{"meta":{"query_hash":"3239adf3675a","filters":{"venue":"Springer series in supply chain management"},"cohort_total":13,"direct_labels_cover":0,"predictions_cover":13,"exported":13,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/3239adf3675a","api":"https://metacan.xera.ac/api/v1/cohort?venue=Springer+series+in+supply+chain+management"},"results":[{"id":"W2508062625","doi":"10.1007/978-3-319-29791-0_12","title":"Green Technology Choice","year":2016,"lang":"en","type":"book-chapter","venue":"Springer series in supply chain management","topic":"Environmental Sustainability in Business","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Simple (philosophy); Economics; Business","score_opus":0.007410724778262247,"score_gpt":0.19616331036690304,"score_spread":0.1887525855886408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508062625","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016184316,0.0005090643,0.00012630322,0.015564769,0.001189858,0.0021851158,0.000017925962,0.0005854762,0.97820306],"genre_scores_gemma":[0.02676483,0.00049327436,0.000623053,0.0017898454,0.0019302276,0.00041895013,0.000071314156,0.00035366238,0.96755487],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99649614,0.0000075273556,0.00078487064,0.0012005082,0.00063767954,0.0008732622],"domain_scores_gemma":[0.9979418,0.00003715687,0.00045448128,0.0014577013,0.00008451636,0.000024364967],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00044134565,0.0008683367,0.00071483554,0.0018202038,0.0001731643,0.0001750486,0.0012761154,0.00052969187,0.0030429917],"category_scores_gemma":[0.000041432842,0.00084310066,0.0001826318,0.00033240725,0.00063850614,0.0010674421,0.0032849056,0.00050617964,0.0017769882],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065588734,0.00007398067,0.02170954,0.0019231662,0.00014529779,0.00045468225,0.000022208522,0.000022440812,0.000011631843,0.9218196,0.0018964838,0.051855363],"study_design_scores_gemma":[0.0005863671,0.000013775454,0.009239878,0.0005393565,0.000086545544,0.0000030782264,0.00009319192,0.000007266246,0.0000058300634,0.13113591,0.8574076,0.0008811823],"about_ca_topic_score_codex":0.00018228721,"about_ca_topic_score_gemma":0.00051183195,"teacher_disagreement_score":0.8555111,"about_ca_system_score_codex":0.0007509312,"about_ca_system_score_gemma":0.0000146340335,"threshold_uncertainty_score":0.999402},"labels":[],"label_agreement":null},{"id":"W2982014188","doi":"10.1007/978-3-030-31733-1_8","title":"Price-Matching Strategy: Implications of Consumer Behavior and Channel Structure","year":2019,"lang":"en","type":"book-chapter","venue":"Springer series in supply chain management","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Queen's University","funders":"","keywords":"Product (mathematics); Business; Price discrimination; Context (archaeology); Competition (biology); Matching (statistics); Channel (broadcasting); Reservation price; Set (abstract data type); Microeconomics; Advertising; Industrial organization; Marketing; Economics; Computer science; Telecommunications","score_opus":0.016485399559957918,"score_gpt":0.2222913034030268,"score_spread":0.20580590384306888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982014188","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012737235,0.0018588472,0.00021199405,0.001261845,0.0017155155,0.0052984566,0.0001738997,0.0002627079,0.9764795],"genre_scores_gemma":[0.7207174,0.002355362,0.00076788047,0.001605391,0.0009214486,0.0003734313,0.0006247444,0.0003922578,0.27224213],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99687946,0.000013464377,0.00095372886,0.0010093473,0.0005344864,0.00060951133],"domain_scores_gemma":[0.9980031,0.00003441091,0.00072738127,0.0010693413,0.00012749722,0.000038242262],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004206121,0.0007940305,0.00083938637,0.0012352301,0.00015161792,0.0002780906,0.00072371826,0.00030929947,0.001534944],"category_scores_gemma":[0.000008907552,0.00084178936,0.00018038756,0.00021627772,0.000268392,0.0008392562,0.0014292402,0.0004834866,0.000107292995],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006991079,0.00009132682,0.0043638228,0.0033210786,0.00031394276,0.0000670991,0.00018142675,0.00030632125,0.000054918342,0.9759182,0.0039090933,0.011402909],"study_design_scores_gemma":[0.0021884625,0.000077807505,0.035251793,0.0013156605,0.00077881815,0.000009324458,0.0016563564,0.00016529195,0.000025230644,0.12224895,0.83421,0.0020723175],"about_ca_topic_score_codex":0.00015597981,"about_ca_topic_score_gemma":0.00034539998,"teacher_disagreement_score":0.8536692,"about_ca_system_score_codex":0.00015519281,"about_ca_system_score_gemma":0.000030608026,"threshold_uncertainty_score":0.9994033},"labels":[],"label_agreement":null},{"id":"W4205390994","doi":"10.1007/978-3-030-85855-1_1","title":"Introduction","year":2022,"lang":"en","type":"book-chapter","venue":"Springer series in supply chain management","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Gadget; Computer science; Scope (computer science); Demand forecasting; Operations research; On demand; Data science; Engineering; Multimedia; Algorithm","score_opus":0.04454961424653824,"score_gpt":0.3019480459911112,"score_spread":0.25739843174457294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205390994","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000046038578,0.00018458803,0.001424159,0.011783223,0.0010740588,0.0010364505,0.0000501004,0.00024137221,0.98416],"genre_scores_gemma":[0.0021850206,0.0006587322,0.0117828185,0.00019699745,0.00059708854,0.0004100129,0.00007331447,0.00007151426,0.9840245],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963626,0.000043411757,0.00087818195,0.0010798062,0.0013207522,0.00031520947],"domain_scores_gemma":[0.9977082,0.00009285712,0.00037276943,0.0016895882,0.00007703853,0.000059518414],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002174118,0.0003340008,0.00041163358,0.0008915755,0.00021767264,0.0001783757,0.0013282105,0.00012621716,0.020297894],"category_scores_gemma":[0.00008027928,0.00032643627,0.00017148655,0.00032088076,0.00016402492,0.00021033098,0.0016630235,0.00050019805,0.00048569846],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018335724,0.000017257567,0.000038045157,0.000017955017,0.000019772066,0.000033022887,0.00008751398,0.00017143252,0.0000019843276,0.8333086,0.11965982,0.046626277],"study_design_scores_gemma":[0.000063700805,0.00004172112,0.000112875336,0.000020370055,0.000011747345,0.000005594014,0.00011352382,0.000037483896,0.000008849959,0.26606768,0.7332818,0.00023462337],"about_ca_topic_score_codex":0.00002243867,"about_ca_topic_score_gemma":0.00007064183,"teacher_disagreement_score":0.613622,"about_ca_system_score_codex":0.0003072239,"about_ca_system_score_gemma":0.00002010166,"threshold_uncertainty_score":0.99991876},"labels":[],"label_agreement":null},{"id":"W4205685700","doi":"10.1007/978-3-030-85855-1_8","title":"Conclusion and Advanced Topics","year":2022,"lang":"en","type":"book-chapter","venue":"Springer series in supply chain management","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Censoring (clinical trials); Data science; Artificial intelligence; Analytics; Machine learning; Econometrics; Mathematics","score_opus":0.040760605019574465,"score_gpt":0.3116947197022129,"score_spread":0.27093411468263845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205685700","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041931064,0.0006011644,0.000818178,0.00464875,0.00043603661,0.0011777207,0.000054563374,0.0001531723,0.9916911],"genre_scores_gemma":[0.0072800787,0.002505291,0.022122918,0.0003981042,0.00010464259,0.00028264715,0.00003664168,0.000058061076,0.9672116],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974375,0.00002920289,0.0006426772,0.00078237825,0.00086248724,0.0002457741],"domain_scores_gemma":[0.99853,0.0001134326,0.00026656833,0.0009750635,0.000052159183,0.00006281919],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011106351,0.00027913856,0.00037505242,0.00043329067,0.00023747708,0.00012195927,0.00081759813,0.00011031259,0.0028722698],"category_scores_gemma":[0.000055530807,0.00026382075,0.00008851534,0.00015444511,0.0001695909,0.00015432667,0.0028507146,0.00033826267,0.000037826405],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019247633,0.000009890956,0.000099808465,0.000027005326,0.000011162064,0.000046631518,0.00017190685,0.000041347514,0.0000032262542,0.8256619,0.004690686,0.16921718],"study_design_scores_gemma":[0.00012517859,0.000054996428,0.00023566533,0.0000550118,0.000008898447,0.000004105451,0.00016956635,0.00006981237,0.000009369011,0.20006387,0.7989818,0.00022172641],"about_ca_topic_score_codex":0.000014822105,"about_ca_topic_score_gemma":0.000074140895,"teacher_disagreement_score":0.79429114,"about_ca_system_score_codex":0.00014325586,"about_ca_system_score_gemma":0.000015053419,"threshold_uncertainty_score":0.9999814},"labels":[],"label_agreement":null},{"id":"W4206741575","doi":"10.1007/978-3-030-81423-6_7","title":"Capacity Management in Agricultural Commodity Processing","year":2012,"lang":"en","type":"book-chapter","venue":"Springer series in supply chain management","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Profitability index; Investment (military); Portfolio; Yield (engineering); Capacity utilization; Commodity; Economics; Yield management; Revenue; Capacity management; Heuristics; Time horizon; Investment decisions; Microeconomics; Revenue management; Finance; Production (economics); Computer science; Mathematical optimization; Mathematics","score_opus":0.024518920849347692,"score_gpt":0.20821923896656933,"score_spread":0.18370031811722162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206741575","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001666346,0.0013891477,0.00008119941,0.0015338374,0.0018946803,0.0033267476,0.00001967046,0.00042085143,0.98966753],"genre_scores_gemma":[0.41558084,0.002762226,0.0035648302,0.0044764765,0.004577084,0.0016474412,0.00096671225,0.00067415694,0.56575024],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9939622,0.000037515525,0.0015259053,0.0015297052,0.0012911028,0.0016535382],"domain_scores_gemma":[0.99776775,0.000025019503,0.00076005916,0.0012532162,0.000114067545,0.00007986832],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0014845283,0.0014665356,0.0012010618,0.002257761,0.00029724595,0.00063310977,0.0015054636,0.0004520696,0.002078397],"category_scores_gemma":[0.000011811696,0.0014834102,0.00036803956,0.00062479544,0.00033597264,0.0023549632,0.0027797283,0.0010594025,0.0010252432],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015104514,0.00039350288,0.0040233084,0.005740335,0.00033196204,0.00055465585,0.00036236297,0.0002824768,0.0000039205124,0.90726924,0.014514418,0.06637275],"study_design_scores_gemma":[0.0016566468,0.000021730522,0.028159976,0.0016667759,0.00029702173,0.000005320657,0.0012062232,0.00024350136,0.0000058850974,0.028259229,0.9364683,0.0020094055],"about_ca_topic_score_codex":0.00025271214,"about_ca_topic_score_gemma":0.0013294414,"teacher_disagreement_score":0.92195386,"about_ca_system_score_codex":0.0010649072,"about_ca_system_score_gemma":0.00001405486,"threshold_uncertainty_score":0.99980843},"labels":[],"label_agreement":null},{"id":"W4235755322","doi":"10.1007/978-3-030-01863-4","title":"Sharing Economy","year":2019,"lang":"en","type":"book","venue":"Springer series in supply chain management","topic":"Sharing Economy and Platforms","field":"Business, Management and Accounting","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sharing economy; Crowdsourcing; Consumption (sociology); Context (archaeology); Perspective (graphical); Business; Production (economics); Economy; Knowledge management; Industrial organization; Economic system; Economics; Computer science; Microeconomics; Geography; Sociology; World Wide Web; Social science","score_opus":0.01234234394046273,"score_gpt":0.19290606479980446,"score_spread":0.18056372085934172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235755322","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005624474,0.00027126743,0.000054164047,0.00047339435,0.001979887,0.0012780776,0.00000763549,0.00026502347,0.9951081],"genre_scores_gemma":[0.0062589706,0.00019335789,0.00031913762,0.002525215,0.0023412162,0.00023683088,0.00041532132,0.00019649866,0.9875134],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.997019,0.000003339704,0.0007813125,0.0011619116,0.00024486438,0.0007895512],"domain_scores_gemma":[0.9982413,0.000031409574,0.00046428994,0.0011916659,0.000047968973,0.00002335823],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00069817033,0.00071901135,0.00075804983,0.0012481313,0.0001315723,0.0008234092,0.0013150559,0.0003372258,0.0030252053],"category_scores_gemma":[0.000011519506,0.00079141004,0.00024195126,0.0002363886,0.00008302798,0.002155033,0.0021296723,0.000611852,0.0051486725],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008395642,0.000051101713,0.014520792,0.0041087167,0.00026857312,0.00021967989,0.000079199024,0.0005316368,3.019746e-7,0.9092412,0.0628113,0.008083571],"study_design_scores_gemma":[0.0005471098,0.000009127359,0.0016369169,0.00061048736,0.00006985153,0.0000020847895,0.00011387728,0.00048533746,0.000001653783,0.09992831,0.8957321,0.00086316274],"about_ca_topic_score_codex":0.00011721617,"about_ca_topic_score_gemma":0.0002896778,"teacher_disagreement_score":0.8329208,"about_ca_system_score_codex":0.00043897695,"about_ca_system_score_gemma":0.00004624132,"threshold_uncertainty_score":0.99945366},"labels":[],"label_agreement":null},{"id":"W4285379626","doi":"10.1007/978-3-030-81945-3_4","title":"Impact of Blockchain-Driven Accountability in Multi-Sourcing Supply Chains","year":2021,"lang":"en","type":"book-chapter","venue":"Springer series in supply chain management","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Accountability; Supply chain; Business; Quality (philosophy); Payment; Industrial organization; Marketing; Finance","score_opus":0.027687210224089823,"score_gpt":0.255779153346721,"score_spread":0.2280919431226312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285379626","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1577836,0.0025893173,0.0004976028,0.0015630232,0.0037387302,0.00867244,0.0002204896,0.00064668234,0.82428813],"genre_scores_gemma":[0.7785477,0.0010848433,0.0017205114,0.00086073385,0.0011348788,0.00046908137,0.00057109265,0.00051629625,0.21509486],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9931003,0.000063152525,0.0021950663,0.0019586305,0.0012418999,0.0014409723],"domain_scores_gemma":[0.9963905,0.00008732788,0.0010877285,0.0020714481,0.00028488884,0.00007806854],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0015897725,0.0015450438,0.0019370088,0.0031393159,0.00016821493,0.00036135942,0.0015452281,0.00057158445,0.004759012],"category_scores_gemma":[0.00008312926,0.0016638483,0.00097570237,0.00085307827,0.0004224028,0.00080564676,0.0030354778,0.0010671552,0.0001757079],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012958805,0.002694094,0.36004612,0.0117201805,0.0025283985,0.003011165,0.0025185486,0.029015625,0.000175702,0.56211203,0.0069355317,0.017946715],"study_design_scores_gemma":[0.016702361,0.00047549582,0.41198364,0.013956586,0.0013008033,0.000025761292,0.010570809,0.031131642,0.00009932445,0.01419082,0.48932326,0.010239498],"about_ca_topic_score_codex":0.0028870995,"about_ca_topic_score_gemma":0.006651258,"teacher_disagreement_score":0.6207641,"about_ca_system_score_codex":0.0014662255,"about_ca_system_score_gemma":0.000103139486,"threshold_uncertainty_score":0.9997298},"labels":[],"label_agreement":null},{"id":"W4394676702","doi":"10.1007/978-3-031-45565-0_3","title":"Carbon Footprinting in Supply Chains: Measurement, Reporting, and Disclosure","year":2024,"lang":"en","type":"book-chapter","venue":"Springer series in supply chain management","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Footprinting; Supply chain; Scope (computer science); Greenhouse gas; Automatic summarization; Environmental economics; Carbon footprint; Carbon fibers; Business; Computer science; Economics; Chemistry; Marketing","score_opus":0.013988999713109008,"score_gpt":0.22207597095516762,"score_spread":0.2080869712420586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394676702","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16867022,0.0015798406,0.000010520179,0.0010984698,0.00045737642,0.0021449686,0.000008169689,0.0001315929,0.8258988],"genre_scores_gemma":[0.6982041,0.0012687277,0.00042559154,0.00009562435,0.000081590304,0.00017525634,0.000011609138,0.00014091736,0.29959655],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.995024,0.000050998824,0.0015712236,0.0014300065,0.0010621841,0.00086155307],"domain_scores_gemma":[0.99829686,0.000020489377,0.00058765954,0.00091800495,0.000008396995,0.00016857384],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0026409137,0.0007692007,0.0007355716,0.00034252577,0.00009570488,0.00013547995,0.00042386088,0.0003035961,0.0006112095],"category_scores_gemma":[0.0000852297,0.0007856355,0.00016737507,0.00016913582,0.000526129,0.00024100773,0.0023855723,0.0008303125,0.000035239416],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000117239375,0.00017551029,0.9213758,0.002077649,0.00020072503,0.0029479782,0.0035422412,0.000503596,0.00020612264,0.044915173,0.00032066187,0.023617279],"study_design_scores_gemma":[0.0011897453,0.0002956875,0.7058811,0.0024354637,0.0002113465,0.00004838406,0.0035473108,0.0002854529,0.00025714614,0.055249196,0.2280235,0.0025756473],"about_ca_topic_score_codex":0.00085710443,"about_ca_topic_score_gemma":0.0034624746,"teacher_disagreement_score":0.5295339,"about_ca_system_score_codex":0.0023649607,"about_ca_system_score_gemma":0.000015909905,"threshold_uncertainty_score":0.99945945},"labels":[],"label_agreement":null},{"id":"W4394676825","doi":"10.1007/978-3-031-45565-0_12","title":"Green Technology Choice for Deep Decarbonization","year":2024,"lang":"en","type":"book-chapter","venue":"Springer series in supply chain management","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Materials science; Process engineering; Environmental science; Engineering","score_opus":0.005968135814540829,"score_gpt":0.2189129426749159,"score_spread":0.21294480686037506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394676825","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003155075,0.0011834058,0.0013860989,0.0031680884,0.0007099866,0.0039558313,0.000047494945,0.00032587227,0.9860681],"genre_scores_gemma":[0.036107358,0.0006498907,0.0037630028,0.00022931391,0.00012128544,0.0004728661,0.00008680874,0.00016222622,0.9584072],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.997854,0.000010010564,0.00043783206,0.0008371567,0.00034114593,0.000519889],"domain_scores_gemma":[0.99908686,0.000027914935,0.00010743554,0.0006920503,0.000006166923,0.00007960419],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029988083,0.0004570663,0.00035192058,0.0003098397,0.0000940394,0.000052144635,0.0004981398,0.00034097568,0.0018836125],"category_scores_gemma":[0.000015533484,0.00048123204,0.0001322187,0.00016629165,0.00043703083,0.00020194912,0.0013131829,0.00031628882,0.00032091627],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001963539,0.00022355778,0.061825752,0.00267018,0.00043173967,0.0004714883,0.0014420913,0.0027814028,0.00009604322,0.73841304,0.0027729021,0.18867543],"study_design_scores_gemma":[0.00030608295,0.00012944294,0.004307915,0.00011448442,0.00009151779,0.0000037123443,0.00027943958,0.00036018586,0.00006110287,0.1808576,0.81290764,0.00058086874],"about_ca_topic_score_codex":0.00011279486,"about_ca_topic_score_gemma":0.0012349818,"teacher_disagreement_score":0.81013477,"about_ca_system_score_codex":0.0013931823,"about_ca_system_score_gemma":0.0000057443,"threshold_uncertainty_score":0.9997639},"labels":[],"label_agreement":null},{"id":"W4400726035","doi":"10.1007/978-3-031-60867-4_11","title":"Business Model Innovation for Ambulance Systems in Low- and Middle-Income Countries","year":2024,"lang":"en","type":"book-chapter","venue":"Springer series in supply chain management","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Business; Low and middle income countries; Middle income; Economic growth; Economics; Demographic economics; Developing country","score_opus":0.012928213389732093,"score_gpt":0.21409896352923125,"score_spread":0.20117075013949914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400726035","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09351624,0.023270953,0.19865768,0.00752165,0.01871492,0.03312841,0.0052262777,0.005084508,0.61487937],"genre_scores_gemma":[0.659869,0.0054028635,0.005667816,0.00021576195,0.00026968247,0.002716195,0.0010244878,0.00039783592,0.32443634],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99842954,0.000002526298,0.00074977626,0.0003716758,0.00019259435,0.00025390042],"domain_scores_gemma":[0.9994705,0.000020568246,0.00006794621,0.00029617353,0.00012455117,0.000020289175],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028019876,0.00033681944,0.00036106573,0.00096784194,0.00003400321,0.000104188424,0.00013809706,0.00018201096,0.000014542262],"category_scores_gemma":[0.000004389285,0.00039595962,0.000028906934,0.0003452082,0.00007398823,0.00020258843,0.00004466293,0.00022008237,0.000008489177],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022317998,0.0000060629554,0.00017662614,0.013334898,0.000066550994,0.000019980456,0.00033897324,0.13408716,0.000004050105,0.8517291,0.00006996819,0.00014431184],"study_design_scores_gemma":[0.0033824989,0.00008684891,0.020707248,0.035305057,0.0002767909,0.000010520897,0.0010440117,0.30610642,0.000077202625,0.13327233,0.4960932,0.0036378312],"about_ca_topic_score_codex":0.000016476175,"about_ca_topic_score_gemma":0.00043898122,"teacher_disagreement_score":0.71845675,"about_ca_system_score_codex":0.00023364919,"about_ca_system_score_gemma":0.000020728821,"threshold_uncertainty_score":0.9998492},"labels":[],"label_agreement":null},{"id":"W4408998909","doi":"10.1007/978-3-031-74994-0_11","title":"Transportation Problems in Humanitarian Supply Chains","year":2025,"lang":"en","type":"book-chapter","venue":"Springer series in supply chain management","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Supply chain; Business; Marketing","score_opus":0.014471017437259339,"score_gpt":0.20432424068045288,"score_spread":0.18985322324319354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408998909","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008840431,0.0005615829,0.0005496325,0.0045069614,0.0021126787,0.0039157327,0.000056459678,0.0003594448,0.98705345],"genre_scores_gemma":[0.11218262,0.001961335,0.0004881176,0.0022825983,0.0005717154,0.00085855776,0.0016616466,0.00017095442,0.87982243],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959406,0.000015945945,0.0013850539,0.0012093539,0.0006986108,0.0007504342],"domain_scores_gemma":[0.99864596,0.000014724015,0.0002468156,0.00094949175,0.00011563205,0.000027394502],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00085090124,0.0008336814,0.00074063183,0.0025533733,0.0001663477,0.00025803858,0.0008586101,0.00030707053,0.0027595514],"category_scores_gemma":[0.000017087827,0.0009924078,0.00023530399,0.0005339684,0.00013636956,0.0009836955,0.00037504188,0.00056992465,0.00046034766],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000671388,0.0001597948,0.004158326,0.004116317,0.00014687444,0.0001334917,0.0004104978,0.004279566,0.0000019463494,0.9687583,0.0077664405,0.010001327],"study_design_scores_gemma":[0.0010951697,0.000023934184,0.025814492,0.0014619604,0.0001422859,2.1529607e-7,0.00063819834,0.00095466716,0.000001562378,0.028353184,0.94041544,0.0010989165],"about_ca_topic_score_codex":0.0023393536,"about_ca_topic_score_gemma":0.07131538,"teacher_disagreement_score":0.9404051,"about_ca_system_score_codex":0.0004126614,"about_ca_system_score_gemma":0.000034142915,"threshold_uncertainty_score":0.9992526},"labels":[],"label_agreement":null},{"id":"W4417181549","doi":"10.1007/978-3-032-07054-8_11","title":"Reimagining Supply Chain Planning Using Machine Learning: A Roadmap to Agility and Resilience","year":2025,"lang":"en","type":"book-chapter","venue":"Springer series in supply chain management","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Supply chain; Resilience (materials science); Supply chain management; Volatility (finance); Supply chain risk management; Scenario planning; Demand forecasting; Sales and operations planning; Decision support system","score_opus":0.014606741285009092,"score_gpt":0.2440926389462294,"score_spread":0.2294858976612203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417181549","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013867582,0.006444909,0.00530855,0.011243225,0.0041141063,0.008952004,0.000076376666,0.0013836394,0.9486096],"genre_scores_gemma":[0.17874873,0.0025863496,0.01393298,0.005830615,0.0021396552,0.00057895953,0.00038287955,0.0005418428,0.795258],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99360096,0.00005768484,0.0013895537,0.0023500505,0.0011560096,0.0014457167],"domain_scores_gemma":[0.9976267,0.00012096355,0.0006416601,0.0013302616,0.00016680793,0.000113569775],"candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0018915184,0.0013990229,0.0013007104,0.0031217758,0.0007050307,0.00089324504,0.001355308,0.00037394898,0.00057841185],"category_scores_gemma":[0.00016332541,0.0013663786,0.00025985908,0.00082520506,0.00039393807,0.0012211269,0.0045168367,0.0013936184,0.0001396169],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015275291,0.00032024478,0.19498053,0.010193659,0.0010152668,0.0049443454,0.0024213376,0.040857647,0.00012017007,0.63699776,0.014746799,0.0918747],"study_design_scores_gemma":[0.0012898897,0.00008960634,0.007954178,0.0042667636,0.00036748537,0.000013333465,0.002208621,0.016568791,0.000022769247,0.013110105,0.951803,0.0023054157],"about_ca_topic_score_codex":0.0016212584,"about_ca_topic_score_gemma":0.00070621015,"teacher_disagreement_score":0.93705624,"about_ca_system_score_codex":0.0004739278,"about_ca_system_score_gemma":0.00006739501,"threshold_uncertainty_score":0.999876},"labels":[],"label_agreement":null},{"id":"W7113895435","doi":"10.1007/978-3-032-07054-8_19","title":"AI May Be Ready for Supply Chains But Are Supply Chains Ready for AI?","year":2025,"lang":"en","type":"book-chapter","venue":"Springer series in supply chain management","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cisco Systems (Canada)","funders":"","keywords":"Supply chain; Transformative learning; Workforce; Process (computing); Key (lock); Investment (military); Analytics; Supply chain management","score_opus":0.02556547036006148,"score_gpt":0.2671226717707208,"score_spread":0.2415572014106593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7113895435","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000793656,0.0037455738,0.017277397,0.143188,0.01562022,0.047816418,0.0056418837,0.0027438137,0.76317304],"genre_scores_gemma":[0.012708669,0.0035088188,0.0030202726,0.029965278,0.0043566814,0.0069726375,0.0054665813,0.00073056977,0.9332705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9897812,0.000045437922,0.0024480897,0.0033866914,0.0015835167,0.0027550664],"domain_scores_gemma":[0.9944322,0.00032104796,0.00144675,0.0028890239,0.0007634566,0.00014751154],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0020708295,0.0025228402,0.0024686072,0.0037550577,0.0010154541,0.0013192631,0.002871063,0.0010410496,0.0010330887],"category_scores_gemma":[0.00017177306,0.0026248859,0.0010504737,0.0007018871,0.000600525,0.0017991809,0.0026903802,0.0011635283,0.00026710858],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080072705,0.00026803595,0.0025098708,0.006536384,0.0007522514,0.0002781492,0.0001834893,0.0004258907,0.000007787204,0.6956462,0.28160456,0.0109866075],"study_design_scores_gemma":[0.003502313,0.00018971718,0.0012081307,0.0021490594,0.00092461886,0.0000041184408,0.0024202974,0.002281973,0.00004421617,0.022919085,0.9616195,0.0027369824],"about_ca_topic_score_codex":0.0007653908,"about_ca_topic_score_gemma":0.0038458002,"teacher_disagreement_score":0.6800149,"about_ca_system_score_codex":0.00088153844,"about_ca_system_score_gemma":0.00016236589,"threshold_uncertainty_score":0.9998801},"labels":[],"label_agreement":null}]}