{"meta":{"query_hash":"3f810e5db5d1","filters":{"venue":"Materials Genome Engineering Advances"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"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/3f810e5db5d1","api":"https://metacan.xera.ac/api/v1/cohort?venue=Materials+Genome+Engineering+Advances"},"results":[{"id":"W4386544158","doi":"10.1002/mgea.4","title":"Atomistic simulations of nucleation and growth of CaCO<sub>3</sub> with the influence of inhibitors: A review","year":2023,"lang":"en","type":"review","venue":"Materials Genome Engineering Advances","topic":"Calcium Carbonate Crystallization and Inhibition","field":"Materials Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Context (archaeology); Biochemical engineering; Nucleation; Mechanism (biology); Biomineralization; Computer science; Scaling; Scale (ratio); Relevance (law); Nanotechnology; Chemistry; Data science; Materials science; Physics; Biology; Engineering; Chemical engineering; Mathematics","score_opus":0.012795812490797719,"score_gpt":0.24776386307017328,"score_spread":0.23496805057937556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386544158","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020742097,0.9892497,0.00409233,0.0003972663,0.0002637098,0.000013584311,0.0001499185,0.00007734038,0.0036819016],"genre_scores_gemma":[0.008202041,0.98771477,0.0024597065,0.0001292017,0.00012403588,0.00003656503,0.0001840824,0.000028319122,0.0011212736],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998746,0.00002423946,0.000013846096,0.000022292816,0.000051570336,0.000013451091],"domain_scores_gemma":[0.9997278,0.00014500576,0.00002313307,0.000010290124,0.000077666264,0.000016076801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003554969,0.0009554779,0.0015347784,0.0009060571,0.00028192412,0.0006025284,0.0010910742,0.0010332199,0.001872739],"category_scores_gemma":[0.00069942384,0.0004878815,0.00082320836,0.0017292448,0.000300252,0.00072741933,0.0005906448,0.00068874354,0.0012304491],"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":[0.00016710104,0.00016523933,0.0007105509,0.041175846,0.0005222231,0.00024250557,0.00016778594,0.077484705,0.0073386985,0.029838696,0.03950283,0.8026839],"study_design_scores_gemma":[0.00007337028,0.00025862,0.0011881702,0.0050492897,0.00051051565,0.00041729124,0.00011321309,0.048710037,0.007878181,0.01647095,0.9192162,0.00011424346],"about_ca_topic_score_codex":0.0024949543,"about_ca_topic_score_gemma":0.0023389037,"teacher_disagreement_score":0.0024949543,"about_ca_system_score_codex":0.0004897381,"about_ca_system_score_gemma":0.00097435946,"threshold_uncertainty_score":0.0062649846},"labels":[],"label_agreement":null},{"id":"W4386557891","doi":"10.1002/mgea.9","title":"<i>Materials Genome Engineering Advances</i>: A new journal dedicated to digital and intelligent materials research and development","year":2023,"lang":"en","type":"article","venue":"Materials Genome Engineering Advances","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pace; Big data; Computer science; Data science; Software deployment; Nanotechnology; Software engineering; Materials science","score_opus":0.03104728505781565,"score_gpt":0.26734603760584624,"score_spread":0.2362987525480306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386557891","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024509054,0.021025322,0.0015948308,0.05397666,0.90990967,0.000044878703,0.00019717707,0.00054834125,0.0124579705],"genre_scores_gemma":[0.0034515944,0.028644294,0.0017367108,0.054667313,0.84872156,0.00007557869,0.00047473167,0.00083139725,0.061396863],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959239,0.000505836,0.00041366467,0.0005849117,0.0021678233,0.00040401937],"domain_scores_gemma":[0.98336107,0.0045244438,0.001072523,0.0009283569,0.006191307,0.003922352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041239588,0.0016259019,0.0020617268,0.0035204682,0.0026851746,0.015806882,0.0027382933,0.006242167,0.024411783],"category_scores_gemma":[0.010540747,0.0006586761,0.0012690768,0.0023165925,0.0033743712,0.0051092627,0.0016389092,0.010497181,0.024663296],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030214913,0.000019326111,0.000061410465,0.00027273802,0.000007895121,0.0000480934,0.000027617294,0.000039432485,0.0005411328,0.0022246086,0.97895026,0.017777182],"study_design_scores_gemma":[0.000004952741,0.000030481644,0.00011474421,0.00013037722,0.000007785843,0.000101430254,0.000036075613,0.00010814796,0.00026254318,0.001158555,0.99803287,0.000012139178],"about_ca_topic_score_codex":0.0005212384,"about_ca_topic_score_gemma":0.001144414,"teacher_disagreement_score":0.024411783,"about_ca_system_score_codex":0.0022159985,"about_ca_system_score_gemma":0.0041302927,"threshold_uncertainty_score":0.081665516},"labels":[],"label_agreement":null},{"id":"W4414378015","doi":"10.1002/mgea.70030","title":"Machine learning‐based research of new refractory high‐entropy alloys using guided multiobjectives search strategy","year":2025,"lang":"en","type":"article","venue":"Materials Genome Engineering Advances","topic":"High Entropy Alloys Studies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Refractory (planetary science); Particle swarm optimization; Refractory metals; Alloy; Space (punctuation); Titanium alloy; Variety (cybernetics)","score_opus":0.05654353701885354,"score_gpt":0.3297832604771596,"score_spread":0.27323972345830605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414378015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39766,0.0013966857,0.5911809,0.00047841982,0.000061072045,0.00010318393,0.00007214599,0.00031429387,0.0087333],"genre_scores_gemma":[0.8845005,0.000268034,0.11292309,0.00006777147,0.000017764629,0.0001061699,0.000066310866,0.000032306212,0.0020180843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998671,0.00003520416,0.0000070972173,0.000025110283,0.00004614992,0.000019301511],"domain_scores_gemma":[0.9997402,0.000118476055,0.00005383881,0.000017899103,0.000047779307,0.000021720092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074680755,0.0006434124,0.0006904727,0.0009556054,0.00031811235,0.00069142424,0.00063019746,0.00067470886,0.0007630723],"category_scores_gemma":[0.00080389326,0.0003928416,0.0006853407,0.00039267878,0.0005058202,0.00049089995,0.0004907806,0.00041440345,0.00012510289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026030524,0.000043654243,0.00087396667,0.00005618701,0.000032352345,0.000047685207,0.000020381673,0.9702444,0.00941759,0.005934891,0.00015837027,0.013144479],"study_design_scores_gemma":[0.0000024789779,0.000014098124,0.000060259907,0.0000020734808,0.0000027627525,0.0000038083724,0.0000026262348,0.9987049,0.0006394164,0.0004553665,0.00011075294,0.0000014882941],"about_ca_topic_score_codex":0.0023153322,"about_ca_topic_score_gemma":0.0022688857,"teacher_disagreement_score":0.0023153322,"about_ca_system_score_codex":0.0007560772,"about_ca_system_score_gemma":0.0007787926,"threshold_uncertainty_score":0.0054857135},"labels":[],"label_agreement":null}]}