990173,0.0001938006,0.0001608318,0.0004512345,0.0001151419,0.00006171574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00004775609,0.0004166758,0.06068544,0.0001211913,0.0001396082,0.00005969647,0.03677187,0.001355014,0.05037291,0.05503741,0.005884241,0.7891082],"study_design_scores_gemma":[0.001366577,0.001764941,0.6985115,0.0002299577,0.00006636568,0.0006931594,0.003280661,0.1510054,0.04247843,0.0143106,0.08460255,0.001689796],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5488789,0.0001768872,0.4433798,0.005501415,0.0008646642,0.000110053,6.209599e-7,0.00008204491,0.001005522],"genre_scores_gemma":[0.9561799,0.000005895686,0.0428449,0.0007684372,0.0000837232,0.000006355971,2.666712e-7,0.000003514973,0.0001070447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7874184,"threshold_uncertainty_score":0.5428627,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2120626248","doi":"10.1016/j.cageo.2006.02.010","title":"An empirical evaluation of spatial regression models","year":2006,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":55,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Geological Survey of Canada","funders":"Ministry of Education, Culture, Sports, Science and Technology","keywords":"Econometrics; Regression analysis; Statistics; Linear regression; Regression diagnostic; Computer science; Proper linear model; Regression; Statistical hypothesis testing; Spatial analysis; Empirical research; Set (abstract data type); Independence (probability theory); Sample size determination; Polynomial regression; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.09965555278562267,"gpt":0.3057037009521233,"spread":0.2060481481665007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009711253,0.00008019652,0.0002121756,0.000194212,0.00009784689,0.00005841324,0.000333435,0.0000434188,0.0001268523],"category_scores_gemma":[0.00001891377,0.00007120619,0.00006554082,0.0003138159,0.0001049197,0.0004170414,0.00004084385,0.00003787965,0.00002534966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000263125,"about_ca_system_score_gemma":0.0000286792,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009249025,"about_ca_topic_score_gemma":0.0003666309,"domain_scores_codex":[0.9989588,0.00003906959,0.0003695231,0.0003408712,0.0001474006,0.0001443091],"domain_scores_gemma":[0.9993625,0.0000272867,0.0002464529,0.0002515823,0.00006858938,0.00004356675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002546304,0.0005989951,0.591543,0.00002022869,0.00003237433,0.000002396556,0.001030499,0.2358467,0.0002609396,0.03451812,0.003096413,0.1330249],"study_design_scores_gemma":[0.0001416611,0.00005279087,0.1060096,0.000006470329,0.000008622455,5.199284e-7,0.00001835038,0.8389455,0.00006452373,0.05429824,0.0003673905,0.00008629539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7217041,0.0003913247,0.27482,0.0001548877,0.0003944309,0.000073502,0.00005183406,0.00001614291,0.002393799],"genre_scores_gemma":[0.9967443,0.00001136515,0.002961407,0.00005711648,0.0001275357,0.000003771066,0.00006629011,0.000003196483,0.0000250533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6030988,"threshold_uncertainty_score":0.9973485,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2057181473","doi":"10.1016/j.cageo.2004.05.001","title":"The art and science of mapping: computing geological categories from field data","year":2004,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":53,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University; Geological Survey of Canada","funders":"","keywords":"Field (mathematics); Categorization; Data science; Computer science; Geologic map; Geological survey; Artificial intelligence; Data mining; Earth science; Geology; Geophysics; Paleontology; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.05009900455115847,"gpt":0.3007352964227465,"spread":0.2506362918715881,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.0025441,0.00008599918,0.00015284,0.0001096732,0.003074975,0.0003554585,0.001658316,0.00003723179,0.000003101158],"category_scores_gemma":[0.000588928,0.00005651877,0.00002166601,0.001003963,0.005734461,0.0006552145,0.0009578581,0.00008453536,0.000006815204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002045783,"about_ca_system_score_gemma":0.0002485804,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007749703,"about_ca_topic_score_gemma":0.001373877,"domain_scores_codex":[0.9982882,0.00005054883,0.0003048748,0.000305971,0.0006852015,0.0003652303],"domain_scores_gemma":[0.9984716,0.000728513,0.0002028536,0.0003373525,0.0001775133,0.00008218104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00001594395,0.00008690409,0.2013224,0.00003565687,0.00006971144,0.000007479558,0.2417617,0.0006936424,0.0002438447,0.4620688,0.004131719,0.08956216],"study_design_scores_gemma":[0.0008940123,0.0003148855,0.4734564,0.0002681638,0.00002883165,0.0000141995,0.247458,0.01166104,0.0003391542,0.06715748,0.1975884,0.000819318],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.949555,0.0006714577,0.02697402,0.01007024,0.002183068,0.0002914366,0.000008720282,0.00009227461,0.01015373],"genre_scores_gemma":[0.9948008,0.0001691108,0.004711575,0.0001876706,0.00009717891,0.000001399463,0.000001973663,0.000001102798,0.00002920253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3949113,"threshold_uncertainty_score":0.9988578,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2088650351","doi":"10.1016/j.cageo.2010.09.007","title":"HOSIM: A high-order stochastic simulation algorithm for generating three-dimensional complex geological patterns","year":2010,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; BHP Billiton","keywords":"Cumulant; Legendre polynomials; Algorithm; Gaussian; Kriging; Orthonormal basis; Mathematics; Computer science; Applied mathematics; Statistics; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.02349225404051768,"gpt":0.259439298560923,"spread":0.2359470445204053,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000299805,0.0001632057,0.0001588379,0.00004001812,0.0005433143,0.0001170333,0.000339113,0.00006336951,0.0003688482],"category_scores_gemma":[0.00008310876,0.0001357601,0.00004214061,0.0002052687,0.0003474386,0.0001539843,0.0002830431,0.0001306051,0.00003615203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002433216,"about_ca_system_score_gemma":0.00001975281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008416316,"about_ca_topic_score_gemma":0.000641697,"domain_scores_codex":[0.9984773,0.00002082238,0.0002267093,0.0005037189,0.0003695387,0.0004019113],"domain_scores_gemma":[0.9991516,0.0004069719,0.0001096766,0.0001670449,0.00003827295,0.0001264249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003023039,0.00007122528,0.006184406,0.000003255174,0.000005926344,0.00000382149,0.000111995,0.6968077,0.002722978,0.0003610011,0.000458712,0.2932659],"study_design_scores_gemma":[0.0002312656,0.00009962689,0.09363591,0.000006054984,0.000006848565,0.000004977132,0.00001278998,0.9037317,0.00001308673,0.001528355,0.0005449516,0.0001844489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3943852,0.000003015199,0.604489,0.0001264742,0.0007322596,0.0001881343,0.00002571115,0.00003561125,0.00001459322],"genre_scores_gemma":[0.6789644,1.749313e-7,0.3203743,0.0004313525,0.0001492478,0.00001862666,0.00003436976,0.000005821571,0.000021738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2930815,"threshold_uncertainty_score":0.5536135,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2070424919","doi":"10.1016/j.cageo.2012.06.023","title":"Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy","year":2012,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":52,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Support vector machine; Random forest; Artificial neural network; Benchmark (surveying); Computer science; Regression; Artificial intelligence; Machine learning; Feature selection; Decision tree; Evolutionary algorithm; Linear regression; Tree (set theory); Data mining; Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01556180750645817,"gpt":0.2389371640800252,"spread":0.2233753565735671,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006149843,0.0002231729,0.000192642,0.00006919554,0.0004375971,0.00006476323,0.0006049998,0.00006591155,0.000721533],"category_scores_gemma":[0.0000191066,0.0001450712,0.00003517406,0.000558547,0.001963722,0.0007203913,0.0003151326,0.0001649539,0.0001825162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001384913,"about_ca_system_score_gemma":0.00002448808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002932492,"about_ca_topic_score_gemma":0.00002955194,"domain_scores_codex":[0.9977388,0.0001090005,0.0002398512,0.0005281851,0.0006779886,0.0007062035],"domain_scores_gemma":[0.9994056,0.00009540893,0.000116284,0.0001678583,0.000002144794,0.0002126527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004789087,0.0006300315,0.9698945,0.000004081008,0.000003121666,0.00001424402,0.0003930207,0.01025827,0.004677136,0.00003533689,0.003672096,0.01037026],"study_design_scores_gemma":[0.0008001733,0.002820607,0.7279499,0.00006688615,0.00001150736,0.000115796,0.0001382618,0.2598963,0.0007986701,0.0002601288,0.006400271,0.0007414908],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972672,0.0001069633,0.0002952101,0.0003324426,0.0003055242,0.0001666945,0.00001381986,0.00006133277,0.001450798],"genre_scores_gemma":[0.9901477,0.00000850294,0.009288656,0.0002728738,0.00004963176,0.000007974667,0.00001937349,0.000007953159,0.0001973009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.249638,"threshold_uncertainty_score":0.790028,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2058713652","doi":"10.1016/j.cageo.2005.07.008","title":"Spatial targeting using queries in a 3-D GIS environment with application to mineral exploration","year":2005,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":51,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Mira Geoscience (Canada); Barrie Urology Group; Natural Resources Canada","funders":"","keywords":"Computer science; Spatial query; Query expansion; Query optimization; Sargable; RDF query language; Query language; Intersection (aeronautics); Data mining; Grid; Web query classification; Set (abstract data type); Geographic information system; Web search query; Information retrieval; Geology; Search engine; Geography; Remote sensing; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.01763343602015528,"gpt":0.2040822065944533,"spread":0.1864487705742981,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002309912,0.00008858334,0.0001093616,0.000101187,0.0001516723,0.00007052992,0.0001641059,0.00002287463,0.0000643124],"category_scores_gemma":[0.000009085202,0.00006289108,0.000019903,0.0002812255,0.00007123219,0.0002950037,0.00001529449,0.00004753138,0.00005132929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008327032,"about_ca_system_score_gemma":0.00001623627,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007727421,"about_ca_topic_score_gemma":0.007822117,"domain_scores_codex":[0.9990485,0.00003763101,0.0001622519,0.0003099635,0.0002248717,0.0002168213],"domain_scores_gemma":[0.9997488,0.00002625069,0.00005262357,0.00008242096,0.0000114725,0.00007843052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005947299,0.000009001063,0.1155315,9.578238e-7,0.000001014007,8.128707e-7,0.000341877,0.7977366,0.00009337176,0.000003775762,0.00000943414,0.08626568],"study_design_scores_gemma":[0.00006189018,0.0000888753,0.06431701,0.000008136272,0.000003466112,0.000001231242,0.0001982031,0.9338693,0.00003078984,0.00006550283,0.001251647,0.0001039237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5944752,0.00004411028,0.4045062,0.0008227914,0.00003495544,0.00006358541,0.000001857186,0.00001363265,0.00003774064],"genre_scores_gemma":[0.9196632,0.000007719053,0.07996272,0.0002146561,0.0001127798,0.000002047808,0.0000223009,9.193112e-7,0.00001365151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3251881,"threshold_uncertainty_score":0.9988802,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2058533675","doi":"10.1016/j.cageo.2004.10.003","title":"A semi-automatic segmentation procedure for feature extraction in remotely sensed imagery","year":2004,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Natural Resources Canada","funders":"Canadian Forest Service; Chinese Academy of Sciences; Government of Ontario","keywords":"Computer science; Thresholding; Artificial intelligence; Computer vision; Feature extraction; Sample (material); Feature (linguistics); Remote sensing; Segmentation; Edge detection; Image segmentation; Pattern recognition (psychology); Software; Image processing; Image (mathematics); Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.01499451929209674,"gpt":0.2569601393947394,"spread":0.2419656201026427,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001836036,0.0001434791,0.0001405672,0.0002030723,0.00009574115,0.0001479373,0.0001323511,0.00007522057,8.621321e-7],"category_scores_gemma":[0.00006574408,0.0001425909,0.0000433937,0.0004918189,0.00006853339,0.0004994486,0.00001086892,0.0001199003,0.000008800532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002251575,"about_ca_system_score_gemma":0.0000618573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001673259,"about_ca_topic_score_gemma":0.00002862316,"domain_scores_codex":[0.9990445,0.00001777113,0.0002115146,0.0002786105,0.0001949141,0.0002526427],"domain_scores_gemma":[0.9995974,0.00008191042,0.00007037153,0.000151803,0.00004969984,0.0000488116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001185384,0.00007294631,0.0002967272,0.0003727083,0.00001244657,0.00002255812,0.002411257,0.266916,0.5655947,0.00004251617,0.002743978,0.1615023],"study_design_scores_gemma":[0.0004402777,0.00003650217,0.03280291,0.0001559198,0.000007773024,0.00005258372,0.000239766,0.9459815,0.01938637,0.0005214342,0.0001769211,0.0001980501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5955739,0.00008293927,0.4019955,0.0006108016,0.0008405484,0.0004625619,0.000003185603,0.0003351147,0.00009551094],"genre_scores_gemma":[0.8544972,0.00001320507,0.1452122,0.0001002732,0.00008853273,0.000008807965,0.00002504921,0.00001769352,0.00003705322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6790655,"threshold_uncertainty_score":0.5814687,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2038884454","doi":"10.1016/j.cageo.2015.04.008","title":"A new intelligent method for minerals segmentation in thin sections based on a novel incremental color clustering","year":2015,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Thin section; Segmentation; Cluster analysis; Geology; Petrography; Hue; Mineralogy; Thin film; Mineral; Mars Exploration Program; Artificial intelligence; Computer science; Pattern recognition (psychology); Materials science; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.06080255484459258,"gpt":0.3160816534234656,"spread":0.255279098578873,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000473997,0.0001137784,0.0001199367,0.0002194332,0.0000771022,0.0001147697,0.0001565721,0.00003299549,0.000003118519],"category_scores_gemma":[0.00003178023,0.0001052611,0.0000318164,0.0003439697,0.00001675957,0.0001703882,0.00002157069,0.0000721568,0.000002575116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001542906,"about_ca_system_score_gemma":0.0000588452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003724997,"about_ca_topic_score_gemma":0.0002641817,"domain_scores_codex":[0.9992084,0.0000178988,0.0001908422,0.0002025283,0.0001701411,0.0002101958],"domain_scores_gemma":[0.9996638,0.0001081673,0.00003376409,0.00006991335,0.00002137929,0.0001029301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001256166,0.00002258915,0.0001094234,0.00002523173,0.000003300965,4.864817e-7,0.001148551,0.971298,0.01631054,0.00003108253,0.00142557,0.009612673],"study_design_scores_gemma":[0.0005039788,0.0001363204,0.0001049156,0.00007926829,0.000004018432,0.000002980157,0.0003998326,0.9917038,0.005955107,0.000073893,0.0009084636,0.0001274153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09004159,0.00003404198,0.9081368,0.0001538768,0.0009940161,0.0002057357,0.000004356918,0.0000963928,0.0003331967],"genre_scores_gemma":[0.469036,7.04073e-7,0.5304797,0.0001872122,0.0001211817,0.00003050008,0.000007854924,0.000009747133,0.0001271158],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3789944,"threshold_uncertainty_score":0.4292421,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1977764052","doi":"10.1016/j.cageo.2006.12.007","title":"An artificial neural net assisted approach to editing edges in petrographic images collected with the rotating polarizer stage","year":2007,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Artificial neural network; Petrography; Pattern recognition (psychology); Stage (stratigraphy); Segmentation; Net (polyhedron); Enhanced Data Rates for GSM Evolution; Texture (cosmology); Computer vision; Image (mathematics); Geology; Mathematics; Geometry; Mineralogy","retraction":null,"screen_n_in":null,"score":{"opus":0.02442917192485867,"gpt":0.2848533985644492,"spread":0.2604242266395906,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002270356,0.0002232713,0.0002243766,0.0005397536,0.0005918918,0.0009687276,0.002011466,0.00005076837,0.000003781204],"category_scores_gemma":[0.000144995,0.000151505,0.0000440515,0.003831319,0.0005154607,0.001022576,0.0002674907,0.0003116089,0.000001907727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004236173,"about_ca_system_score_gemma":0.0001032786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003279161,"about_ca_topic_score_gemma":0.0002791322,"domain_scores_codex":[0.9970142,0.0002180085,0.0004233154,0.0007677876,0.0008769864,0.0006997616],"domain_scores_gemma":[0.9984623,0.0004447096,0.0002065666,0.0004782305,0.0001466427,0.0002615307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005332119,0.0007521035,0.03194024,0.00004346946,0.00002354994,0.0001444129,0.01491179,0.002073334,0.03089574,0.002650622,0.002694536,0.9138169],"study_design_scores_gemma":[0.0005981128,0.001258212,0.3589307,0.0001015507,0.00001171764,0.00007314858,0.005423449,0.6100786,0.02227155,0.0002469489,0.0001977606,0.0008081858],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2023714,0.00001912633,0.7956322,0.0007305798,0.0002936271,0.0004121747,0.000002114472,0.0003013444,0.0002373473],"genre_scores_gemma":[0.5321115,4.596439e-7,0.4663911,0.001310904,0.0001232843,0.00002575861,0.000004451713,0.000007737399,0.00002487857],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9130087,"threshold_uncertainty_score":0.9341463,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2058032984","doi":"10.1016/j.cageo.2013.03.016","title":"A hybrid framework for reservoir characterization using fuzzy ranking and an artificial neural network","year":2013,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Reservoir modeling; Computer science; Artificial neural network; Data mining; Field (mathematics); Ranking (information retrieval); Characterization (materials science); Multilayer perceptron; Neuro-fuzzy; Fuzzy logic; Artificial intelligence; Set (abstract data type); Machine learning; Fuzzy control system; Geology; Petroleum engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02971701933240444,"gpt":0.261549443056046,"spread":0.2318324237236415,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001950471,0.000126658,0.0001657062,0.0001062183,0.0003137218,0.0004204994,0.0002033024,0.00004596164,0.0000101425],"category_scores_gemma":[0.00002030051,0.0001140765,0.0000459016,0.0003333493,0.00008268002,0.0007438761,0.00004079558,0.00008687157,0.000003128947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001587716,"about_ca_system_score_gemma":0.0000119713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003619469,"about_ca_topic_score_gemma":0.00001201291,"domain_scores_codex":[0.9990314,0.00003871738,0.0002180318,0.0002447136,0.0001609703,0.0003061953],"domain_scores_gemma":[0.9995731,0.00006492089,0.00004888639,0.0001492047,0.00004880112,0.0001151283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006511202,0.00001810659,0.001142576,0.00003347193,0.00002184867,0.000001759514,0.0009020151,0.961,0.01311858,0.001167538,0.0001057668,0.02248187],"study_design_scores_gemma":[0.00005507403,0.0000322427,0.001349104,0.00002636005,0.00001095705,0.000002434018,0.00007292639,0.9899704,0.0002198845,0.007951564,0.0001588638,0.0001501566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5893038,0.00004105973,0.4097598,0.0001236675,0.0005292696,0.0001362973,0.000001918398,0.00009434329,0.000009809401],"genre_scores_gemma":[0.9739535,0.00001656559,0.02519315,0.0001535581,0.0006163662,0.00002049108,0.00002767937,0.00001381138,0.0000048762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3846497,"threshold_uncertainty_score":0.4651903,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1998512621","doi":"10.1016/s0098-3004(00)00112-6","title":"GIS-based statistical and fractal/multifractal analysis of surface stream patterns in the Oak Ridges Moraine","year":2001,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Groundwater and Watershed Analysis","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ministry of Natural Resources and Forestry; Natural Resources Canada; Geological Survey of Canada; York University","funders":"","keywords":"Geology; Drainage density; Bedrock; Drainage basin; STREAMS; Geomorphology; Structural basin; Drainage; Lithology; Hydrology (agriculture); Drainage system (geomorphology); Paleontology; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.01102353451705058,"gpt":0.2422812706767223,"spread":0.2312577361596717,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005363242,0.0001435845,0.0002696036,0.0001153285,0.0001225671,0.0001077764,0.0004727063,0.00003258853,0.0002141428],"category_scores_gemma":[0.00001837034,0.00009137671,0.00007989897,0.001283302,0.0005630417,0.0002194604,0.0001182424,0.00008865644,0.000008163488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003048207,"about_ca_system_score_gemma":0.000007674195,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008896274,"about_ca_topic_score_gemma":0.002007316,"domain_scores_codex":[0.9984436,0.0001367275,0.0002644432,0.0004011562,0.0004550879,0.0002989846],"domain_scores_gemma":[0.999273,0.0003115694,0.00008895025,0.0002371904,0.000008558358,0.00008074415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007593906,0.0001229955,0.9524112,0.000003241209,0.00003261368,0.00002457581,0.0008559194,0.03345506,0.0002746332,0.00001092735,0.00005659195,0.01274466],"study_design_scores_gemma":[0.0001113172,0.00005215363,0.6066205,0.000004311662,0.00008740077,0.000001849583,0.0003602922,0.3924511,0.00004809055,0.00005156118,0.0001261602,0.00008522741],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9598898,0.00001571271,0.03904408,0.0007622614,0.00004233473,0.00008108225,0.00004432304,0.00001116168,0.0001092121],"genre_scores_gemma":[0.9973338,0.0000185138,0.002279362,0.0002932919,0.000008774125,0.000002320806,0.00004111372,0.000003295678,0.00001957933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3589961,"threshold_uncertainty_score":0.9977036,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2106744058","doi":"10.1016/j.cageo.2005.08.009","title":"An automated GIS procedure for comparing GPS and proximal LIDAR elevations","year":2006,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University; Nova Scotia Community College","funders":"","keywords":"Lidar; Remote sensing; Ranging; Global Positioning System; Elevation (ballistics); Ground truth; Digital elevation model; Data collection; Computer science; Geology; Geodesy; Artificial intelligence; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.01128935157728385,"gpt":0.2533769871592462,"spread":0.2420876355819623,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001649624,0.00009875698,0.0001004787,0.00003906615,0.0005405701,0.0001689593,0.0002283695,0.00003199428,0.000006576528],"category_scores_gemma":[0.000008927577,0.00008707011,0.00002193409,0.000314116,0.0003481229,0.0002504465,0.00005675756,0.00004383793,0.00001306185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003017864,"about_ca_system_score_gemma":0.00001741882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004852516,"about_ca_topic_score_gemma":0.000211738,"domain_scores_codex":[0.9990562,0.00001665132,0.0001482632,0.0003756027,0.0001663676,0.0002369426],"domain_scores_gemma":[0.9996479,0.00003947811,0.00006398877,0.0001601917,0.00001312641,0.00007529333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004072112,0.001315698,0.5296263,0.0001104353,0.00002748857,0.000005918954,0.004472155,0.1063809,0.152411,0.01002853,0.05990706,0.1356738],"study_design_scores_gemma":[0.0001085566,0.00005830178,0.2793557,0.000007569036,0.000005475522,0.000009611729,0.00006247376,0.7143783,0.0004388342,0.0008929827,0.004566837,0.0001153691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9501839,0.0000144724,0.04714121,0.000522099,0.00008442579,0.0003435914,0.000004113844,0.0003059974,0.001400202],"genre_scores_gemma":[0.9463695,8.806545e-7,0.0533328,0.0001058656,0.00005372574,0.000008787091,0.000015477,0.000005805613,0.000107169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6079974,"threshold_uncertainty_score":0.4157682,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2032637631","doi":"10.1016/j.cageo.2012.10.019","title":"Underground stope optimization with network flow method","year":2012,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Sink (geography); Graph; Block (permutation group theory); Flow (mathematics); Node (physics); Computer science; Algorithm; Mathematical optimization; Geology; Engineering; Mathematics; Structural engineering; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.01402988699931035,"gpt":0.2202686209697558,"spread":0.2062387339704455,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002879404,0.00008744572,0.00009712274,0.00003532298,0.00009393563,0.00007639952,0.0001790023,0.00003183612,0.00002428869],"category_scores_gemma":[0.000001957681,0.00007390144,0.00001691051,0.0001716087,0.00003724707,0.0003261401,0.00003616897,0.00005364299,0.000005986853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000032437,"about_ca_system_score_gemma":0.000009043017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001155588,"about_ca_topic_score_gemma":0.000005875253,"domain_scores_codex":[0.9994333,0.00001309811,0.00009435888,0.000111045,0.00006189768,0.0002862363],"domain_scores_gemma":[0.9997286,0.00004246251,0.00002324536,0.0001234644,0.000009186124,0.00007308536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[8.544359e-7,0.000003953781,0.0008775084,0.000003937359,0.000004946182,2.393422e-7,0.0001590938,0.9848035,0.000001745296,0.0006571402,0.002435853,0.01105125],"study_design_scores_gemma":[0.00004179502,0.00003023037,0.0004985451,0.00001375529,0.000004564808,0.00000962483,0.00002903975,0.991971,0.00002004669,0.0001286268,0.007133234,0.0001195107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008229611,0.0001671423,0.988305,0.00003846929,0.0008247401,0.00005943793,6.965061e-7,0.0002841218,0.002090802],"genre_scores_gemma":[0.1386585,0.00003624196,0.8609548,0.0000872786,0.0002141129,0.000004707543,0.000003082945,0.0000098614,0.00003138034],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1304289,"threshold_uncertainty_score":0.3013614,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2108033773","doi":"10.1016/j.cageo.2010.07.001","title":"On the simulation of continuous in scale universal multifractals, Part II: Space–time processes and finite size corrections","year":2010,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":40,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Université du Québec à Montréal","funders":"","keywords":"Discretization; Scale (ratio); Computer science; Spacetime; Statistical physics; Code (set theory); Space (punctuation); Space time; Algorithm; Applied mathematics; Mathematics; Mathematical analysis; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01310543700998622,"gpt":0.1980111995617381,"spread":0.1849057625517518,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003275428,0.0000824353,0.0002277838,0.0001367848,0.0002260074,0.00007032668,0.0001743194,0.00003537369,0.0002031801],"category_scores_gemma":[0.0003071231,0.00006856638,0.00004141564,0.0005473928,0.0002047235,0.0001677408,0.00006706817,0.00008997341,0.00001763424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001008181,"about_ca_system_score_gemma":0.00001958363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001308792,"about_ca_topic_score_gemma":0.00102866,"domain_scores_codex":[0.9992885,0.00001286913,0.0002711354,0.0002498507,0.00004275557,0.0001348727],"domain_scores_gemma":[0.9987718,0.0007593299,0.0002334609,0.0001576405,0.0000434314,0.00003431771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001268317,0.001194268,0.6060115,0.0001595692,0.0001680272,0.000008769792,0.01524282,0.2608876,0.0005476694,0.0913413,0.00244137,0.02187035],"study_design_scores_gemma":[0.0002850014,0.0001675774,0.036614,0.00004032173,0.000006102256,0.000001098703,0.0004444679,0.938764,0.00003435641,0.003184529,0.02027384,0.0001846786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954883,0.00008844595,0.001864156,0.0004804607,0.0004272177,0.0001281937,0.00003044645,0.0000134158,0.001479359],"genre_scores_gemma":[0.9987236,0.00001076456,0.0002503883,0.00004060024,0.00003527134,0.000003139886,0.00000172457,0.000003582702,0.0009309758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6778765,"threshold_uncertainty_score":0.2796056,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2047538642","doi":"10.1016/j.cageo.2014.11.001","title":"Stochastic interpretation of thermal response test with TRT-SInterp","year":2014,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geothermal Energy Systems and Applications","field":"Energy","cited_by":40,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Borehole; Thermal resistance; Thermal; Heat capacity; Heat exchanger; Grout; Thermal conductivity; Volumetric heat capacity; Line source; Heat transfer; Inversion (geology); Mechanics; Geology; Materials science; Environmental science; Geotechnical engineering; Thermodynamics; Heat spreader; Engineering; Mechanical engineering; Structural basin; Acoustics; Physics; Composite material","retraction":null,"screen_n_in":null,"score":{"opus":0.007196296315848867,"gpt":0.2169366912733408,"spread":0.2097403949574919,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003373835,0.0001097252,0.0001551976,0.00009060765,0.00009845125,0.00004369937,0.0003468464,0.000029644,0.00002249898],"category_scores_gemma":[0.0000661659,0.00007793771,0.00003981337,0.000263212,0.000256496,0.0001163508,0.00004018335,0.00004661103,0.00002162169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000134729,"about_ca_system_score_gemma":0.00003764675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001099218,"about_ca_topic_score_gemma":0.0001548064,"domain_scores_codex":[0.9990866,0.00009432557,0.0002088969,0.0002397286,0.0001962588,0.0001742336],"domain_scores_gemma":[0.9990384,0.0004405886,0.0001474681,0.0002459415,0.00006724055,0.00006038713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002810991,0.0001646367,0.001124817,0.00002105384,0.0000227255,0.00000131329,0.002636929,0.8507503,0.03120518,0.02048597,0.00007457007,0.09323145],"study_design_scores_gemma":[0.0003800028,0.0006758866,0.04165453,0.0001496623,0.00001344258,0.00001395898,0.0002743701,0.9519703,0.001029326,0.0003051863,0.003323305,0.000210022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7305762,0.00001558681,0.2668987,0.0002223848,0.0001497556,0.00007130736,0.000002156193,0.00005023285,0.002013676],"genre_scores_gemma":[0.9975462,2.058719e-7,0.001924834,0.0001171433,0.00005297396,0.00001841319,0.000002010166,0.000008882515,0.0003293257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.26697,"threshold_uncertainty_score":0.3178208,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2131979394","doi":"10.1016/j.cageo.2003.10.012","title":"Derivation of deformation characteristics in fast-moving glaciers","year":2004,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":38,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Geology; Crevasse; Glacier; Deformation (meteorology); Surge; Glaciology; Geodesy; Geomorphology; Glacier morphology; Ice sheet; Ice stream; Cryosphere; Paleontology; Climatology; Sea ice; Tectonics; Oceanography; Stratigraphy","retraction":null,"screen_n_in":null,"score":{"opus":0.01438427827513244,"gpt":0.2038478684546809,"spread":0.1894635901795484,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001962294,0.00006699723,0.0001146681,0.0000595604,0.0001396634,0.00003795173,0.0001757624,0.00002184047,0.00003532061],"category_scores_gemma":[0.00004228367,0.00005638972,0.00002477038,0.0005539884,0.0001222728,0.000357017,0.00001526206,0.00004382432,0.000009215991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001050742,"about_ca_system_score_gemma":0.00005302296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002198579,"about_ca_topic_score_gemma":0.001534297,"domain_scores_codex":[0.9992514,0.0000126864,0.0002458411,0.0001318344,0.0001870183,0.0001712337],"domain_scores_gemma":[0.999679,0.0000559741,0.0001191774,0.00007519291,0.00003531328,0.0000352897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004131322,0.00001380831,0.8722855,0.00001289023,0.000002341039,0.000001299432,0.001813485,0.02288887,0.00003572122,0.0002781012,0.00001383223,0.10265],"study_design_scores_gemma":[0.0001273933,0.00005158827,0.9591837,0.00002755158,0.000001875713,0.000001627924,0.0007763847,0.03896266,0.00002963727,0.0004907401,0.0002784349,0.00006841841],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9776739,0.00008339447,0.02112923,0.0002555451,0.0005307962,0.00007963167,0.000008478981,0.00001600738,0.0002229386],"genre_scores_gemma":[0.9921983,0.00004727505,0.007543167,0.0001490075,0.00003178313,5.411596e-7,0.00002351611,7.717869e-7,0.000005640843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1025816,"threshold_uncertainty_score":0.3323608,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3018312834","doi":"10.1016/j.cageo.2020.104505","title":"Modeling transport of charged species in pore networks: Solution of the Nernst–Planck equations coupled with fluid flow and charge conservation equations","year":2020,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; University of Waterloo","funders":"Canarie","keywords":"Nernst equation; Planck; Flow (mathematics); Charge (physics); Charge conservation; Fluid dynamics; Physics; Mechanics; Conservation law; Classical mechanics; Quantum mechanics; Electrode","retraction":null,"screen_n_in":null,"score":{"opus":0.02331861220396944,"gpt":0.2023612407787191,"spread":0.1790426285747496,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001753944,0.000096798,0.0001701249,0.00006710886,0.00006700796,0.00001244549,0.0001785491,0.00003864168,0.000004042126],"category_scores_gemma":[0.00002830064,0.00007652455,0.00002832964,0.0004823545,0.0001360776,0.0002384778,0.000018376,0.0000886252,1.57345e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002098214,"about_ca_system_score_gemma":0.00003959848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008093109,"about_ca_topic_score_gemma":0.0002474893,"domain_scores_codex":[0.9992239,0.00001902652,0.0002773656,0.0001482894,0.0001912049,0.0001402254],"domain_scores_gemma":[0.9996686,0.00007427727,0.00006353552,0.0001028262,0.00005874018,0.00003200839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006046019,0.00001041451,0.003304575,0.00004423604,0.000006697451,3.243148e-7,0.00111454,0.9869753,0.006941251,0.0007766759,0.00002149479,0.0007985127],"study_design_scores_gemma":[0.0001406945,0.00004466722,0.003129577,0.0001193446,0.000008023421,4.851585e-7,0.00007595783,0.9950738,0.001219616,0.00009361627,0.00001113477,0.00008311405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3115902,0.0001146691,0.6876578,0.000248439,0.0001117526,0.0001535142,0.00001060901,0.00006001623,0.00005306341],"genre_scores_gemma":[0.9905148,0.00008701874,0.009265682,0.00006027039,0.00002566034,0.00001205888,0.00002328347,0.000007047584,0.00000414958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6789247,"threshold_uncertainty_score":0.312058,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2901475805","doi":"10.1016/j.cageo.2018.11.003","title":"A novel hierarchical clustering analysis method based on Kullback–Leibler divergence and application on dalaimiao geochemical exploration data","year":2018,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Divergence (linguistics); Hierarchical clustering; Geology; Pairwise comparison; Kullback–Leibler divergence; Covariance; Computer science; Measure (data warehouse); Regolith; Data mining; Pattern recognition (psychology); Artificial intelligence; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.0540333020122351,"gpt":0.3045135062292817,"spread":0.2504802042170466,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001030434,0.0002134727,0.0002411954,0.0002460956,0.0004807768,0.0002948266,0.002215835,0.00008928522,0.00001238317],"category_scores_gemma":[0.0001608475,0.0001833899,0.00005515019,0.001600085,0.0003502173,0.0006072635,0.001220921,0.0001777802,0.00001635325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002586687,"about_ca_system_score_gemma":0.00005811146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006929609,"about_ca_topic_score_gemma":0.00002640563,"domain_scores_codex":[0.9973491,0.00009467117,0.0002791709,0.001385313,0.0005159731,0.0003757368],"domain_scores_gemma":[0.9978384,0.000380272,0.0001512073,0.001339943,0.0001202351,0.0001699574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001912496,0.001185855,0.01089067,0.0001404368,0.0002423121,0.00002384655,0.003212437,0.3183556,0.05364946,0.0207064,0.00362225,0.5877795],"study_design_scores_gemma":[0.0001745426,0.0001521089,0.003392449,0.00002459945,0.00002497264,0.000005070931,0.00002141665,0.9896106,0.002154292,0.001392109,0.002825317,0.0002225134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008483156,0.000007937911,0.9853949,0.00464593,0.0002734126,0.0001495451,0.00001030732,0.0001212627,0.0009135251],"genre_scores_gemma":[0.6740038,0.000004029901,0.3247765,0.0009746987,0.0001390194,0.0000158286,0.00004551151,0.000002112811,0.00003846542],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.671255,"threshold_uncertainty_score":0.7478421,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2091004324","doi":"10.1016/j.cageo.2007.05.013","title":"Application of DInSAR-GPS optimization for derivation of three-dimensional surface motion of the southern California region along the San Andreas fault","year":2007,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"earthquake and tectonic studies","field":"Earth and Planetary Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; California Institute of Technology; Jet Propulsion Laboratory; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Geology; Global Positioning System; Geodesy; Motion (physics); Seismology; Surface (topology); Fault (geology); San andreas fault; Remote sensing; Computer science; Geometry; Artificial intelligence; Mathematics; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.01764673021793257,"gpt":0.2117834117936105,"spread":0.1941366815756779,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006078845,0.00007835327,0.0001258841,0.00003947053,0.0002426917,0.00001036367,0.0002606671,0.00003656633,0.00001128242],"category_scores_gemma":[0.00004064764,0.00004387188,0.00007611779,0.0003930939,0.0003648745,0.00008174074,0.00002098809,0.00003853671,0.000001134636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003143113,"about_ca_system_score_gemma":0.00003346749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003030888,"about_ca_topic_score_gemma":0.004709823,"domain_scores_codex":[0.9990667,0.00003592978,0.0002909413,0.0001660167,0.0003018736,0.0001385887],"domain_scores_gemma":[0.9989899,0.0003308212,0.0003523402,0.000161966,0.0001437804,0.0000211249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003012819,0.00001988016,0.5884858,0.00002284261,0.00001352377,6.422127e-8,0.0003978357,0.3457376,0.0001566282,0.00007361414,0.00003902526,0.06502298],"study_design_scores_gemma":[0.0001346624,0.00006746897,0.5230283,0.00002626198,0.00001724493,0.000002152859,0.0002336494,0.4750917,0.0009723737,0.0003203213,0.00005547215,0.000050349],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6845198,0.0002188959,0.3146274,0.0001614214,0.0001399363,0.0002553323,0.00003915652,0.000007222407,0.00003086156],"genre_scores_gemma":[0.994998,0.000008630492,0.004892782,0.00004015981,0.00002754027,7.627031e-7,0.00002558697,0.000001588491,0.000004991885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3104782,"threshold_uncertainty_score":0.4581817,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1971037209","doi":"10.1016/j.cageo.2014.05.015","title":"The ValleyMorph Tool: An automated extraction tool for transverse topographic symmetry (T-) factor and valley width to valley height (Vf-) ratio","year":2014,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"earthquake and tectonic studies","field":"Earth and Planetary Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Transverse plane; Geology; Extraction (chemistry); Symmetry (geometry); Aspect ratio (aeronautics); Geometry; Remote sensing; Geodesy; Geomorphology; Physics; Mathematics; Engineering; Structural engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01759098815613977,"gpt":0.246010831828557,"spread":0.2284198436724172,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009978043,0.0002526858,0.0002615796,0.0001518352,0.001493868,0.0004748416,0.0004939042,0.00007552445,0.00006084109],"category_scores_gemma":[0.000123834,0.0001665532,0.0001068235,0.0004765845,0.0003219844,0.000564997,0.0000267008,0.0001200495,0.00002539797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000517836,"about_ca_system_score_gemma":0.00005744528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006190275,"about_ca_topic_score_gemma":0.002415987,"domain_scores_codex":[0.9979194,0.0001846543,0.0003263236,0.0005976711,0.0004112516,0.0005606574],"domain_scores_gemma":[0.9982284,0.001095542,0.0001076092,0.0002968612,0.00008416088,0.000187422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000111854,0.00006970506,0.1271336,0.00004269541,0.00009658298,0.000004490224,0.002195769,0.002016295,0.0002257009,0.002031732,0.002300563,0.863771],"study_design_scores_gemma":[0.0004378641,0.0009229656,0.7671785,0.00002450055,0.00002645683,0.00001586513,0.0002992551,0.2024265,0.00007333316,0.00123384,0.02698747,0.0003734686],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9616624,0.0005827259,0.03334625,0.001286913,0.001714283,0.0006497721,0.00003846508,0.0003322807,0.0003869519],"genre_scores_gemma":[0.9900953,0.0002625249,0.00881866,0.0005020462,0.0001866529,0.00001051955,0.00001982084,0.000005787321,0.00009868621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8633975,"threshold_uncertainty_score":0.999806,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1977321581","doi":"10.1016/s0098-3004(02)00071-7","title":"DSSIM-HR: A FORTRAN 90 program for direct sequential simulation with histogram reproduction","year":2002,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Fortran; Computer science; Histogram; Reproduction; Parallel computing; Computer graphics (images); Computational science; Programming language; Artificial intelligence; Image (mathematics); Biology; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.02703173706570216,"gpt":0.2607365912920229,"spread":0.2337048542263207,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002548281,0.0001311339,0.0001244264,0.00003751058,0.0003951035,0.0001338728,0.0002098302,0.00003189067,0.00009223802],"category_scores_gemma":[0.0000378575,0.0001067969,0.00004796066,0.000381881,0.0003565399,0.0002889135,0.00005641121,0.00005117389,0.00002041636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007769987,"about_ca_system_score_gemma":0.000007539073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002811305,"about_ca_topic_score_gemma":0.0001259508,"domain_scores_codex":[0.9985617,0.00002295641,0.0001747208,0.0005843549,0.0003229087,0.0003333599],"domain_scores_gemma":[0.9994906,0.0000676047,0.0001109978,0.0002208925,0.00002025337,0.00008966772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001637747,0.0001653946,0.006499942,0.00001404174,0.000008560667,0.000003587637,0.0007775516,0.09058194,0.0001482823,0.00006376663,0.002657913,0.8990626],"study_design_scores_gemma":[0.0003233195,0.0006037611,0.01260446,0.00001945102,0.00002171163,0.000009133197,0.0000524036,0.8601979,0.00008586391,0.0003573948,0.125484,0.000240571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4734581,0.0001614431,0.5128128,0.0008009198,0.002131205,0.002220614,0.00001744253,0.0005394675,0.007857969],"genre_scores_gemma":[0.9370526,0.00000815243,0.06216782,0.00008293563,0.0001177125,0.00007327746,0.00001027353,0.000009028286,0.0004782189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8988221,"threshold_uncertainty_score":0.435505,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2905895112","doi":"10.1016/j.cageo.2018.12.005","title":"Geochemical characterisation of rock hydration processes using t-SNE","year":2018,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"First Quantum Minerals","keywords":"Curse of dimensionality; Dimensionality reduction; Pairwise comparison; Computer science; Representation (politics); Data mining; Visualization; Layered intrusion; Geology; Pattern recognition (psychology); Mafic; Artificial intelligence; Petrology","retraction":null,"screen_n_in":null,"score":{"opus":0.02536958636722985,"gpt":0.2491969164228079,"spread":0.223827330055578,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002866566,0.0001204771,0.0001527037,0.00008208807,0.0002198139,0.0001159212,0.0008977042,0.00005971205,0.00001500454],"category_scores_gemma":[0.0001704658,0.0001069245,0.00003376708,0.0007879858,0.0002015183,0.0006848323,0.0002753421,0.00006046216,0.00001052302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001835122,"about_ca_system_score_gemma":0.0001523168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003176297,"about_ca_topic_score_gemma":0.0000027218,"domain_scores_codex":[0.998727,0.00003142701,0.0002652652,0.0004201046,0.0002974538,0.000258724],"domain_scores_gemma":[0.998955,0.00007183869,0.0002280664,0.0002959553,0.0003837259,0.00006542621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002348125,0.0003166389,0.009290164,0.0003687355,0.00002814223,0.000009467991,0.008327527,0.004515517,0.9368921,0.004779193,0.001061037,0.03438798],"study_design_scores_gemma":[0.0001365722,0.0001446134,0.003448523,0.00008491196,0.000005121882,0.00003428346,0.00006015998,0.7504234,0.2383593,0.003664255,0.003419897,0.0002189578],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4248461,0.00002699026,0.5734566,0.0005008452,0.0004156271,0.00006603433,8.212926e-7,0.00006707288,0.0006199087],"genre_scores_gemma":[0.9686661,0.000003917043,0.03091884,0.0001364515,0.0001930306,0.000002622891,0.000003670272,0.000001216696,0.00007417472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7459078,"threshold_uncertainty_score":0.4360253,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3089544298","doi":"10.1016/j.cageo.2020.104621","title":"A comparison of isometric and amalgamation logratio balances in compositional data analysis","year":2020,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Compositional data; Transformation (genetics); Set (abstract data type); Pairwise comparison; Computer science; Data set; Variance (accounting); Domain (mathematical analysis); Contrast (vision); Mathematics; Algorithm; Statistics; Artificial intelligence; Mathematical analysis; Accounting","retraction":null,"screen_n_in":null,"score":{"opus":0.06946208439208983,"gpt":0.3115562797541143,"spread":0.2420941953620244,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003565206,0.000094767,0.0002876711,0.0002797869,0.00008901006,0.0001274287,0.001434528,0.00003344881,0.00000698463],"category_scores_gemma":[0.0001006131,0.00008539262,0.00003179246,0.003890472,0.0001742767,0.0005526427,0.0006604034,0.00008046021,0.00000153039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006475404,"about_ca_system_score_gemma":0.00003827544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004532343,"about_ca_topic_score_gemma":0.00002007818,"domain_scores_codex":[0.9986201,0.00006271313,0.0003238785,0.0005279551,0.0002980062,0.0001673626],"domain_scores_gemma":[0.9991816,0.0001710379,0.0001826342,0.0003242153,0.00006102143,0.00007945322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007101336,0.000143312,0.9436359,0.00007138086,0.00006164554,0.000005976861,0.003417082,0.0232377,0.001472718,0.005810129,0.0006892123,0.02144777],"study_design_scores_gemma":[0.0001032859,0.00005783665,0.1754957,0.000009411251,0.00001273398,0.000001475685,0.0001246564,0.8230888,0.0002309424,0.0004236284,0.0003689966,0.00008257024],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1943369,0.0004015638,0.7982329,0.006390114,0.00009725372,0.00008204374,0.00000986082,0.00004162821,0.0004076872],"genre_scores_gemma":[0.9472256,0.00001048362,0.05248429,0.0002173243,0.00002095919,0.00000161038,0.00003488402,4.722024e-7,0.00000434217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7998511,"threshold_uncertainty_score":0.348221,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2048951010","doi":"10.1016/j.cageo.2007.06.002","title":"Reasoning about geological space: Coupling 3D GeoModels and topological queries as an aid to spatial data selection","year":2007,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Natural Resources Canada; Geological Survey of Canada; Université Laval","funders":"","keywords":"Computer science; Context (archaeology); Adjacency list; Intersection (aeronautics); Topology (electrical circuits); Data mining; Geology; Algorithm; Paleontology; Geography; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03267212640399489,"gpt":0.273319660637599,"spread":0.2406475342336041,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001715806,0.0002064158,0.0002883438,0.0001472662,0.0007553545,0.0002938549,0.0007609974,0.0001347501,0.0003300683],"category_scores_gemma":[0.000261486,0.0001453208,0.00003630987,0.0005062006,0.0004060297,0.000493739,0.0001854153,0.0002152029,0.00005436369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005585181,"about_ca_system_score_gemma":0.00004668314,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01589697,"about_ca_topic_score_gemma":0.01162018,"domain_scores_codex":[0.9975898,0.00006863307,0.0003003988,0.0009736317,0.0004217783,0.0006457876],"domain_scores_gemma":[0.9988856,0.0002324203,0.00008940387,0.0002953044,0.00006835657,0.0004288778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007909277,0.00003976672,0.628351,0.000005085041,0.00001179587,0.00003582181,0.0003076807,0.2536514,0.00002725764,0.0004987233,0.00006183554,0.1169305],"study_design_scores_gemma":[0.00008067274,0.0004917738,0.2333214,0.00001360061,0.00001360014,0.00004000886,0.0002303968,0.7636969,0.00001058316,0.0009232661,0.0009792052,0.000198648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7097497,0.0002103319,0.2887302,0.0005202073,0.0002917953,0.00008098984,0.00001152798,0.00009921614,0.0003060385],"genre_scores_gemma":[0.9428829,0.00005880519,0.05598672,0.0006230362,0.0003017885,5.269412e-7,0.00008577137,0.00000195869,0.00005850151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5100455,"threshold_uncertainty_score":0.9906563,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2001831701","doi":"10.1016/j.cageo.2012.08.008","title":"Ultimate open pit stochastic optimization","year":2012,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Stochastic optimization; Stochastic programming; Mathematical optimization; Stochastic modelling; Open-pit mining; Profit (economics); Stochastic simulation; Range (aeronautics); Optimization problem; Computer science; Stochastic process; Mathematics; Geology; Statistics; Economics; Engineering; Mining engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02462385994550464,"gpt":0.2429425660778449,"spread":0.2183187061323403,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001944901,0.00007228593,0.00008657827,0.00004550358,0.00007812334,0.0001463836,0.000440082,0.00002485992,0.00004975406],"category_scores_gemma":[0.000005280222,0.00006942524,0.00001518547,0.00009783058,0.00003583063,0.0005031188,0.0001333176,0.00003773426,0.00002828625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002651046,"about_ca_system_score_gemma":0.000006449854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002121665,"about_ca_topic_score_gemma":0.000001303671,"domain_scores_codex":[0.9995164,0.000004980866,0.0001045635,0.00009758188,0.00004496452,0.0002314953],"domain_scores_gemma":[0.9997579,0.00002048812,0.00002170042,0.0001190714,0.000007518999,0.00007330194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[5.842785e-7,0.000007901745,0.0002184286,0.000004811211,0.000002797985,1.403908e-7,0.0003366922,0.9897981,0.00001696009,0.000564272,0.001996611,0.00705268],"study_design_scores_gemma":[0.00004441988,0.0000138697,0.000545025,0.00001232703,0.000002319896,0.000004080204,0.00002571891,0.9977576,0.00004445968,0.0000842897,0.001360909,0.0001050171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04139112,0.00007901588,0.9546655,0.00002993179,0.001025888,0.0001099916,0.000002248627,0.0002496097,0.002446724],"genre_scores_gemma":[0.7830155,0.00001511861,0.216773,0.00006372036,0.00007885186,0.000009641206,0.000003817398,0.00000835298,0.0000320138],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7416244,"threshold_uncertainty_score":0.2831079,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}