{"meta":{"query_hash":"7b3a72ac1f38","filters":{"topic":"Domain Adaptation and Few-Shot Learning"},"cohort_total":705,"direct_labels_cover":0,"predictions_cover":705,"exported":705,"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/7b3a72ac1f38","api":"https://metacan.xera.ac/api/v1/cohort?topic=Domain+Adaptation+and+Few-Shot+Learning"},"results":[{"id":"W110522552","doi":"","title":"Machine Life-Long Learning with csMTL Networks.","year":2006,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Artificial intelligence; Inductive transfer; Machine learning; Multi-task learning; Inductive bias; Artificial neural network; Transfer of learning; Task (project management); Backpropagation; Context (archaeology); Robot learning; Engineering","score_opus":0.006116951233803086,"score_gpt":0.1928618586921687,"score_spread":0.1867449074583656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W110522552","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010889759,0.0009436685,0.9820503,0.00053698075,0.00013140231,0.00009538511,0.00013981527,0.0016843387,0.0035283763],"genre_scores_gemma":[0.37896457,0.0005711151,0.6132945,0.00061208755,0.00013197161,0.00029425806,0.00048284282,0.00023556814,0.0054131323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992385,0.00032989477,0.00003341193,0.00020150394,0.0001568931,0.000039923758],"domain_scores_gemma":[0.9974796,0.0013827324,0.00014194519,0.00054362067,0.00033721887,0.00011481224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001831586,0.00060723384,0.00047601445,0.00061738014,0.00040596235,0.0009404612,0.0023558864,0.0014334946,0.003994425],"category_scores_gemma":[0.008299734,0.0004309519,0.00055073574,0.00070899713,0.0008208124,0.0030301071,0.0024124873,0.0021685667,0.0013217039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015006553,0.00019868248,0.0018595087,0.000252949,0.00012786867,0.00022299988,0.0002295831,0.43564755,0.007535584,0.05187879,0.010689738,0.4912066],"study_design_scores_gemma":[0.000003937529,0.000020341911,0.00010768334,0.000008393319,0.0000048573843,0.000027507518,0.00000752493,0.9806487,0.0012429296,0.015941743,0.0019806188,0.000005905568],"about_ca_topic_score_codex":0.0020647931,"about_ca_topic_score_gemma":0.0048130997,"teacher_disagreement_score":0.003994425,"about_ca_system_score_codex":0.00119817,"about_ca_system_score_gemma":0.0006217732,"threshold_uncertainty_score":0.013362706},"labels":[],"label_agreement":null},{"id":"W113882479","doi":"10.1007/978-3-642-21043-3_16","title":"Consolidation Using Context-Sensitive Multiple Task Learning","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"Acadia University","funders":"","keywords":"Computer science; Consolidation (business); Artificial intelligence; Transfer of learning; Task (project management); Knowledge transfer; Context (archaeology); Machine learning; Knowledge management; Engineering","score_opus":0.03743050568410767,"score_gpt":0.25366410986390914,"score_spread":0.21623360417980148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W113882479","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017378457,0.0007060019,0.97661906,0.00014889103,0.00027634433,0.00012617819,0.00011778183,0.003013754,0.0016134926],"genre_scores_gemma":[0.58727604,0.0007328697,0.40194756,0.00043393899,0.0003490078,0.00047545065,0.0012264594,0.00074504997,0.006813644],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880385,0.000294371,0.000095455325,0.0004815058,0.0001889118,0.0001357698],"domain_scores_gemma":[0.9950788,0.002389035,0.00023668156,0.0012310342,0.00074522186,0.00031931768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024353839,0.0016061119,0.002541683,0.001031226,0.0007151963,0.0019680704,0.0041401563,0.0016438492,0.0057743937],"category_scores_gemma":[0.00755284,0.0010405697,0.001150077,0.0017327772,0.0008096444,0.0047641764,0.004091484,0.0039286823,0.0025255745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005255297,0.00058419904,0.00080671645,0.00020956414,0.00020996964,0.00011004649,0.00013771578,0.056929942,0.032014802,0.0060759545,0.0061200005,0.8962757],"study_design_scores_gemma":[0.000046417248,0.00016706297,0.000439051,0.00001954404,0.00005978292,0.00008125198,0.000029052077,0.9588152,0.015209482,0.023488035,0.0016089666,0.000036108308],"about_ca_topic_score_codex":0.0018863488,"about_ca_topic_score_gemma":0.0021040125,"teacher_disagreement_score":0.0057743937,"about_ca_system_score_codex":0.0005712003,"about_ca_system_score_gemma":0.0012635943,"threshold_uncertainty_score":0.019317329},"labels":[],"label_agreement":null},{"id":"W119215678","doi":"10.1007/978-3-642-30353-1_17","title":"Image Morphing: Transfer Learning between Tasks That Have Multiple Outputs","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Acadia University","funders":"","keywords":"Computer science; Transfer of learning; Morphing; Artificial intelligence; Task (project management); Artificial neural network; Machine learning; Context (archaeology); Multiplicative function; Multi-task learning; Transformation (genetics); Domain (mathematical analysis); Image (mathematics); Inductive transfer; Pattern recognition (psychology); Mathematics","score_opus":0.03987737130813371,"score_gpt":0.257223916041184,"score_spread":0.2173465447330503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W119215678","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010694815,0.00031464983,0.981047,0.00012852791,0.00009580239,0.00009208262,0.00012421586,0.0056434567,0.0018595357],"genre_scores_gemma":[0.24730383,0.00048304812,0.7363895,0.00036609458,0.00012670727,0.00030929968,0.0012678282,0.0011179884,0.012635649],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996232,0.00008637521,0.000016494449,0.00016451474,0.0000685602,0.000040916835],"domain_scores_gemma":[0.9994153,0.00024525294,0.000026782882,0.00020552833,0.000068026784,0.000039079285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010691177,0.0010723844,0.0009105821,0.0004608595,0.00025483847,0.0006712881,0.0024651273,0.0017260851,0.0060814293],"category_scores_gemma":[0.001934471,0.00052900554,0.00094882696,0.00068867503,0.0007214335,0.0022641714,0.0024606897,0.0020043708,0.0023951796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002209397,0.00024886781,0.00036897618,0.00021190781,0.00011303033,0.0001150647,0.00009021065,0.06234504,0.032692432,0.0060837255,0.010315878,0.88719386],"study_design_scores_gemma":[0.000027705915,0.00012564508,0.00057655363,0.000017761186,0.00003449955,0.0001662287,0.000038065424,0.93816376,0.02604046,0.030720105,0.0040666945,0.000022579876],"about_ca_topic_score_codex":0.0011745755,"about_ca_topic_score_gemma":0.0013174703,"teacher_disagreement_score":0.0060814293,"about_ca_system_score_codex":0.00039646274,"about_ca_system_score_gemma":0.0004428259,"threshold_uncertainty_score":0.020344436},"labels":[],"label_agreement":null},{"id":"W1526028571","doi":"10.1007/3-540-44886-1_16","title":"Selective Transfer of Task Knowledge Using Stochastic Noise","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Generalization; Task (project management); Context (archaeology); Multi-task learning; Artificial intelligence; Noise (video); Artificial neural network; Transfer of learning; Machine learning; Mathematics","score_opus":0.024703247331107212,"score_gpt":0.2606277837755209,"score_spread":0.23592453644441372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1526028571","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":[0.06942191,0.0005399613,0.923226,0.00029286076,0.00015291273,0.000108959925,0.00009686069,0.0014722095,0.004688333],"genre_scores_gemma":[0.8916927,0.0006425377,0.09546128,0.00039884564,0.00013734505,0.00021265094,0.0005008508,0.00029189998,0.010661955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992611,0.00019336902,0.00003548691,0.0002636546,0.00015129843,0.00009514105],"domain_scores_gemma":[0.9962657,0.0024576685,0.00015030096,0.0007110228,0.00027689908,0.00013825868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015895799,0.0009717317,0.0011981255,0.0004630591,0.00033974217,0.0010466923,0.0014137418,0.0012768722,0.002116636],"category_scores_gemma":[0.009525499,0.0005797509,0.00080312527,0.0006401806,0.0008594512,0.002559703,0.0030268405,0.0021887335,0.0010714914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006333875,0.00071839,0.0017577811,0.00033104085,0.0003310256,0.00025548882,0.0005053625,0.23534088,0.07604909,0.016173186,0.006007736,0.6618966],"study_design_scores_gemma":[0.00002906544,0.00016120086,0.0014618242,0.000024221708,0.00007267223,0.00012880962,0.00006265467,0.947026,0.018641392,0.030970741,0.0013896853,0.000031605316],"about_ca_topic_score_codex":0.0015277766,"about_ca_topic_score_gemma":0.0016127796,"teacher_disagreement_score":0.002116636,"about_ca_system_score_codex":0.0004736662,"about_ca_system_score_gemma":0.0007429488,"threshold_uncertainty_score":0.008406639},"labels":[],"label_agreement":null},{"id":"W1558298672","doi":"10.1007/978-3-540-68825-9_28","title":"Image Transformation: Inductive Transfer between Multiple Tasks Having Multiple Outputs","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Transfer of learning; Computer science; Inductive transfer; Task (project management); Transformation (genetics); Inductive bias; Artificial intelligence; Multi-task learning; Context (archaeology); Scalar (mathematics); Artificial neural network; Domain (mathematical analysis); Image (mathematics); Machine learning; Transfer (computing); Pattern recognition (psychology); Mathematics","score_opus":0.03240238157992148,"score_gpt":0.25012021528536144,"score_spread":0.21771783370543996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1558298672","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":[0.006631381,0.00014187342,0.9877659,0.00010329576,0.00007189761,0.00007737181,0.0000842075,0.0032231172,0.0019009257],"genre_scores_gemma":[0.34634387,0.00036905555,0.6344084,0.00040259465,0.0001694384,0.00043323726,0.0012131705,0.0008693856,0.015790826],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994154,0.00014182965,0.000022732704,0.00023409369,0.000112053196,0.000073885494],"domain_scores_gemma":[0.9992192,0.00033758974,0.000039847902,0.000235194,0.00012211691,0.000046013563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012877415,0.00096340914,0.00082527247,0.0005854133,0.00037414877,0.0008043549,0.002242072,0.0016196034,0.0064697424],"category_scores_gemma":[0.0023626394,0.0005428011,0.0010902985,0.0009062721,0.00077977614,0.0021705674,0.0029661818,0.0021540595,0.003465931],"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":[0.0003438917,0.0003799696,0.0005680717,0.00023422409,0.00016763892,0.0002107437,0.0001933242,0.06292448,0.056255758,0.010305508,0.010212227,0.8582042],"study_design_scores_gemma":[0.000045798806,0.00015839809,0.00069054,0.000025980406,0.0000668438,0.00023497644,0.00008873252,0.8986278,0.05551555,0.039017934,0.005497383,0.000030085072],"about_ca_topic_score_codex":0.0016033975,"about_ca_topic_score_gemma":0.0017603617,"teacher_disagreement_score":0.0064697424,"about_ca_system_score_codex":0.00044670823,"about_ca_system_score_gemma":0.0006075537,"threshold_uncertainty_score":0.02164352},"labels":[],"label_agreement":null},{"id":"W1575148107","doi":"10.1109/ijcnn.2005.1556228","title":"Effect of curriculum on the consolidation of neural network task knowledge","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Acadia University","funders":"","keywords":"Computer science; Memory consolidation; Domain knowledge; Curriculum; Knowledge transfer; Consolidation (business); Artificial neural network; Task (project management); Artificial intelligence; Task analysis; Procedural knowledge; Machine learning; Knowledge management; Psychology; Engineering; Pedagogy","score_opus":0.02479044076917084,"score_gpt":0.272771640684665,"score_spread":0.24798119991549414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1575148107","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98972964,0.00030688837,0.00893382,0.00007266874,0.00002188672,0.000037872225,0.000044927743,0.00010873182,0.0007434249],"genre_scores_gemma":[0.99361247,0.00020478894,0.005481223,0.000036670546,0.000007589279,0.000046643898,0.000112817586,0.000023870492,0.0004739676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995608,0.00014397585,0.000045778408,0.0000978561,0.00007429223,0.00007733418],"domain_scores_gemma":[0.99007976,0.0065110708,0.00121291,0.00097540265,0.0005771323,0.0006436439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001382997,0.00046186178,0.00050528004,0.00022640651,0.00020041855,0.00056044606,0.0005642357,0.0004454606,0.0015733668],"category_scores_gemma":[0.02174,0.0002408405,0.00017469736,0.00024564055,0.0005163796,0.0011373704,0.00090140855,0.00090698904,0.00019220167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007436176,0.0053715557,0.028545465,0.00083752687,0.00029375858,0.00025524158,0.00073125813,0.17112572,0.30331606,0.002645406,0.0011053089,0.47833648],"study_design_scores_gemma":[0.0007622843,0.015276282,0.06597676,0.00019974247,0.00044371517,0.00026154492,0.00037935964,0.6304484,0.27544385,0.008212334,0.0024862303,0.00010961636],"about_ca_topic_score_codex":0.0010704482,"about_ca_topic_score_gemma":0.0012207952,"teacher_disagreement_score":0.0015733668,"about_ca_system_score_codex":0.00043298633,"about_ca_system_score_gemma":0.0005846928,"threshold_uncertainty_score":0.0073140264},"labels":[],"label_agreement":null},{"id":"W1579705726","doi":"10.1007/3-540-47922-8_8","title":"The Task Rehearsal Method of Life-Long Learning: Overcoming Impoverished Data","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":127,"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; Acadia University","funders":"","keywords":"Computer science; Task (project management); Inductive bias; Multi-task learning; Artificial intelligence; Representation (politics); Artificial neural network; Domain knowledge; Recall; Inductive transfer; Machine learning; Domain (mathematical analysis); Cognitive psychology; Robot learning","score_opus":0.03726599607242419,"score_gpt":0.2864126542421482,"score_spread":0.24914665816972398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579705726","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018740438,0.0002885208,0.97644037,0.00017111583,0.00008916249,0.00009427247,0.00005946462,0.0025862902,0.0015303816],"genre_scores_gemma":[0.30826747,0.0002823102,0.68195504,0.0003105562,0.00009215829,0.00031575662,0.00048169337,0.00073050515,0.007564501],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897504,0.00045025971,0.00004789314,0.00024892256,0.00020667363,0.00007125903],"domain_scores_gemma":[0.99508554,0.0026932685,0.00018520953,0.0012406562,0.00059585215,0.00019954663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028980398,0.0006787058,0.00080467,0.0004158946,0.00045901968,0.0009229194,0.002655008,0.0009301002,0.0038128274],"category_scores_gemma":[0.009788445,0.00040778978,0.00037616532,0.00049209816,0.0006761366,0.002439862,0.0023664245,0.0021874176,0.0016632631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028822513,0.00031752783,0.00065542234,0.00014434344,0.00006135994,0.0000598193,0.00043621293,0.008297484,0.02089103,0.004080453,0.004765369,0.9600028],"study_design_scores_gemma":[0.0001284129,0.00065016287,0.0020539088,0.00005886621,0.00012037463,0.00041428595,0.00033951266,0.8884831,0.056774825,0.037561174,0.013333048,0.00008221089],"about_ca_topic_score_codex":0.001239908,"about_ca_topic_score_gemma":0.002281254,"teacher_disagreement_score":0.0038128274,"about_ca_system_score_codex":0.00025233056,"about_ca_system_score_gemma":0.0007408882,"threshold_uncertainty_score":0.01532644},"labels":[],"label_agreement":null},{"id":"W1593188841","doi":"10.1109/crv.2015.21","title":"Zero-Shot Object Recognition Using Semantic Label Vectors","year":2015,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba; Nvidia","keywords":"Object (grammar); Computer science; Artificial intelligence; Benchmark (surveying); Cognitive neuroscience of visual object recognition; Set (abstract data type); 3D single-object recognition; Pattern recognition (psychology); Zero (linguistics); Exploit; Method; Computer vision; Object-oriented programming","score_opus":0.18081393116364933,"score_gpt":0.3161115470926952,"score_spread":0.13529761592904585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1593188841","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050174497,0.0007047201,0.943938,0.00019245388,0.00011715408,0.00012866038,0.00031005952,0.0030706741,0.0013637741],"genre_scores_gemma":[0.6709407,0.0006457901,0.31959152,0.00040914136,0.00014399076,0.00021659426,0.0035272662,0.00023728408,0.004287773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985575,0.00023841512,0.00006119119,0.00067031523,0.00030652955,0.00016610773],"domain_scores_gemma":[0.99845624,0.00051364925,0.00016404365,0.00045681032,0.00029759735,0.000111678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012646592,0.0012155662,0.0019481037,0.0017076171,0.0005463593,0.0014259755,0.0037711777,0.0016090262,0.0015229355],"category_scores_gemma":[0.0034762328,0.00045110655,0.0011006572,0.0014885043,0.0014545843,0.0035982379,0.0020813816,0.0018831554,0.00095734914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053894677,0.0007200597,0.0032702638,0.00058562687,0.00020320843,0.0003586163,0.00039875717,0.078530036,0.04482784,0.009813935,0.0069153155,0.85383725],"study_design_scores_gemma":[0.000026442733,0.00030649384,0.0017190386,0.000033942586,0.000057604582,0.0003563592,0.00020489746,0.92933685,0.031125044,0.033942595,0.0028287095,0.000061956016],"about_ca_topic_score_codex":0.003433586,"about_ca_topic_score_gemma":0.003491493,"teacher_disagreement_score":0.0037711777,"about_ca_system_score_codex":0.0008321449,"about_ca_system_score_gemma":0.00081764837,"threshold_uncertainty_score":0.0068271756},"labels":[],"label_agreement":null},{"id":"W171557998","doi":"10.1007/978-3-642-34106-9_14","title":"On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":61,"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 Waterloo","funders":"","keywords":"Computer science; Realizability; Domain adaptation; Task (project management); Domain (mathematical analysis); Matching (statistics); Generalization; Artificial intelligence; Labeled data; Class (philosophy); Sample (material); Adaptation (eye); Dimension (graph theory); Function (biology); Concept class; Test data; Machine learning; Pattern recognition (psychology); Algorithm; Mathematics; Statistics; Classifier (UML)","score_opus":0.037028903000383764,"score_gpt":0.2421709051910609,"score_spread":0.20514200219067713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W171557998","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":[0.028608857,0.005260432,0.947241,0.0056135333,0.00019169257,0.00012008324,0.00052294775,0.0003588321,0.012082712],"genre_scores_gemma":[0.641255,0.009979898,0.3216396,0.0036077278,0.0023799608,0.0008277284,0.002172562,0.0009401387,0.01719734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9923718,0.003946234,0.00039793414,0.0016670139,0.0011643716,0.00045263534],"domain_scores_gemma":[0.8367797,0.15244494,0.0012878578,0.006576298,0.0018920824,0.001019063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013357656,0.0018887888,0.0050987825,0.0020853668,0.0019451624,0.004124695,0.005277823,0.0054390607,0.005359332],"category_scores_gemma":[0.07632598,0.0015746106,0.0026544768,0.0033264488,0.008678501,0.01696634,0.009564209,0.012202211,0.0008699185],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012894627,0.00031086526,0.0029281399,0.0013492039,0.00034764945,0.0004242714,0.0009869474,0.33371943,0.002848933,0.491299,0.019323735,0.1451724],"study_design_scores_gemma":[0.00006558982,0.00006702144,0.0006491721,0.00009797435,0.0000465168,0.00020834286,0.000080712336,0.4058876,0.00076324696,0.59049124,0.0016070749,0.00003540692],"about_ca_topic_score_codex":0.0046293205,"about_ca_topic_score_gemma":0.0024728607,"teacher_disagreement_score":0.013357656,"about_ca_system_score_codex":0.0025137772,"about_ca_system_score_gemma":0.0018531326,"threshold_uncertainty_score":0.07064289},"labels":[],"label_agreement":null},{"id":"W1844822759","doi":"10.1007/978-3-540-24840-8_16","title":"Sequential Consolidation of Learned Task Knowledge","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Domain knowledge; Task (project management); Knowledge transfer; Artificial intelligence; Artificial neural network; Machine learning; Multi-task learning; Consolidation (business); Knowledge management","score_opus":0.03235171012310735,"score_gpt":0.2822765319773342,"score_spread":0.24992482185422688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1844822759","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2893132,0.004016553,0.67163336,0.0013489316,0.001079451,0.00040514945,0.0011722543,0.0051172357,0.025913842],"genre_scores_gemma":[0.9230693,0.0011499064,0.055434905,0.00043004818,0.00021703745,0.00019430858,0.0024287798,0.0004729083,0.016602835],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99915195,0.00012659743,0.00005461789,0.00039674324,0.00016738052,0.00010271009],"domain_scores_gemma":[0.9947625,0.0023290243,0.00025465013,0.0014757746,0.0008219478,0.0003561788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012446406,0.0008627019,0.0011409036,0.00069318415,0.00042113147,0.0022737526,0.0019819848,0.0009992893,0.0071607814],"category_scores_gemma":[0.011715342,0.0008438649,0.00071944046,0.0010824038,0.0006868476,0.0050788755,0.00268842,0.0032048542,0.002684189],"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":[0.0007970712,0.00077011227,0.0048495834,0.00047083927,0.00033381433,0.0002573781,0.00057895936,0.027136937,0.07786047,0.014636312,0.009710324,0.8625981],"study_design_scores_gemma":[0.0001226125,0.0009002125,0.030952275,0.00023553873,0.00045906892,0.00065115816,0.00058850745,0.7111587,0.08213892,0.1561096,0.016510956,0.00017253068],"about_ca_topic_score_codex":0.002252998,"about_ca_topic_score_gemma":0.0026612657,"teacher_disagreement_score":0.0071607814,"about_ca_system_score_codex":0.00063210074,"about_ca_system_score_gemma":0.001122892,"threshold_uncertainty_score":0.023955226},"labels":[],"label_agreement":null},{"id":"W1853900790","doi":"10.48550/arxiv.1312.5663","title":"k-Sparse Autoencoders","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":148,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Autoencoder; MNIST database; Dropout (neural networks); Pattern recognition (psychology); Neural coding; Computer science; Artificial intelligence; Encoding (memory); Noise reduction; Sparse approximation; Machine learning; Algorithm; Deep learning","score_opus":0.09595176305524669,"score_gpt":0.18514676342265626,"score_spread":0.08919500036740957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1853900790","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013627663,0.0005333334,0.98323673,0.00018134952,0.0000508904,0.000031118707,0.00014332267,0.00061534677,0.0015802125],"genre_scores_gemma":[0.50045615,0.0014505468,0.48665273,0.0004495196,0.00019673991,0.00020364438,0.0016924375,0.00022220134,0.008675918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947757,0.0001683397,0.00003417492,0.00014337499,0.00012774127,0.000048787253],"domain_scores_gemma":[0.9984499,0.00082127843,0.00010718986,0.0003110371,0.00027094048,0.00003964966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092525664,0.00073079194,0.0010687282,0.0005951155,0.00028631173,0.00061470474,0.00096787314,0.0009631861,0.002124679],"category_scores_gemma":[0.0041214623,0.0004863924,0.00067942933,0.00076828705,0.0006907096,0.0014050418,0.00086564064,0.0013597471,0.0012434598],"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":[0.0001714234,0.0001388352,0.0013143556,0.0002639317,0.00017747509,0.00011621531,0.00015950965,0.59025514,0.009776404,0.048507977,0.010210984,0.33890778],"study_design_scores_gemma":[0.000005487563,0.000011736682,0.00017435341,0.000008305586,0.0000071173063,0.000023122151,0.000008131669,0.9877009,0.0010654768,0.010050801,0.00093981024,0.000004821964],"about_ca_topic_score_codex":0.002622939,"about_ca_topic_score_gemma":0.004460242,"teacher_disagreement_score":0.002622939,"about_ca_system_score_codex":0.00045526164,"about_ca_system_score_gemma":0.0006050233,"threshold_uncertainty_score":0.0071077347},"labels":[],"label_agreement":null},{"id":"W1885448067","doi":"10.48550/arxiv.1506.04573","title":"A New PAC-Bayesian Perspective on Domain Adaptation","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Bayesian probability; Divergence (linguistics); Perspective (graphical); Computer science; Domain (mathematical analysis); Generalization; Upper and lower bounds; Domain adaptation; Measure (data warehouse); Adaptation (eye); Generalization error; Artificial intelligence; Focus (optics); Machine learning; Econometrics; Data mining; Mathematics; Psychology","score_opus":0.09015163841440388,"score_gpt":0.2134027382900402,"score_spread":0.12325109987563632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1885448067","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001985586,0.00032891138,0.99460804,0.00068026886,0.00004143423,0.000020932584,0.00002885552,0.00007687361,0.0022291357],"genre_scores_gemma":[0.41796848,0.0019234419,0.56191576,0.0021695371,0.001275309,0.0005358634,0.00044589303,0.00053212955,0.013233561],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99437755,0.0028396905,0.00016791237,0.0011084093,0.0012259925,0.00028034826],"domain_scores_gemma":[0.9872967,0.008738092,0.0006487529,0.0016900239,0.0011575257,0.00046888887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00802864,0.0016648188,0.0018654861,0.001609112,0.001276403,0.0035239612,0.0036553459,0.0033471214,0.0043318146],"category_scores_gemma":[0.029877307,0.001042822,0.0014096247,0.0016778893,0.0042818277,0.008550262,0.0064611756,0.007943493,0.0013778338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011357606,0.00013314717,0.0010781655,0.00028244822,0.00016112451,0.00016745034,0.0005600052,0.22261484,0.0034525294,0.6893627,0.0059341057,0.0761399],"study_design_scores_gemma":[0.000016532325,0.000048951206,0.00020419479,0.00003839821,0.000020384621,0.00011107807,0.00004391491,0.5593764,0.0014332043,0.4347237,0.0039565093,0.000026718397],"about_ca_topic_score_codex":0.0014046644,"about_ca_topic_score_gemma":0.0010085428,"teacher_disagreement_score":0.00802864,"about_ca_system_score_codex":0.002049721,"about_ca_system_score_gemma":0.001398342,"threshold_uncertainty_score":0.042460024},"labels":[],"label_agreement":null},{"id":"W1947967435","doi":"10.1016/j.cortex.2018.12.009","title":"Accelerated long-term forgetting","year":2019,"lang":"en","type":"editorial","venue":"Cortex","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital; Toronto Rehabilitation Institute","funders":"","keywords":"Psychology; Forgetting; Term (time); Cognitive psychology","score_opus":0.022301837918572626,"score_gpt":0.28982178323326646,"score_spread":0.2675199453146938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1947967435","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":[0.00008559163,0.042460356,0.0020113515,0.06971608,0.8832521,0.000025811049,0.00014476015,0.00022333332,0.002080698],"genre_scores_gemma":[0.0018046836,0.019987212,0.0010119278,0.033298902,0.9261286,0.00004835531,0.00010311903,0.000116892865,0.017500307],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99760157,0.0005079319,0.00031491023,0.00036678117,0.0010597957,0.00014895834],"domain_scores_gemma":[0.98503083,0.006703666,0.00073112204,0.0005735073,0.0059408434,0.0010200882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061514527,0.0017920177,0.0023082923,0.0029831566,0.0011820709,0.004087163,0.0031808533,0.013606832,0.010109118],"category_scores_gemma":[0.022758748,0.0009940516,0.0015523155,0.0011564784,0.0027637149,0.0029625823,0.0014491852,0.01653524,0.008459451],"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":[0.000076477205,0.000012463076,0.000024146528,0.00042760538,0.00006900711,0.00009320283,0.0000070644805,0.000079154466,0.0001079863,0.0011838364,0.9658282,0.03209095],"study_design_scores_gemma":[0.00019439861,0.000052537685,0.0003701619,0.00059026433,0.00017195093,0.00037146892,0.000011324576,0.00075040106,0.0004956595,0.0064773345,0.99046445,0.00004998658],"about_ca_topic_score_codex":0.003135569,"about_ca_topic_score_gemma":0.007547134,"teacher_disagreement_score":0.013606832,"about_ca_system_score_codex":0.0026483627,"about_ca_system_score_gemma":0.0026017756,"threshold_uncertainty_score":0.033818424},"labels":[],"label_agreement":null},{"id":"W1982658111","doi":"10.1080/09658210344000242","title":"Sharpening the echo: An iterative‐resonance model for short‐term recognition memory","year":2004,"lang":"en","type":"article","venue":"Memory","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Sharpening; Feature (linguistics); Set (abstract data type); Similarity (geometry); Echo (communications protocol); Pattern recognition (psychology); Artificial intelligence; Data set; Computer science; Function (biology); Term (time); Latency (audio); Algorithm; Psychology; Image (mathematics); Physics","score_opus":0.0720773608540888,"score_gpt":0.2935031773999124,"score_spread":0.22142581654582358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982658111","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":[0.06250318,0.00039177845,0.9270807,0.0010494329,0.00009240634,0.000111615336,0.00013033695,0.0005030909,0.008137531],"genre_scores_gemma":[0.8298459,0.0005404739,0.14948633,0.0004958856,0.00010490693,0.00050393806,0.00018986937,0.00018325832,0.01864943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989711,0.00034713827,0.000049808623,0.0002591148,0.00020642954,0.00016645221],"domain_scores_gemma":[0.99606174,0.0021654505,0.00044956745,0.00055871566,0.0004917218,0.00027288246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024355797,0.00087484316,0.0014350325,0.000827598,0.00065910845,0.0018135512,0.005308417,0.0029240435,0.006275443],"category_scores_gemma":[0.011396039,0.0009021354,0.0017373104,0.0007071866,0.0022011371,0.0046793213,0.0017659654,0.003034319,0.0019107051],"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":[0.00046817935,0.00034161328,0.002587704,0.00022315882,0.00025178236,0.00052855554,0.0010779515,0.63510144,0.013642401,0.26392078,0.003044008,0.07881236],"study_design_scores_gemma":[0.000026146692,0.000085229294,0.00022173773,0.000010675921,0.000028163433,0.00012469881,0.000020702286,0.90346485,0.0008209482,0.0946502,0.00051514135,0.000031434796],"about_ca_topic_score_codex":0.0029836784,"about_ca_topic_score_gemma":0.0023001716,"teacher_disagreement_score":0.006275443,"about_ca_system_score_codex":0.0012301701,"about_ca_system_score_gemma":0.0009296679,"threshold_uncertainty_score":0.020993471},"labels":[],"label_agreement":null},{"id":"W2002223291","doi":"10.1007/s11390-014-1415-z","title":"Minimizing the Discrepancy Between Source and Target Domains by Learning Adapting Components","year":2014,"lang":"en","type":"article","venue":"Journal of Computer Science and Technology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"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 Waterloo; University of British Columbia","funders":"","keywords":"Computer science; Independence (probability theory); Embedding; Dimensionality reduction; Range (aeronautics); Feature (linguistics); Reproducing kernel Hilbert space; Domain (mathematical analysis); Theory of computation; Kernel (algebra); Domain adaptation; Feature vector; Curse of dimensionality; Artificial intelligence; Kernel method; Feature selection; Machine learning; Algorithm; Hilbert space; Support vector machine; Mathematics","score_opus":0.009523472966358878,"score_gpt":0.22457562986387813,"score_spread":0.21505215689751925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002223291","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":[0.05301847,0.00070459483,0.94372624,0.00022967748,0.000069880065,0.000070090246,0.000086235,0.0011738467,0.0009209003],"genre_scores_gemma":[0.6109488,0.00072091917,0.38182887,0.00051158475,0.00013770712,0.00018966608,0.0010161147,0.00042791467,0.0042183874],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991247,0.00025409053,0.000057250596,0.00033406672,0.00015822751,0.0000716355],"domain_scores_gemma":[0.99698406,0.0019612203,0.0001267307,0.00041876506,0.0004042967,0.00010486734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015805396,0.0014625709,0.00160206,0.0010404632,0.0004562126,0.0010550024,0.0020425878,0.0021797824,0.0012269164],"category_scores_gemma":[0.007025308,0.000576623,0.00090921926,0.00091869844,0.0008106082,0.0023701952,0.0021379204,0.0022641895,0.00077621493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079990027,0.00050348166,0.004864098,0.00040458742,0.00035310994,0.00019430541,0.00023912515,0.34876353,0.03526628,0.007231813,0.0066999486,0.5946799],"study_design_scores_gemma":[0.00002462968,0.0000852772,0.00055005064,0.000018045806,0.00006024196,0.00009340998,0.000049917006,0.98476195,0.005585084,0.008101689,0.00065314403,0.00001646782],"about_ca_topic_score_codex":0.003208378,"about_ca_topic_score_gemma":0.0035557395,"teacher_disagreement_score":0.003208378,"about_ca_system_score_codex":0.0005874075,"about_ca_system_score_gemma":0.0011816111,"threshold_uncertainty_score":0.008358777},"labels":[],"label_agreement":null},{"id":"W2003032055","doi":"10.1002/minf.201100053","title":"Target‐Driven Subspace Mapping Methods and Their Applicability Domain Estimation","year":2011,"lang":"en","type":"article","venue":"Molecular Informatics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Investigaciones Científicas y Técnicas; Deutsche Forschungsgemeinschaft","keywords":"Subspace topology; Estimation; Computer science; Domain (mathematical analysis); Data mining; Artificial intelligence; Computational biology; Pattern recognition (psychology); Mathematics; Biology; Engineering","score_opus":0.025776361414791102,"score_gpt":0.27292270232969645,"score_spread":0.24714634091490534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003032055","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009484752,0.00017877553,0.9896917,0.00007071529,0.000006348124,0.000019654819,0.000023316092,0.00015417633,0.00037051985],"genre_scores_gemma":[0.47482446,0.0005910646,0.52221084,0.00011811466,0.000066279696,0.0003197635,0.0003083902,0.00013852608,0.0014225875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99878126,0.00062301575,0.00004527209,0.00013351739,0.00037286093,0.000044037384],"domain_scores_gemma":[0.99535817,0.003350549,0.00031859992,0.00045076915,0.00043117008,0.00009071951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029278954,0.0005111256,0.00069322117,0.0011338309,0.00042380352,0.0007052291,0.0010050979,0.0006415411,0.0010048442],"category_scores_gemma":[0.009585235,0.0002960124,0.00077036733,0.0008054187,0.0009879251,0.0011483425,0.0015633738,0.0012910105,0.0003614417],"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":[0.00012617596,0.000093622155,0.0020251132,0.00015669118,0.000096994016,0.00007950255,0.00022463629,0.58683944,0.0070006736,0.03890313,0.0011252834,0.3633287],"study_design_scores_gemma":[0.000007278068,0.000035404184,0.00044030263,0.000009909327,0.0000058486276,0.000052219904,0.000020384301,0.97290015,0.0026118096,0.023190107,0.0007118754,0.000014645231],"about_ca_topic_score_codex":0.0010265495,"about_ca_topic_score_gemma":0.0006790681,"teacher_disagreement_score":0.0029278954,"about_ca_system_score_codex":0.0003923564,"about_ca_system_score_gemma":0.00068952056,"threshold_uncertainty_score":0.015484393},"labels":[],"label_agreement":null},{"id":"W2004109264","doi":"10.1109/tpami.2014.2306414","title":"A Hybrid Loss for Multiclass and Structured Prediction","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Northwest A and F University; University of Adelaide; University of Alberta","keywords":"Hinge loss; CRFS; Conditional random field; Artificial intelligence; Computer science; Machine learning; Support vector machine; Structured prediction; Margin (machine learning); Probabilistic logic; Consistency (knowledge bases); Multiclass classification; Parametric statistics; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.0158254362966248,"score_gpt":0.25645961207226653,"score_spread":0.24063417577564172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004109264","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":[0.016608978,0.00032206878,0.98056513,0.00051975425,0.000052924963,0.000048912356,0.000100864905,0.00043164284,0.0013497275],"genre_scores_gemma":[0.65333587,0.00046373592,0.33664978,0.0009058553,0.00032715473,0.00033358956,0.00095637696,0.00036846436,0.0066590975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99690527,0.0011231125,0.00014230602,0.00057884207,0.0010400002,0.00021053299],"domain_scores_gemma":[0.9945123,0.0029078848,0.00053878507,0.0011322704,0.00064097095,0.00026762602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057263034,0.0012240385,0.0013262755,0.001078058,0.0006419206,0.0015650339,0.0029522087,0.0021814601,0.0022412061],"category_scores_gemma":[0.012582614,0.00049209065,0.00095396204,0.00092544477,0.001874568,0.0051464597,0.0040829843,0.002939099,0.0009223071],"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":[0.00050565554,0.00046407932,0.005055333,0.0001923083,0.00015767114,0.0002678919,0.00019342532,0.57862103,0.009352903,0.089741915,0.010658662,0.30478916],"study_design_scores_gemma":[0.000015301875,0.00009729549,0.0004195106,0.000015800366,0.00001048157,0.00009698653,0.000015081331,0.9628954,0.0016732364,0.033816643,0.0009288698,0.000015461],"about_ca_topic_score_codex":0.0007772556,"about_ca_topic_score_gemma":0.00083195337,"teacher_disagreement_score":0.0057263034,"about_ca_system_score_codex":0.0011359509,"about_ca_system_score_gemma":0.0013259219,"threshold_uncertainty_score":0.030283988},"labels":[],"label_agreement":null},{"id":"W2010323135","doi":"10.1016/j.beproc.2009.12.010","title":"The Terrace simultaneous chaining paradigm","year":2010,"lang":"en","type":"article","venue":"Behavioural Processes","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chaining; Terrace (agriculture); Context (archaeology); Human memory; Underpinning; Psychology; Cognitive psychology; Cognitive science; Computer science; Artificial intelligence; Geography; Developmental psychology; Cognition; Neuroscience; Geology; Archaeology","score_opus":0.024855373464846373,"score_gpt":0.2655252212630526,"score_spread":0.24066984779820624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010323135","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":[0.04531138,0.0001043453,0.9464721,0.0003370689,0.000047405963,0.00007515395,0.00008349017,0.0004007746,0.007168266],"genre_scores_gemma":[0.74921083,0.0002378295,0.23574671,0.00024868996,0.00007954038,0.00030435092,0.00027518914,0.00014574765,0.013751029],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924845,0.00025171984,0.000023800367,0.00026920642,0.00013973225,0.00006708738],"domain_scores_gemma":[0.99632746,0.0020396423,0.00022910925,0.0009316513,0.00018342222,0.00028871285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011937694,0.0005178637,0.0007403737,0.00040822188,0.000623224,0.0008812807,0.0028440563,0.0014393101,0.008514144],"category_scores_gemma":[0.005823752,0.00047144527,0.0005785501,0.00050530024,0.0015487168,0.0038095468,0.0023822985,0.0023192568,0.00076653896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092585443,0.00075462664,0.0020841195,0.00035238362,0.00021631345,0.00045697668,0.00082006917,0.16350362,0.0275082,0.5689031,0.003159067,0.23131575],"study_design_scores_gemma":[0.000051393083,0.00012170572,0.00047733882,0.000010882296,0.000017583237,0.00008630837,0.00004825231,0.50990325,0.0023479718,0.48550576,0.0014063687,0.000023238337],"about_ca_topic_score_codex":0.0015758204,"about_ca_topic_score_gemma":0.0018940198,"teacher_disagreement_score":0.008514144,"about_ca_system_score_codex":0.00037700357,"about_ca_system_score_gemma":0.000582576,"threshold_uncertainty_score":0.028482616},"labels":[],"label_agreement":null},{"id":"W2031434482","doi":"10.1109/icdmw.2013.117","title":"Auto-Tuning Kernel Mean Matching","year":2013,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"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 Waterloo","funders":"","keywords":"Kernel (algebra); Matching (statistics); Mean squared error; Reproducing kernel Hilbert space; Benchmark (surveying); Measure (data warehouse); Mathematics; Algorithm; Computer science; Hilbert space; Applied mathematics; Statistics; Data mining; Mathematical analysis","score_opus":0.01906497583972241,"score_gpt":0.23763389947867802,"score_spread":0.2185689236389556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031434482","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048795044,0.00027724486,0.94829875,0.000066267734,0.00004672265,0.0000377423,0.000032675827,0.0015091358,0.0009364558],"genre_scores_gemma":[0.6552047,0.00013741819,0.3419351,0.00017320421,0.000045994973,0.00009459885,0.00028866925,0.00044896928,0.0016713303],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984634,0.00041902225,0.00008712905,0.0004826701,0.0004365042,0.00011131016],"domain_scores_gemma":[0.9972574,0.0011298935,0.0002239739,0.0006527097,0.0006427656,0.00009327668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030553553,0.00064965175,0.0012701548,0.0011546969,0.0005084334,0.00091749313,0.0020326646,0.0015018665,0.0012583744],"category_scores_gemma":[0.012276096,0.00039612481,0.00081451796,0.0009043399,0.00068465335,0.0020986183,0.0017462432,0.0012033129,0.00071450724],"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":[0.0002919869,0.00028151963,0.0041062967,0.00016843635,0.00017219802,0.00007698213,0.00017817675,0.4017159,0.026422929,0.009402649,0.003691477,0.5534915],"study_design_scores_gemma":[0.000010259282,0.000029187064,0.00053765695,0.0000046135965,0.000010486021,0.000056367488,0.0000143801735,0.9911171,0.004984856,0.0027248012,0.0004988653,0.000011459911],"about_ca_topic_score_codex":0.0018116212,"about_ca_topic_score_gemma":0.0017135824,"teacher_disagreement_score":0.0030553553,"about_ca_system_score_codex":0.00076347275,"about_ca_system_score_gemma":0.00090732536,"threshold_uncertainty_score":0.016158402},"labels":[],"label_agreement":null},{"id":"W2074652467","doi":"10.1109/tmm.2012.2234729","title":"An Unsupervised Hierarchical Feature Learning Framework for One-Shot Image Recognition","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":29,"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 British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Pyramid (geometry); Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Feature (linguistics); Feature extraction; Domain (mathematical analysis); Machine learning; Feature learning; Domain knowledge; Matching (statistics)","score_opus":0.05355395215045962,"score_gpt":0.3000440028239455,"score_spread":0.2464900506734859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074652467","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":[0.0030823231,0.00010581415,0.9959831,0.000035677025,0.000009872952,0.000025244863,0.0000770576,0.00042171785,0.0002591983],"genre_scores_gemma":[0.27538547,0.00032218712,0.72021985,0.00014767185,0.00010221402,0.00022939799,0.0012226448,0.00012846752,0.0022420897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999401,0.00012595534,0.000029758376,0.00021174592,0.0001579998,0.000073594696],"domain_scores_gemma":[0.999424,0.0001632449,0.000064198524,0.00016614632,0.00014814427,0.000034323297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069246045,0.0006688034,0.0010390284,0.0011638036,0.00032001504,0.0005620571,0.0019591965,0.00083955115,0.0013813417],"category_scores_gemma":[0.0017426643,0.00030065503,0.0010410853,0.00121213,0.00056601566,0.0013899112,0.0009246931,0.0013596066,0.0006200552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001624908,0.00029271672,0.0012199789,0.00016455098,0.00012214112,0.00015686732,0.00014293696,0.18697453,0.027348429,0.027268685,0.0058840057,0.75026274],"study_design_scores_gemma":[0.0000061972837,0.00006303898,0.0004992315,0.0000056366994,0.000012672612,0.00006449532,0.000011941818,0.98142767,0.0036597836,0.012953262,0.0012799874,0.000016114192],"about_ca_topic_score_codex":0.005836111,"about_ca_topic_score_gemma":0.007177979,"teacher_disagreement_score":0.005836111,"about_ca_system_score_codex":0.00063226104,"about_ca_system_score_gemma":0.0007537934,"threshold_uncertainty_score":0.0116042495},"labels":[],"label_agreement":null},{"id":"W2075536623","doi":"10.1109/cvpr.2012.6247941","title":"Complex loss optimization via dual decomposition","year":2012,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Decomposition; Dual (grammatical number); Computer science; Margin (machine learning); Factorization; Measure (data warehouse); Exploit; Class (philosophy); Matrix decomposition; Mathematical optimization; Algorithm; Artificial intelligence; Machine learning; Mathematics; Data mining","score_opus":0.029897036605595464,"score_gpt":0.2867345743199211,"score_spread":0.25683753771432566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075536623","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":[0.0019746749,0.00008189414,0.99683577,0.0000837924,0.000017974544,0.000012364142,0.000017651631,0.000099949335,0.0008759455],"genre_scores_gemma":[0.23901297,0.00036914274,0.75200176,0.000372631,0.0001652301,0.00025350132,0.00033893913,0.00036315125,0.007122749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930644,0.0002185985,0.00003134821,0.00014086913,0.0002402915,0.000062439874],"domain_scores_gemma":[0.9990687,0.00036791884,0.00010287318,0.00021744921,0.00016220163,0.00008091027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016221474,0.0012658577,0.0013935978,0.00077425805,0.00036835377,0.0014781422,0.0014766599,0.0015232662,0.003399676],"category_scores_gemma":[0.0036996312,0.00057993946,0.0008852856,0.0006330187,0.0010181793,0.0022507356,0.002758164,0.0028290355,0.0015522682],"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":[0.00013863014,0.00015531867,0.0005630956,0.00014471397,0.000070898655,0.000121025754,0.00006674747,0.700346,0.011103127,0.10341139,0.007147661,0.17673144],"study_design_scores_gemma":[0.000004416136,0.000016361186,0.000030015637,0.00000378178,0.0000029559546,0.000023022172,0.0000023128298,0.9859854,0.00081772625,0.012239282,0.0008700534,0.0000046705118],"about_ca_topic_score_codex":0.0005909103,"about_ca_topic_score_gemma":0.0005099888,"teacher_disagreement_score":0.003399676,"about_ca_system_score_codex":0.00078571873,"about_ca_system_score_gemma":0.00077451405,"threshold_uncertainty_score":0.011373043},"labels":[],"label_agreement":null},{"id":"W2076167926","doi":"10.1007/s10994-008-5088-0","title":"Inductive transfer with context-sensitive neural networks","year":2008,"lang":"en","type":"article","venue":"Machine Learning","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Inductive transfer; Machine learning; Task (project management); Multi-task learning; Artificial intelligence; Transfer of learning; Context (archaeology); Artificial neural network; Encoding (memory); Reduction (mathematics); Robot learning","score_opus":0.017420568659186616,"score_gpt":0.2137613535598668,"score_spread":0.1963407849006802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076167926","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":[0.011600365,0.0005388586,0.98519415,0.00016114608,0.00007700568,0.000049977647,0.000054794673,0.00087084714,0.0014527517],"genre_scores_gemma":[0.677714,0.0007334679,0.30968118,0.0005403023,0.00024721478,0.00034870536,0.0006571542,0.00035550463,0.009722479],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928766,0.0002798885,0.000031750795,0.00019174199,0.00014969954,0.00005921034],"domain_scores_gemma":[0.99799347,0.0012536241,0.000077064025,0.00039969364,0.00021490785,0.000061326355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017428144,0.00068006094,0.0011857434,0.0007359595,0.00052949006,0.0007088387,0.0026721216,0.0015747839,0.0030468309],"category_scores_gemma":[0.005335631,0.00051682297,0.0007001578,0.0008589722,0.00093481864,0.002397757,0.0029357125,0.0022362107,0.001137857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028211472,0.00036370722,0.00087598286,0.00024787415,0.00018999733,0.0001823802,0.00017717706,0.45920733,0.016298238,0.040920597,0.007290031,0.47396445],"study_design_scores_gemma":[0.0000071437794,0.000026002015,0.0001273449,0.000008811191,0.000010528111,0.00003255408,0.000009965438,0.9667909,0.0035781546,0.028752085,0.0006468002,0.000009671859],"about_ca_topic_score_codex":0.0015217665,"about_ca_topic_score_gemma":0.0019659437,"teacher_disagreement_score":0.0030468309,"about_ca_system_score_codex":0.0006238805,"about_ca_system_score_gemma":0.0005844975,"threshold_uncertainty_score":0.010192692},"labels":[],"label_agreement":null},{"id":"W2081990996","doi":"10.1109/tip.2013.2259836","title":"Cross-Domain Object Recognition Via Input-Output Kernel Analysis","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":41,"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 British Columbia","funders":"","keywords":"Kernel (algebra); Artificial intelligence; Reproducing kernel Hilbert space; Pattern recognition (psychology); Discriminative model; Kernel embedding of distributions; Computer science; Tree kernel; Domain (mathematical analysis); Radial basis function kernel; Kernel method; Benchmark (surveying); Feature vector; Feature (linguistics); Polynomial kernel; Cognitive neuroscience of visual object recognition; Support vector machine; Object (grammar); Mathematics; Hilbert space","score_opus":0.021392609484000888,"score_gpt":0.27336581453154235,"score_spread":0.25197320504754145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081990996","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":[0.04220982,0.00023124772,0.95560247,0.00006785844,0.000023787956,0.000026012664,0.000055766795,0.0011685801,0.0006144439],"genre_scores_gemma":[0.7446869,0.00022222976,0.25230962,0.00008635354,0.000028092432,0.000049102317,0.0006151901,0.0001823365,0.0018203125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989687,0.00025018444,0.000060525705,0.00035802255,0.0002614834,0.000101116246],"domain_scores_gemma":[0.9982553,0.0004689623,0.00020069897,0.00055189297,0.00044684025,0.000076215176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017048257,0.000753719,0.001095382,0.0012243364,0.00029979122,0.0010689324,0.0013092795,0.00086900016,0.00092241657],"category_scores_gemma":[0.004157428,0.00028660204,0.0010559004,0.001234405,0.0007764953,0.0018993665,0.0017720398,0.0013627276,0.0006562268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037015456,0.00029376638,0.005142953,0.00016085146,0.0002616837,0.00028733863,0.00020775109,0.3196252,0.04843431,0.010067671,0.002990161,0.6121582],"study_design_scores_gemma":[0.0000044707317,0.000024155828,0.0010779928,0.0000035355404,0.000014280524,0.000079806465,0.00002476676,0.9851525,0.00967371,0.0034344539,0.0004968788,0.000013368006],"about_ca_topic_score_codex":0.0017394538,"about_ca_topic_score_gemma":0.0011495501,"teacher_disagreement_score":0.0017394538,"about_ca_system_score_codex":0.0005694743,"about_ca_system_score_gemma":0.00046769396,"threshold_uncertainty_score":0.009016097},"labels":[],"label_agreement":null},{"id":"W2101664365","doi":"10.7551/mitpress/7503.003.0104","title":"Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields","year":2007,"lang":"en","type":"book-chapter","venue":"The MIT Press eBooks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Dependency (UML); Computer science; Artificial intelligence; Conditional random field; Pattern recognition (psychology)","score_opus":0.06359360411955886,"score_gpt":0.274220810934391,"score_spread":0.21062720681483213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101664365","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":[0.0029188923,0.00017492705,0.99584466,0.00008571528,0.000011111689,0.000013684369,0.000054403834,0.00031473662,0.0005818776],"genre_scores_gemma":[0.26297393,0.0006924112,0.7297287,0.00035859115,0.00015189542,0.00018573842,0.00089924404,0.00033011535,0.0046793576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913174,0.00042373588,0.000028769142,0.00021138205,0.00014993949,0.000054390057],"domain_scores_gemma":[0.9978801,0.0011661609,0.00016395777,0.00050131994,0.000234068,0.00005444577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021036302,0.0009305834,0.0012516803,0.00069211744,0.00028853273,0.00080714014,0.0020156635,0.0011960149,0.0012313835],"category_scores_gemma":[0.0041355174,0.0006526,0.00071801065,0.0010153367,0.001238975,0.00213578,0.0010791231,0.0019042069,0.0007121731],"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":[0.00009474937,0.00010742,0.0012942671,0.00019928327,0.000108637316,0.00011754036,0.00016972072,0.5864464,0.010973328,0.043676667,0.007604981,0.34920698],"study_design_scores_gemma":[0.0000051762668,0.000021065409,0.00024340951,0.000010980251,0.000007742875,0.00005896334,0.000006711465,0.9708581,0.0017002994,0.025559356,0.0015162604,0.000011888514],"about_ca_topic_score_codex":0.0018771386,"about_ca_topic_score_gemma":0.0030601504,"teacher_disagreement_score":0.0021036302,"about_ca_system_score_codex":0.00059094187,"about_ca_system_score_gemma":0.0006796068,"threshold_uncertainty_score":0.011125147},"labels":[],"label_agreement":null},{"id":"W2104094955","doi":"10.1007/s10994-009-5152-4","title":"A theory of learning from different domains","year":2009,"lang":"en","type":"article","venue":"Machine Learning","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3486,"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 Waterloo","funders":"Advanced Research Projects Agency; Defense Advanced Research Projects Agency; University of Pennsylvania; National Science Foundation","keywords":"Classifier (UML); Computer science; Weighting; Discriminative model; Artificial intelligence; Bounding overwatch; Pattern recognition (psychology); Bayes error rate; Labeled data; Machine learning; Test data; Quadratic classifier; Divergence (linguistics); Algorithm; Bayes classifier; Support vector machine; Naive Bayes classifier","score_opus":0.015224496617028887,"score_gpt":0.23817871003438795,"score_spread":0.22295421341735908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104094955","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":[0.0032860404,0.0033752748,0.9743397,0.0031647068,0.00028901536,0.00009335185,0.0002607144,0.00018909799,0.015002015],"genre_scores_gemma":[0.32871696,0.012146045,0.61651736,0.0072554247,0.0048757256,0.0024313848,0.0019000693,0.00054584915,0.025611179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98591125,0.0059950934,0.0009067138,0.003118042,0.0034134164,0.0006554328],"domain_scores_gemma":[0.956077,0.03365522,0.0016825909,0.005355957,0.002376306,0.0008528769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014380254,0.0025159973,0.003030754,0.005854788,0.0025844024,0.008852513,0.005280232,0.005102105,0.0093676485],"category_scores_gemma":[0.04085714,0.0017262134,0.004565594,0.004972297,0.012499618,0.018018562,0.010053108,0.00976848,0.003025445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022317088,0.000025833697,0.00030087298,0.00018082817,0.000072443276,0.000060242925,0.00014163523,0.014558581,0.0000963855,0.9651338,0.0021184096,0.017288594],"study_design_scores_gemma":[0.000015387313,0.000022910288,0.00014044857,0.000059178015,0.000014153385,0.000077155746,0.000029361445,0.04977415,0.0001017949,0.9444194,0.0053279144,0.000018176599],"about_ca_topic_score_codex":0.0027525602,"about_ca_topic_score_gemma":0.001304121,"teacher_disagreement_score":0.014380254,"about_ca_system_score_codex":0.0045065074,"about_ca_system_score_gemma":0.0023562708,"threshold_uncertainty_score":0.076051},"labels":[],"label_agreement":null},{"id":"W2104710058","doi":"10.1162/coli_r_00188","title":"<b>Semi-Supervised Learning and Domain Adaptation in Natural Language Processing</b> <b>Anders Søgaard</b> University of Copenhagen Morgan &amp; Claypool (Synthesis Lectures on Human Language Technologies, edited by Graeme Hirst, volume 21), 2013, x+93 pp; paperbound, ISBN 978-1-60845-985-8, $40.00; e-book, ISBN 978-1-60845-986-5, $30.00 or by subscription","year":2014,"lang":"en","type":"article","venue":"Computational Linguistics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Artificial intelligence; Natural language processing; Machine learning; Parsing; Test set; Test data; Supervised learning; sort; Adaptation (eye); Information retrieval; Artificial neural network; Psychology","score_opus":0.014480307356805262,"score_gpt":0.24449198145169979,"score_spread":0.23001167409489454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104710058","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035805218,0.10158897,0.8350985,0.016423903,0.0033975542,0.00017531367,0.00081885746,0.003060318,0.035856105],"genre_scores_gemma":[0.076478,0.12626086,0.66823334,0.005805035,0.0089785475,0.0008642737,0.005494001,0.002499365,0.105386555],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99848205,0.0005489357,0.000100098456,0.0003807381,0.0004231955,0.00006507105],"domain_scores_gemma":[0.99611,0.0028044276,0.0001343699,0.00038498925,0.00044235738,0.00012380058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002664428,0.0010058513,0.0010689639,0.0013888777,0.00043602442,0.0029733344,0.0015299203,0.0017848748,0.016117824],"category_scores_gemma":[0.004145222,0.00073147827,0.00072112214,0.0039607557,0.0024134675,0.004080174,0.0019037891,0.0039167427,0.013201529],"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":[0.00009676299,0.000091427544,0.0003597531,0.0011287162,0.000117681004,0.00012227522,0.00021339947,0.012634038,0.0038793664,0.0393288,0.25813693,0.6838908],"study_design_scores_gemma":[0.00004036436,0.00016585538,0.0029423728,0.000903027,0.0000656615,0.0008170308,0.00022053067,0.20113395,0.0055523133,0.21116678,0.57683307,0.00015889987],"about_ca_topic_score_codex":0.002914996,"about_ca_topic_score_gemma":0.0030528668,"teacher_disagreement_score":0.016117824,"about_ca_system_score_codex":0.0009511617,"about_ca_system_score_gemma":0.001009587,"threshold_uncertainty_score":0.053919435},"labels":[],"label_agreement":null},{"id":"W2107215754","doi":"","title":"Discriminative Transfer Learning with Tree-based Priors","year":2013,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":171,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Discriminative model; Prior probability; Tree (set theory); Machine learning; Pattern recognition (psychology); Artificial neural network; Transfer of learning; Deep learning; Classifier (UML); Deep neural networks; Contextual image classification; Tree structure; Set (abstract data type); Decision tree; Image (mathematics); Binary tree; Algorithm; Bayesian probability; Mathematics","score_opus":0.012288865688161786,"score_gpt":0.21085326981038516,"score_spread":0.19856440412222337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107215754","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":[0.025935842,0.0002858733,0.9696242,0.00025493864,0.000041785137,0.0000642596,0.0001390567,0.0018708993,0.001783125],"genre_scores_gemma":[0.71528155,0.00034138132,0.27622065,0.00048725578,0.00015926188,0.00028328318,0.0013160224,0.00024474488,0.0056658424],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993241,0.00020118267,0.000035142784,0.00019649773,0.00016041989,0.000082608436],"domain_scores_gemma":[0.9977642,0.0011367521,0.00017055322,0.00048901676,0.00032213685,0.00011731424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00134474,0.0010236059,0.0011760416,0.00095821015,0.00052076514,0.0007793453,0.0021709176,0.0015156268,0.0028376577],"category_scores_gemma":[0.0054680607,0.00061504764,0.0006680787,0.0011989126,0.0009920809,0.004213667,0.0019956431,0.0026609064,0.0015133669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000331053,0.00035918725,0.00219422,0.00013727824,0.00006991409,0.00016486418,0.00018894435,0.41011557,0.012417341,0.030910727,0.011700509,0.53141046],"study_design_scores_gemma":[0.00001165136,0.000029189874,0.00014431657,0.000005790284,0.0000053454123,0.000019972642,0.000008504619,0.97775674,0.0016423299,0.019849665,0.00052040326,0.0000060464454],"about_ca_topic_score_codex":0.002770299,"about_ca_topic_score_gemma":0.0035394835,"teacher_disagreement_score":0.0028376577,"about_ca_system_score_codex":0.001146527,"about_ca_system_score_gemma":0.0010834489,"threshold_uncertainty_score":0.009492934},"labels":[],"label_agreement":null},{"id":"W2108806771","doi":"10.48550/arxiv.1206.6407","title":"Large-Scale Feature Learning With Spike-and-Slab Sparse Coding","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Spike (software development); Artificial intelligence; Machine learning; Inference; Neural coding; Exploit; Feature extraction; Feature learning; Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Coding (social sciences); Mathematics","score_opus":0.040242199508861515,"score_gpt":0.17405397545326906,"score_spread":0.13381177594440755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108806771","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":[0.007163195,0.0000637442,0.99154145,0.000134219,0.000017316723,0.000017229926,0.00007727405,0.00057429884,0.00041134082],"genre_scores_gemma":[0.37939256,0.00017327751,0.6160615,0.0002827669,0.000109794026,0.00015324258,0.0009327193,0.00018623662,0.00270785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999571,0.0001044665,0.000018595989,0.000102715654,0.00013833861,0.000064826694],"domain_scores_gemma":[0.9988035,0.00048669407,0.00009301972,0.00036348292,0.00017027606,0.000083069775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085298467,0.0006473859,0.0010335541,0.0006483909,0.00042971407,0.0007485015,0.0020235924,0.001070047,0.0017387343],"category_scores_gemma":[0.004142953,0.00036809398,0.0009777006,0.0012977701,0.000976724,0.002174662,0.0016242153,0.0023102278,0.00063325604],"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":[0.00021435422,0.00016012065,0.001587006,0.00012534212,0.00011080049,0.00016472721,0.00017209743,0.5312283,0.017141305,0.05697462,0.011214004,0.38090736],"study_design_scores_gemma":[0.000005629976,0.00001163444,0.00007583793,0.0000020535615,0.0000027960439,0.000018117042,0.0000052036103,0.97929275,0.0013871075,0.018777728,0.00041615963,0.0000049569167],"about_ca_topic_score_codex":0.004719397,"about_ca_topic_score_gemma":0.0067015775,"teacher_disagreement_score":0.004719397,"about_ca_system_score_codex":0.0006809063,"about_ca_system_score_gemma":0.0011021963,"threshold_uncertainty_score":0.009383857},"labels":[],"label_agreement":null},{"id":"W2110244106","doi":"10.1016/j.neunet.2012.02.010","title":"Analysis of the IJCNN 2011 UTL challenge","year":2012,"lang":"en","type":"article","venue":"Neural Networks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"Acadia University","funders":"Defense Advanced Research Projects Agency; European Science Foundation; European Commission; National Science Foundation","keywords":"Computer science; Transfer of learning; Artificial intelligence; Task (project management); Handwriting; Unsupervised learning; Machine learning; Labeled data; Pattern recognition (psychology)","score_opus":0.027744572017228818,"score_gpt":0.24800827991987884,"score_spread":0.22026370790265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110244106","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43081367,0.039770164,0.17602533,0.06851819,0.017332986,0.000904666,0.11777186,0.027163172,0.121699944],"genre_scores_gemma":[0.6339303,0.0028263954,0.08270293,0.00742608,0.0023238102,0.0006972697,0.21261375,0.0031627652,0.05431668],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963527,0.0014329664,0.00013762599,0.0006434319,0.0010833291,0.0003499066],"domain_scores_gemma":[0.993148,0.0034538244,0.00021046546,0.0012744836,0.001580453,0.0003327265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045986567,0.0014458059,0.0014898281,0.0013347841,0.0017374123,0.0019865902,0.0026069963,0.0032612681,0.008409247],"category_scores_gemma":[0.02214534,0.00034425288,0.00088812097,0.0017567581,0.0010020445,0.0032359478,0.0027592843,0.0037099167,0.0050873496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008129958,0.0004986954,0.0027004946,0.0007089115,0.00015059908,0.00027638432,0.00013752801,0.036735658,0.0019076281,0.0108443545,0.8101574,0.13506934],"study_design_scores_gemma":[0.00034508388,0.00039544996,0.008694938,0.00027280828,0.0001166411,0.00076470047,0.00077085465,0.7197663,0.013426808,0.06441134,0.19091482,0.0001202292],"about_ca_topic_score_codex":0.019687979,"about_ca_topic_score_gemma":0.037221354,"teacher_disagreement_score":0.019687979,"about_ca_system_score_codex":0.0026219122,"about_ca_system_score_gemma":0.00204297,"threshold_uncertainty_score":0.03914678},"labels":[],"label_agreement":null},{"id":"W2113839990","doi":"10.48550/arxiv.1312.6211","title":"An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":493,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Forgetting; Dropout (neural networks); Task (project management); Computer science; Artificial neural network; Artificial intelligence; Function (biology); Machine learning; Activation function; Psychology; Cognitive psychology; Engineering","score_opus":0.07776421947274287,"score_gpt":0.21291964737534344,"score_spread":0.13515542790260057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113839990","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90680116,0.0041253087,0.08357008,0.001552899,0.00009116168,0.0001277434,0.0004199076,0.0004499384,0.0028618542],"genre_scores_gemma":[0.9913374,0.00035140695,0.0070768064,0.00012900875,0.00003145421,0.000044380224,0.00047893543,0.000043501295,0.0005071052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971673,0.0016934688,0.00017631933,0.00040378296,0.00037973994,0.00017932703],"domain_scores_gemma":[0.890806,0.0858343,0.0064740386,0.010916818,0.0045684045,0.0014004356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014886918,0.0007621257,0.0008867266,0.0011541217,0.0007129208,0.00094392453,0.0019192741,0.0015542501,0.0015990646],"category_scores_gemma":[0.14129311,0.00039340527,0.0005691021,0.0010071755,0.0022493524,0.0034573493,0.0014654903,0.0029555243,0.00022299413],"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":[0.0019568515,0.0011645633,0.17976609,0.0011761404,0.00079209864,0.00074118597,0.0011398939,0.61538,0.0035135255,0.04110848,0.013549812,0.13971142],"study_design_scores_gemma":[0.00010086712,0.00058348995,0.037125614,0.00020923025,0.00009842572,0.00056268007,0.00026679973,0.9043172,0.0029332093,0.052099172,0.0016295918,0.00007373194],"about_ca_topic_score_codex":0.0029458797,"about_ca_topic_score_gemma":0.0035585419,"teacher_disagreement_score":0.014886918,"about_ca_system_score_codex":0.0010702749,"about_ca_system_score_gemma":0.00056492584,"threshold_uncertainty_score":0.078730464},"labels":[],"label_agreement":null},{"id":"W2114168642","doi":"","title":"One-shot learning by inverting a compositional causal process","year":2013,"lang":"en","type":"article","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":226,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Army Research Office; Multidisciplinary University Research Initiative; National Science Foundation","keywords":"Computer science; Artificial intelligence; Task (project management); Machine learning; Process (computing); Class (philosophy); Turing test; Range (aeronautics); Simple (philosophy); Causality (physics); Bayesian probability","score_opus":0.02457841219761775,"score_gpt":0.2623974005152571,"score_spread":0.23781898831763937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114168642","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":[0.019256733,0.00016681485,0.9773555,0.0003294946,0.000039902265,0.00006819566,0.000084900494,0.0009072091,0.0017912091],"genre_scores_gemma":[0.6636237,0.00035397089,0.32692754,0.0005557769,0.00012475783,0.00020060252,0.0006221341,0.0002302555,0.007361377],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993808,0.00018082955,0.000022656579,0.00026133892,0.00010769831,0.00004660774],"domain_scores_gemma":[0.99749976,0.0016131463,0.00014157359,0.00043370292,0.00019101673,0.000120830126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017943471,0.0007907494,0.00091083377,0.0009136982,0.00056108553,0.00095930946,0.002037949,0.0015364228,0.0041645057],"category_scores_gemma":[0.0076437937,0.0007517973,0.0011038198,0.0006383604,0.0014961838,0.0038948634,0.0020097492,0.0029418112,0.00081854215],"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":[0.0003793889,0.00045430232,0.0031330704,0.00036776823,0.00018971498,0.00030978318,0.0005387413,0.40204945,0.018476197,0.12932557,0.0058078337,0.43896818],"study_design_scores_gemma":[0.000014883018,0.00003705215,0.0002246843,0.000010663693,0.000014335534,0.000052999538,0.000016179722,0.93631834,0.002045575,0.060432557,0.00082011905,0.000012531072],"about_ca_topic_score_codex":0.0050711515,"about_ca_topic_score_gemma":0.009478149,"teacher_disagreement_score":0.0050711515,"about_ca_system_score_codex":0.00095965323,"about_ca_system_score_gemma":0.0011608724,"threshold_uncertainty_score":0.013931692},"labels":[],"label_agreement":null},{"id":"W2114960120","doi":"","title":"Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs","year":2013,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Bottleneck; Encoder; Estimator; Feature (linguistics); Algorithm; Matching (statistics); Feature vector; Sampling (signal processing); Feature matching; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Mathematics","score_opus":0.0189065197988435,"score_gpt":0.23986428295482093,"score_spread":0.22095776315597743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114960120","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":[0.015251159,0.00013311357,0.9830104,0.000093345276,0.000020341977,0.000040601644,0.000033102406,0.0007604167,0.0006574971],"genre_scores_gemma":[0.51531845,0.00017291821,0.4800616,0.00030169412,0.00007882555,0.00027304285,0.00039289906,0.00035661392,0.0030439978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981158,0.0007589697,0.00012827087,0.0004021817,0.00044830106,0.00014647121],"domain_scores_gemma":[0.9948672,0.0032342128,0.00039044674,0.000780602,0.0005629196,0.00016469389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035925321,0.00082748797,0.0016046995,0.0011529174,0.00051961583,0.0010513524,0.0021135744,0.0016127165,0.0027193143],"category_scores_gemma":[0.018038435,0.0005593749,0.0009525008,0.00092817657,0.001102064,0.0025617918,0.001952544,0.0015585367,0.001385371],"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":[0.00042137565,0.00019669237,0.0016329847,0.00017695302,0.00010052543,0.00016076623,0.00017369367,0.6327961,0.0117016155,0.047683228,0.0027468523,0.30220926],"study_design_scores_gemma":[0.000010125126,0.000026962407,0.00011830695,0.0000051145726,0.00000433814,0.000035178422,0.000009408844,0.9852889,0.0024780387,0.011676493,0.00033981018,0.0000073563224],"about_ca_topic_score_codex":0.0017530875,"about_ca_topic_score_gemma":0.0015895973,"teacher_disagreement_score":0.0035925321,"about_ca_system_score_codex":0.0008275798,"about_ca_system_score_gemma":0.0012040931,"threshold_uncertainty_score":0.018999398},"labels":[],"label_agreement":null},{"id":"W2115108235","doi":"10.1016/j.neucom.2014.11.028","title":"Combinative hypergraph learning for semi-supervised image classification","year":2014,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"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 Waterloo","funders":"","keywords":"Hypergraph; Computer science; Artificial intelligence; Pattern recognition (psychology); Image (mathematics); Supervised learning; Machine learning; Mathematics; Artificial neural network; Combinatorics","score_opus":0.023133036445901294,"score_gpt":0.25922468824672473,"score_spread":0.23609165180082345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115108235","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":[0.013746601,0.0007970734,0.9839406,0.00017631585,0.000027886075,0.000048678026,0.0001250108,0.0007266925,0.00041112],"genre_scores_gemma":[0.5664268,0.0010635974,0.42450613,0.0004970869,0.00021538956,0.00044432023,0.0016895628,0.0004282777,0.004728768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815506,0.0008844322,0.000092893926,0.00049698947,0.00027209596,0.000098455035],"domain_scores_gemma":[0.99459225,0.0037366878,0.00029240185,0.00082954834,0.00038890532,0.00016027526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021615236,0.0012838898,0.0026489545,0.00273159,0.00091445685,0.0011113076,0.0034207806,0.002724178,0.0015391354],"category_scores_gemma":[0.006861321,0.00094366824,0.0017310929,0.003048942,0.0016943137,0.0034605872,0.002678148,0.0024031927,0.00057444745],"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":[0.00031950764,0.0004395714,0.0017485667,0.0004146937,0.00059293915,0.00019896879,0.0003055442,0.496027,0.0073093986,0.019578379,0.006970279,0.4660951],"study_design_scores_gemma":[0.0000067799683,0.000031672556,0.00019452836,0.000010619748,0.000022333703,0.000027026463,0.000015939864,0.973838,0.00073039223,0.024765193,0.0003474477,0.000010210974],"about_ca_topic_score_codex":0.004693153,"about_ca_topic_score_gemma":0.0071185804,"teacher_disagreement_score":0.004693153,"about_ca_system_score_codex":0.0010880372,"about_ca_system_score_gemma":0.00094128365,"threshold_uncertainty_score":0.011431336},"labels":[],"label_agreement":null},{"id":"W2116028222","doi":"","title":"An Online Algorithm for Learning over Constrained Latent Representations using Multiple Views","year":2013,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Machine learning; Probabilistic latent semantic analysis; Classifier (UML); Grammaticality; Latent variable; Semi-supervised learning; Task (project management); Online learning","score_opus":0.1035494078126028,"score_gpt":0.341129565368156,"score_spread":0.23758015755555317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116028222","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010718224,0.000077379394,0.9973316,0.00008157747,0.000031457515,0.00004714076,0.00004136764,0.0010369371,0.00028068948],"genre_scores_gemma":[0.055722855,0.00012613858,0.94054353,0.00019383729,0.00011416948,0.00040845678,0.00059977104,0.0003081459,0.0019831832],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99836713,0.0004461536,0.00010290364,0.00053235854,0.00039827268,0.00015314171],"domain_scores_gemma":[0.9972364,0.0014659318,0.00016921242,0.00056879595,0.00040213825,0.00015759266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002028361,0.0015252031,0.0018375603,0.0014706119,0.0006970266,0.0016375959,0.0039578653,0.002981584,0.0075075603],"category_scores_gemma":[0.0069804178,0.0008942808,0.0013350078,0.0013785647,0.00092595554,0.0040627318,0.0036848425,0.0046169558,0.0031596182],"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":[0.00024054863,0.00028417638,0.00080040545,0.00014388013,0.00012531628,0.0001059197,0.00013968848,0.089800656,0.006800738,0.032999337,0.013271911,0.85528743],"study_design_scores_gemma":[0.00007455451,0.000062771374,0.00012356308,0.000014565741,0.000021855407,0.0000896723,0.000026816368,0.96175367,0.002245911,0.03313911,0.0024255721,0.000021858032],"about_ca_topic_score_codex":0.0035470647,"about_ca_topic_score_gemma":0.0055750064,"teacher_disagreement_score":0.0075075603,"about_ca_system_score_codex":0.0012009592,"about_ca_system_score_gemma":0.0020810675,"threshold_uncertainty_score":0.025115311},"labels":[],"label_agreement":null},{"id":"W2118099541","doi":"10.1145/1390156.1390203","title":"Boosting with incomplete information","year":2008,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Boosting (machine learning); Computer science; Artificial intelligence; Classifier (UML); Machine learning; Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Feature extraction","score_opus":0.01759088385830544,"score_gpt":0.18567034108990954,"score_spread":0.1680794572316041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118099541","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009227035,0.00043562404,0.9889271,0.00020091856,0.000042750456,0.00003050823,0.00003006987,0.00019429988,0.00091168797],"genre_scores_gemma":[0.5744652,0.0007949994,0.41930744,0.00076773495,0.0003539732,0.00027681916,0.000466393,0.00018492709,0.0033824905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972186,0.001586134,0.00008715513,0.00034682584,0.0005876066,0.00017366573],"domain_scores_gemma":[0.9951166,0.0026408716,0.00029809523,0.0009044196,0.0008142995,0.0002257311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007099749,0.0012346479,0.0033690839,0.0009314442,0.00066846213,0.0014608192,0.0022553457,0.0015407394,0.00125892],"category_scores_gemma":[0.012576982,0.0008738534,0.0010953961,0.0009046169,0.0017873283,0.0029211894,0.0023082152,0.0021139665,0.00065859704],"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":[0.00034955738,0.00015152551,0.0016758573,0.0003129924,0.00024670383,0.00017185426,0.00018931807,0.68086433,0.004506609,0.13448435,0.0055344156,0.17151247],"study_design_scores_gemma":[0.000018307222,0.000054499855,0.00015042955,0.000014580125,0.000019674679,0.000040267347,0.0000067854335,0.9433685,0.0009786339,0.05414752,0.0011902824,0.000010573605],"about_ca_topic_score_codex":0.0005398348,"about_ca_topic_score_gemma":0.0004775365,"teacher_disagreement_score":0.007099749,"about_ca_system_score_codex":0.00088916876,"about_ca_system_score_gemma":0.000918488,"threshold_uncertainty_score":0.03754753},"labels":[],"label_agreement":null},{"id":"W2119808881","doi":"10.1145/1553374.1553480","title":"Learning when to stop thinking and do something!","year":2009,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Task (project management); Face (sociological concept); Artificial intelligence; Entropy (arrow of time); Quality (philosophy); Sequence (biology); Gradient descent; Machine learning; Artificial neural network; Engineering","score_opus":0.014459496016143743,"score_gpt":0.25341987179706055,"score_spread":0.2389603757809168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119808881","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":[0.07626316,0.00066740206,0.9055473,0.0036615531,0.0003659776,0.00017389281,0.00016924883,0.004672355,0.008479141],"genre_scores_gemma":[0.5727939,0.0004996728,0.40711498,0.0018336228,0.00017783754,0.0002743496,0.00038600195,0.00060857943,0.01631105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927217,0.00027851423,0.000032192896,0.00024935752,0.000092111775,0.00007568596],"domain_scores_gemma":[0.9969427,0.0015464734,0.00028464565,0.00047373737,0.00047638317,0.00027611468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024415313,0.000977113,0.0008228739,0.000311398,0.0005766722,0.0011638235,0.0013396562,0.0013282032,0.0060229134],"category_scores_gemma":[0.010716048,0.0004164937,0.00040661302,0.00019527882,0.0011995786,0.0028511605,0.0007538002,0.0025181295,0.0038406407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016159368,0.0008330386,0.0107205855,0.00039955112,0.00029054115,0.0003019727,0.0007885551,0.08928802,0.031479858,0.037014943,0.037975945,0.789291],"study_design_scores_gemma":[0.0001332746,0.00055007427,0.0035181914,0.00010820836,0.00008703688,0.00026504367,0.0003479502,0.8495488,0.02571139,0.104544125,0.015058215,0.00012776096],"about_ca_topic_score_codex":0.0014647809,"about_ca_topic_score_gemma":0.0022524311,"teacher_disagreement_score":0.0060229134,"about_ca_system_score_codex":0.00046258594,"about_ca_system_score_gemma":0.0009820688,"threshold_uncertainty_score":0.020148635},"labels":[],"label_agreement":null},{"id":"W2126374126","doi":"10.1109/cvpr.2008.4587362","title":"Latent topic random fields: Learning using a taxonomy of labels","year":2008,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Probabilistic logic; Pattern recognition (psychology); Representation (politics); Contextual image classification; Feature learning; Image (mathematics); Hierarchy; Context model; Graphical model; Object detection; Machine learning; Object (grammar)","score_opus":0.08348291829754127,"score_gpt":0.2504180563305242,"score_spread":0.16693513803298293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126374126","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":[0.0096237715,0.00074397423,0.9876425,0.00047140027,0.00005269345,0.00008079596,0.00029356263,0.0005615909,0.000529766],"genre_scores_gemma":[0.37014064,0.001978084,0.61687493,0.000724621,0.00072360586,0.0009321107,0.004454885,0.00031169943,0.0038593877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968051,0.0015579378,0.00010707116,0.00092733925,0.00042982804,0.00017277533],"domain_scores_gemma":[0.99027514,0.0072448715,0.0005868089,0.0010941455,0.00055845646,0.00024046304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063251923,0.0013889855,0.0020688549,0.0028285552,0.0010955735,0.0024392386,0.0033342899,0.0031338765,0.0018081684],"category_scores_gemma":[0.017318506,0.0009250097,0.001742243,0.0031468573,0.001527458,0.0074065644,0.002161215,0.0039511975,0.0010934033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057819765,0.00059598003,0.012733512,0.00067851075,0.00034269787,0.00029928205,0.0013123653,0.24476577,0.0050403457,0.14267068,0.022855727,0.568127],"study_design_scores_gemma":[0.000046464298,0.00006912861,0.00080092077,0.000064998436,0.000035392226,0.000105592364,0.00008171226,0.8488067,0.0008532627,0.14588545,0.0032146473,0.00003582694],"about_ca_topic_score_codex":0.0035629002,"about_ca_topic_score_gemma":0.0045180186,"teacher_disagreement_score":0.0063251923,"about_ca_system_score_codex":0.001663522,"about_ca_system_score_gemma":0.0012543573,"threshold_uncertainty_score":0.0334512},"labels":[],"label_agreement":null},{"id":"W2136922672","doi":"10.1162/neco.2006.18.7.1527","title":"A Fast Learning Algorithm for Deep Belief Nets","year":2006,"lang":"en","type":"article","venue":"Neural Computation","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":16404,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Computer science; Associative property; Prior probability; Discriminative model; Generative model; Content-addressable memory; Artificial intelligence; Inference; Algorithm; Generative grammar; Pattern recognition (psychology); Deep belief network; Artificial neural network; Mathematics; Bayesian probability","score_opus":0.013151844529637757,"score_gpt":0.25050590874976464,"score_spread":0.2373540642201269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136922672","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":[0.00056490663,0.00008474315,0.9978265,0.000086114196,0.00003205393,0.000030511086,0.000053646403,0.0007280993,0.0005933188],"genre_scores_gemma":[0.04171103,0.0001819584,0.95413846,0.00020200745,0.000079772115,0.00040101036,0.0003857147,0.0003993653,0.0025006114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915636,0.00024664865,0.00006701278,0.00015660517,0.0002797159,0.00009370265],"domain_scores_gemma":[0.9980252,0.0011872356,0.00008550574,0.00021758555,0.0004092999,0.00007515791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018504867,0.0014646909,0.0013343284,0.001238877,0.0007854582,0.0016891862,0.0025395488,0.0020576005,0.00965349],"category_scores_gemma":[0.008056201,0.001108322,0.0012226636,0.001580772,0.0010189182,0.0025543785,0.002857337,0.0038222312,0.003940732],"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":[0.00012392535,0.00008150345,0.00046126565,0.00018739456,0.00009883149,0.00008628881,0.000088294146,0.4478057,0.0018269552,0.09822274,0.010548269,0.4404689],"study_design_scores_gemma":[0.000028706658,0.000015221423,0.00003489506,0.000014842921,0.000007974219,0.000021851352,0.0000065088147,0.9433806,0.0006143232,0.053609625,0.0022577005,0.000007804636],"about_ca_topic_score_codex":0.004149746,"about_ca_topic_score_gemma":0.00617531,"teacher_disagreement_score":0.00965349,"about_ca_system_score_codex":0.0012877599,"about_ca_system_score_gemma":0.0018724675,"threshold_uncertainty_score":0.032294095},"labels":[],"label_agreement":null},{"id":"W2138857742","doi":"","title":"Why Does Unsupervised Pre-training Help Deep Learning?","year":2010,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2115,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Unsupervised learning; Computer science; Machine learning; Deep learning; Regularization (linguistics); Generalization; Autoencoder; Deep belief network; Semi-supervised learning; Competitive learning; Training (meteorology); Mathematics","score_opus":0.01361732876810003,"score_gpt":0.24342165452423856,"score_spread":0.22980432575613852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138857742","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":[0.040264163,0.0061372565,0.92257977,0.018108258,0.00074339315,0.00014536019,0.00023584026,0.0037063682,0.008079656],"genre_scores_gemma":[0.47738534,0.0052749836,0.49935836,0.005797181,0.0012456169,0.00026737875,0.00068628736,0.0013558252,0.008629015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976816,0.0011626113,0.00010353805,0.00055177574,0.00031950657,0.00018092297],"domain_scores_gemma":[0.9823805,0.011531159,0.00065360713,0.0034069566,0.0015111957,0.0005166743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073125116,0.0017138337,0.0018773325,0.0005122933,0.00077987346,0.0018054518,0.0023020196,0.0033852016,0.0038705913],"category_scores_gemma":[0.03210307,0.00080385647,0.0007319591,0.0007758,0.0025385309,0.006976046,0.0021596826,0.005198376,0.003072922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010267205,0.00073044276,0.011177208,0.0014637661,0.0004199435,0.0002769861,0.0006978896,0.07441362,0.023562085,0.06667838,0.037004784,0.7825482],"study_design_scores_gemma":[0.00017676066,0.00069730484,0.006342016,0.00072262064,0.00016842173,0.0006857694,0.00059937243,0.5636077,0.039565958,0.35325608,0.033997696,0.0001802951],"about_ca_topic_score_codex":0.0015032744,"about_ca_topic_score_gemma":0.0027614476,"teacher_disagreement_score":0.0073125116,"about_ca_system_score_codex":0.00060670567,"about_ca_system_score_gemma":0.0011900272,"threshold_uncertainty_score":0.038672686},"labels":[],"label_agreement":null},{"id":"W2140045824","doi":"","title":"High Order Regularization for Semi-Supervised Learning of Structured Output Problems","year":2014,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"","keywords":"Computer science; Regularization (linguistics); Discriminative model; Machine learning; Artificial intelligence; Labeled data; Semi-supervised learning; Margin (machine learning); Graph; Structured prediction; Segmentation; Pattern recognition (psychology); Theoretical computer science","score_opus":0.014630176151953871,"score_gpt":0.2216546490827776,"score_spread":0.20702447293082374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140045824","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026204903,0.00011846193,0.9964258,0.000114418915,0.000010843544,0.000020195543,0.00003288327,0.00023601492,0.00042076217],"genre_scores_gemma":[0.2958354,0.0005050041,0.69740534,0.0002928727,0.00020645882,0.00035464813,0.0008106307,0.00043313342,0.0041564754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975078,0.0014557461,0.00009147981,0.00041743918,0.00044040504,0.0000871204],"domain_scores_gemma":[0.99457896,0.0033229433,0.00042626212,0.00092489424,0.00056952774,0.00017736641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00401192,0.0012348202,0.0014622569,0.0007479891,0.000603044,0.0012420216,0.0019868966,0.0018269453,0.0018592045],"category_scores_gemma":[0.011184221,0.00066373404,0.0008546379,0.0009589051,0.0024434424,0.0024790931,0.0022078499,0.0038975764,0.0008997751],"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":[0.00022694761,0.00016241566,0.00082463457,0.00032465882,0.00010143948,0.00012515202,0.00022508873,0.7430275,0.009510473,0.113661505,0.006218332,0.12559187],"study_design_scores_gemma":[0.0000051800794,0.000017945214,0.0000710005,0.0000070213114,0.0000026411199,0.000010640303,0.000005012462,0.97556096,0.00071461545,0.023245022,0.00035371157,0.0000062199615],"about_ca_topic_score_codex":0.0016810915,"about_ca_topic_score_gemma":0.0024261235,"teacher_disagreement_score":0.00401192,"about_ca_system_score_codex":0.0014315895,"about_ca_system_score_gemma":0.0011101501,"threshold_uncertainty_score":0.021217287},"labels":[],"label_agreement":null},{"id":"W2145494108","doi":"","title":"Semi-supervised Learning by Entropy Minimization","year":2004,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1022,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Semi-supervised learning; Machine learning; Computer science; Artificial intelligence; Entropy (arrow of time); Labeled data; Regularization (linguistics); Weighting; Unsupervised learning; Generative grammar; Robustness (evolution); Supervised learning; Mathematics; Pattern recognition (psychology); Artificial neural network","score_opus":0.010201128253287957,"score_gpt":0.22030987830713133,"score_spread":0.21010875005384338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145494108","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":[0.0026963502,0.00015576105,0.9962475,0.00014541793,0.000022600065,0.000031392556,0.00002719389,0.00014177905,0.00053200877],"genre_scores_gemma":[0.3250882,0.000693841,0.6684245,0.0004074581,0.0004599013,0.0004085591,0.00055377,0.00021334126,0.003750492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960758,0.002215027,0.00014456316,0.00080033793,0.0006520308,0.00011229702],"domain_scores_gemma":[0.9918711,0.0053775664,0.0005824936,0.0012600042,0.0006846935,0.00022420003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004513504,0.0011089367,0.0020523865,0.0012419784,0.00061727536,0.001610145,0.0024812543,0.0017051429,0.0019887034],"category_scores_gemma":[0.010142053,0.0006140173,0.0010088389,0.0013114423,0.0025047946,0.00301839,0.002766563,0.0026589911,0.0008975172],"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":[0.00017937152,0.00025387228,0.0015306749,0.0005502222,0.0003552549,0.00024092644,0.000393918,0.5437194,0.005768457,0.12613605,0.007316871,0.31355503],"study_design_scores_gemma":[0.000009187069,0.000050315954,0.00013047234,0.000014782413,0.0000098585615,0.00005505018,0.000014533386,0.9238861,0.0011806284,0.073560335,0.0010742652,0.000014548127],"about_ca_topic_score_codex":0.0005386747,"about_ca_topic_score_gemma":0.00070303015,"teacher_disagreement_score":0.004513504,"about_ca_system_score_codex":0.0007524657,"about_ca_system_score_gemma":0.0010017486,"threshold_uncertainty_score":0.023869991},"labels":[],"label_agreement":null},{"id":"W2147297756","doi":"10.1109/icmla.2007.84","title":"Modifying kernels using label information improves SVM classification performance","year":2007,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Genome Canada","keywords":"Support vector machine; Computer science; Artificial intelligence; Kernel (algebra); Pattern recognition (psychology); Tree kernel; Classifier (UML); Kernel method; Machine learning; Radial basis function kernel; Mathematics","score_opus":0.065105551887089,"score_gpt":0.29270707493078085,"score_spread":0.22760152304369186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147297756","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15864873,0.00019902772,0.8339056,0.00021396097,0.00007261601,0.000054354725,0.00008182724,0.0056124325,0.001211466],"genre_scores_gemma":[0.68152416,0.000102588296,0.316148,0.00008429193,0.000048762373,0.000053002484,0.00047495606,0.00035584,0.0012084392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820685,0.00059878186,0.00016114165,0.0004452092,0.0004550663,0.00013297427],"domain_scores_gemma":[0.9926859,0.0036950363,0.00057090074,0.0016555685,0.0012220301,0.0001705528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002794827,0.0011971799,0.0010837343,0.0009867168,0.0004021696,0.0012562703,0.00092797587,0.001016519,0.0010462641],"category_scores_gemma":[0.012148028,0.00033104533,0.0005715082,0.0009600231,0.0004865417,0.0032494087,0.00094778836,0.0016746497,0.0012767938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051709724,0.00069878803,0.0061345035,0.00014013656,0.00011567162,0.0001034591,0.00014496356,0.12442913,0.0664566,0.003458465,0.004369452,0.7934317],"study_design_scores_gemma":[0.00001911301,0.00008035313,0.0013578634,0.0000057416237,0.000018730429,0.00006177409,0.000026818077,0.97315073,0.02175854,0.0026367716,0.00085734576,0.000026251359],"about_ca_topic_score_codex":0.00091445155,"about_ca_topic_score_gemma":0.0010730436,"teacher_disagreement_score":0.002794827,"about_ca_system_score_codex":0.00046577503,"about_ca_system_score_gemma":0.0005971259,"threshold_uncertainty_score":0.014780641},"labels":[],"label_agreement":null},{"id":"W2147876569","doi":"10.1109/tkde.2015.2453171","title":"RankRC: Large-Scale Nonlinear Rare Class Ranking","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Kernel (algebra); Machine learning; Artificial intelligence; Focus (optics); Class (philosophy); Nonlinear system; Rare events; Algorithm; Computational complexity theory; Mathematics","score_opus":0.03825382451170466,"score_gpt":0.27483103722904034,"score_spread":0.23657721271733567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147876569","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016466463,0.00032197856,0.9778265,0.00031904515,0.00008279479,0.00014986524,0.0002312261,0.003042616,0.0015595279],"genre_scores_gemma":[0.3727706,0.0003319029,0.6167295,0.0004934108,0.00020449475,0.0003560472,0.0017615401,0.0006580561,0.006694471],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99667513,0.0011803788,0.00016136859,0.0005457434,0.0011583567,0.00027904872],"domain_scores_gemma":[0.993258,0.002465226,0.00064899714,0.0019307734,0.001405256,0.00029176826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045770067,0.0011736517,0.0020659203,0.0016903075,0.0010528067,0.0023503744,0.0030208358,0.0021857936,0.003967816],"category_scores_gemma":[0.014905957,0.000438874,0.00089996157,0.0013873106,0.0012734235,0.0030739696,0.0027506538,0.0024791944,0.0028511565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048385587,0.0004735461,0.002579386,0.00032901333,0.00014666597,0.0002509505,0.00017575012,0.22113615,0.009610462,0.037545115,0.03168866,0.6955804],"study_design_scores_gemma":[0.00002588075,0.00010273119,0.0003540216,0.000010543497,0.000011706781,0.0001400602,0.000029661958,0.97951114,0.0036355418,0.0137792295,0.0023750216,0.000024368845],"about_ca_topic_score_codex":0.0033337208,"about_ca_topic_score_gemma":0.0041384995,"teacher_disagreement_score":0.0045770067,"about_ca_system_score_codex":0.0010933189,"about_ca_system_score_gemma":0.0019069132,"threshold_uncertainty_score":0.024205863},"labels":[],"label_agreement":null},{"id":"W2150295085","doi":"10.1184/r1/6476456.v1","title":"Zero-Shot Learning with Semantic Output Codes","year":2018,"lang":"en","type":"article","venue":"Figshare","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":829,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Classifier (UML); Computer science; Artificial intelligence; Artificial neural network; Decoding methods; Training set; Formalism (music); Machine learning; Pattern recognition (psychology); Natural language processing; Algorithm","score_opus":0.05610884768842433,"score_gpt":0.26679728966136,"score_spread":0.21068844197293568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150295085","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":[0.033451553,0.00027514462,0.964308,0.0004488493,0.000035302106,0.000040275267,0.00008369994,0.0002651155,0.0010920283],"genre_scores_gemma":[0.8604097,0.00026501418,0.1349296,0.00042983115,0.00013694982,0.00016721185,0.0004895787,0.00009518199,0.003076996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986785,0.00040335575,0.000058196307,0.00043458663,0.00027717368,0.00014815721],"domain_scores_gemma":[0.99400204,0.0043893047,0.00029656995,0.000622458,0.00048147928,0.0002081421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029045441,0.00077262474,0.0017506926,0.00087957614,0.00059105223,0.0014641428,0.0026193834,0.002096519,0.0015341026],"category_scores_gemma":[0.013961177,0.0005052983,0.0008512415,0.0008943183,0.0031084395,0.0040758126,0.002674284,0.0025589252,0.00039330195],"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":[0.0005353144,0.0003425571,0.0039978786,0.00046330516,0.00021207145,0.00039688993,0.0005380465,0.6357438,0.008402872,0.17704369,0.0035216059,0.16880196],"study_design_scores_gemma":[0.000012201126,0.00004974085,0.00020729644,0.000011419455,0.000008110064,0.00004092706,0.000018958324,0.9149425,0.001541193,0.08284795,0.00030606613,0.000013630604],"about_ca_topic_score_codex":0.0023327353,"about_ca_topic_score_gemma":0.0016560985,"teacher_disagreement_score":0.0029045441,"about_ca_system_score_codex":0.0011796987,"about_ca_system_score_gemma":0.00089589,"threshold_uncertainty_score":0.015360892},"labels":[],"label_agreement":null},{"id":"W2150385401","doi":"","title":"One-Shot Learning with a Hierarchical Nonparametric Bayesian Model","year":2011,"lang":"en","type":"article","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; MNIST database; Prior probability; Bayesian probability; Machine learning; Metric (unit); Computer science; Similarity (geometry); Pattern recognition (psychology); Mathematics; Image (mathematics); Deep learning","score_opus":0.048729554109779305,"score_gpt":0.25454679067785213,"score_spread":0.20581723656807283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150385401","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008821011,0.00015471524,0.9891857,0.00031239807,0.000020236108,0.00003715996,0.00010968442,0.00028958195,0.0010695391],"genre_scores_gemma":[0.6248078,0.00045885763,0.36497092,0.0006799874,0.00014280592,0.0003953752,0.00085238466,0.00017573053,0.007516166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987803,0.00040622897,0.00004423142,0.000361459,0.0003123557,0.00009546956],"domain_scores_gemma":[0.99770737,0.0014423792,0.00014452776,0.00034979839,0.0002532988,0.00010255465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018163363,0.00059495127,0.0013040514,0.0008773303,0.00050892384,0.0011374506,0.003387904,0.0022431677,0.0024912187],"category_scores_gemma":[0.007442206,0.00080083346,0.0010952391,0.00089441455,0.0014066081,0.0030841539,0.0018931996,0.002609322,0.0009802053],"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":[0.00018852088,0.00023908043,0.0015684848,0.00022885951,0.00016464894,0.00016906863,0.0003337483,0.6782036,0.0058574183,0.1267879,0.0064120055,0.17984658],"study_design_scores_gemma":[0.000009155529,0.000020078922,0.00018087981,0.000008273588,0.000009846101,0.00003228354,0.0000067001615,0.9589229,0.0005044926,0.039736144,0.00055563886,0.000013579835],"about_ca_topic_score_codex":0.0068345307,"about_ca_topic_score_gemma":0.009505492,"teacher_disagreement_score":0.0068345307,"about_ca_system_score_codex":0.001165933,"about_ca_system_score_gemma":0.0013757547,"threshold_uncertainty_score":0.013589501},"labels":[],"label_agreement":null},{"id":"W2156557681","doi":"","title":"Impossibility Theorems for Domain Adaptation","year":2010,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":196,"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; University of Toronto; University of Waterloo","funders":"","keywords":"Domain adaptation; Computer science; Classifier (UML); Covariate; Artificial intelligence; Machine learning; Mathematical proof; Test data; Adaptation (eye); Class (philosophy); Mathematics","score_opus":0.016703126240313046,"score_gpt":0.2641089280584327,"score_spread":0.24740580181811966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156557681","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024541568,0.0011496317,0.92195624,0.00705442,0.00040686445,0.00020758894,0.0009887047,0.0013047206,0.042390306],"genre_scores_gemma":[0.70416313,0.002402097,0.25567216,0.0055717775,0.001928954,0.001906772,0.0039561153,0.0009560077,0.02344293],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9882251,0.004429776,0.0009226395,0.0033619967,0.002116745,0.000943762],"domain_scores_gemma":[0.8896976,0.09174687,0.0016917887,0.011843716,0.0033366838,0.0016833884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013141125,0.0017143184,0.0018570502,0.0024704377,0.0032410643,0.0051695798,0.0039994614,0.00446393,0.01403054],"category_scores_gemma":[0.09063596,0.001536517,0.0037757938,0.0023931547,0.006719774,0.015991196,0.013424225,0.01178627,0.002304384],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025432743,0.00011617548,0.0015347374,0.0004755179,0.00020716999,0.00045899593,0.0005605709,0.019861547,0.0014440296,0.9139284,0.0205369,0.040621646],"study_design_scores_gemma":[0.00005345653,0.000022778555,0.00033113267,0.000065905275,0.000041875883,0.0002837922,0.000080059945,0.06194636,0.0008079501,0.9316534,0.004681795,0.000031592757],"about_ca_topic_score_codex":0.0017308021,"about_ca_topic_score_gemma":0.0011872194,"teacher_disagreement_score":0.01403054,"about_ca_system_score_codex":0.002290405,"about_ca_system_score_gemma":0.0014655307,"threshold_uncertainty_score":0.069497764},"labels":[],"label_agreement":null},{"id":"W2160725942","doi":"","title":"The Consolidation of Task Knowledge for Lifelong Machine Learning","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Acadia University","funders":"","keywords":"Computer science; Lifelong learning; Task (project management); Consolidation (business); Domain knowledge; Knowledge transfer; Knowledge management; Artificial intelligence; Human–computer interaction; Engineering; Psychology; Systems engineering","score_opus":0.12035925711131597,"score_gpt":0.34744418537688576,"score_spread":0.2270849282655698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160725942","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":[0.033430297,0.0007757196,0.96117985,0.0011299059,0.000051585066,0.0001299856,0.00004577996,0.0005217091,0.0027350576],"genre_scores_gemma":[0.7295641,0.0006023318,0.26506522,0.0005382882,0.00013194907,0.0004490983,0.00020045518,0.00017196688,0.0032766038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984837,0.00057250744,0.0001458329,0.00041634223,0.00026768883,0.00011398929],"domain_scores_gemma":[0.98819345,0.0050796135,0.00097242184,0.0039269375,0.0011925856,0.0006349883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004438514,0.0005353933,0.0013571675,0.00077631447,0.0012409036,0.0025241803,0.0030756549,0.0017934231,0.0029355073],"category_scores_gemma":[0.024038378,0.00071230583,0.0006041264,0.0008524159,0.0032512732,0.008858116,0.006118584,0.0033104145,0.00088261225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031991056,0.0005107663,0.005126195,0.00043741896,0.00016744292,0.00023817919,0.0020102821,0.16262,0.014021031,0.22348495,0.0041200207,0.58694375],"study_design_scores_gemma":[0.000022737662,0.00016107857,0.00079694326,0.000049524526,0.000029792163,0.00012860245,0.00014884735,0.734088,0.003952849,0.25732908,0.0032521111,0.00004046247],"about_ca_topic_score_codex":0.0015232415,"about_ca_topic_score_gemma":0.0013690209,"teacher_disagreement_score":0.004438514,"about_ca_system_score_codex":0.0014857952,"about_ca_system_score_gemma":0.001395095,"threshold_uncertainty_score":0.023473442},"labels":[],"label_agreement":null},{"id":"W2169924623","doi":"10.1007/978-3-540-73053-8_31","title":"Requirements for Machine Lifelong Learning","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Lifelong learning; Artificial intelligence; Human–computer interaction; Pedagogy; Sociology","score_opus":0.04658765491235223,"score_gpt":0.30104778339089955,"score_spread":0.2544601284785473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169924623","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009123402,0.0013276667,0.9622519,0.006590384,0.00044266597,0.0002596326,0.0008293997,0.0014931466,0.017681904],"genre_scores_gemma":[0.30276734,0.0026773615,0.6513455,0.0022705954,0.0016297136,0.0017916047,0.0052805482,0.0017963438,0.030441012],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9928807,0.0017867989,0.00084233383,0.0008223305,0.003261252,0.000406406],"domain_scores_gemma":[0.9354757,0.03937808,0.0012875767,0.0109223295,0.011369547,0.0015667379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072095967,0.0006740409,0.0013503651,0.00072501163,0.000931976,0.0026593315,0.0027850857,0.0026305579,0.015168506],"category_scores_gemma":[0.07072047,0.0008253645,0.0008795203,0.00085347827,0.0015519956,0.010532206,0.005025249,0.0054008365,0.011466208],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042017686,0.00040744012,0.0019362182,0.001005112,0.000052979645,0.0003709788,0.00048787432,0.022965262,0.012663561,0.67402506,0.03174338,0.25392196],"study_design_scores_gemma":[0.000044549102,0.00013869537,0.0006810198,0.00013723687,0.000021779686,0.00047841846,0.0001678699,0.17320417,0.008488934,0.76792276,0.04867977,0.00003483585],"about_ca_topic_score_codex":0.0011170151,"about_ca_topic_score_gemma":0.0010670796,"teacher_disagreement_score":0.015168506,"about_ca_system_score_codex":0.00092552556,"about_ca_system_score_gemma":0.0015571143,"threshold_uncertainty_score":0.05074376},"labels":[],"label_agreement":null},{"id":"W2185249064","doi":"","title":"Domain Adaptation: A Small Sample Statistical Approach","year":2012,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Overfitting; Similarity (geometry); Artificial intelligence; Computer science; Sample (material); Pattern recognition (psychology); Domain (mathematical analysis); Statistic; Feature (linguistics); Greedy algorithm; Set (abstract data type); Machine learning; Variance (accounting); Feature selection; Selection (genetic algorithm); Object (grammar); Domain adaptation; Mathematics; Algorithm; Statistics; Artificial neural network; Image (mathematics)","score_opus":0.2080192309043449,"score_gpt":0.34113617773857846,"score_spread":0.13311694683423356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2185249064","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010068007,0.0001536711,0.9887561,0.00018497372,0.000026277043,0.0000697471,0.000023635777,0.00025152107,0.0004659808],"genre_scores_gemma":[0.5806179,0.0004619085,0.41371793,0.0006924925,0.00033161213,0.0006002956,0.0003643127,0.0002896459,0.0029240227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967919,0.0017611979,0.00010491334,0.0006794674,0.00055473647,0.00010779553],"domain_scores_gemma":[0.97308064,0.021866497,0.0009959079,0.0025275496,0.0011467757,0.00038271706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009643534,0.00092479965,0.001554279,0.0013247246,0.00064282486,0.0011500267,0.0027544193,0.0017022168,0.0015445078],"category_scores_gemma":[0.03794868,0.0006769871,0.0013309182,0.001126773,0.0022856863,0.002259946,0.002125565,0.0028724393,0.0004321486],"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":[0.0004480079,0.0004903407,0.009410111,0.00028955235,0.0005538034,0.0006345622,0.00040299166,0.6777333,0.008890086,0.062114142,0.0038333433,0.23519969],"study_design_scores_gemma":[0.000017087114,0.000085020394,0.00059744157,0.000008401076,0.000022013057,0.000093325536,0.000026241763,0.97947156,0.0012536062,0.017815,0.0005959691,0.000014228239],"about_ca_topic_score_codex":0.0017935777,"about_ca_topic_score_gemma":0.0012553888,"teacher_disagreement_score":0.009643534,"about_ca_system_score_codex":0.00086275616,"about_ca_system_score_gemma":0.0008934759,"threshold_uncertainty_score":0.051000535},"labels":[],"label_agreement":null},{"id":"W2196283579","doi":"10.1609/aaai.v29i1.9572","title":"A Probabilistic Covariate Shift Assumption for Domain Adaptation","year":2015,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"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 Waterloo","funders":"","keywords":"Covariate; Classifier (UML); Computer science; Probabilistic logic; Domain adaptation; Discriminative model; Artificial intelligence; Margin (machine learning); Labeled data; Pattern recognition (psychology); Domain (mathematical analysis); Algorithm; Machine learning; Mathematics","score_opus":0.21184496271369196,"score_gpt":0.32627189166883136,"score_spread":0.1144269289551394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2196283579","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":[0.008746059,0.00015599551,0.9895682,0.0002991544,0.000026151965,0.00006795852,0.00007087098,0.000311158,0.00075444975],"genre_scores_gemma":[0.5628433,0.0005960858,0.42792487,0.0009208875,0.00029227015,0.00070520554,0.00095847325,0.00021885034,0.0055401013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966396,0.0011253561,0.0001881004,0.0011432931,0.0007255517,0.00017814185],"domain_scores_gemma":[0.98934025,0.005529295,0.00066568935,0.0028300583,0.0013553824,0.00027938385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051642633,0.0010044556,0.0016314004,0.00095377566,0.0010990965,0.0016238536,0.003117005,0.0026188379,0.002766104],"category_scores_gemma":[0.028097175,0.0007227362,0.0016562475,0.0013660645,0.002211233,0.0057098316,0.0037579495,0.004797015,0.0016365594],"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":[0.00063607364,0.00033838075,0.008630219,0.00029783812,0.0002533284,0.00040761972,0.0007513537,0.49440604,0.020336738,0.1631605,0.00863143,0.3021505],"study_design_scores_gemma":[0.000031401847,0.00008001005,0.00076450134,0.000021446776,0.00002755071,0.00016008902,0.000034327786,0.9099869,0.004491756,0.08192797,0.0024455586,0.000028438793],"about_ca_topic_score_codex":0.0016292607,"about_ca_topic_score_gemma":0.0014679618,"teacher_disagreement_score":0.0051642633,"about_ca_system_score_codex":0.0011830081,"about_ca_system_score_gemma":0.0012513133,"threshold_uncertainty_score":0.027311563},"labels":[],"label_agreement":null},{"id":"W2218267073","doi":"10.1007/s12652-015-0296-5","title":"Bagging based ensemble transfer learning","year":2015,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"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 New Brunswick","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Transfer of learning; Classifier (UML); Artificial intelligence; Labeled data; Machine learning; Domain (mathematical analysis); Computational intelligence; Training set; Ensemble learning; Data mining; Data set; Set (abstract data type); Process (computing); Pattern recognition (psychology); Mathematics","score_opus":0.06679212953884972,"score_gpt":0.28558374997322894,"score_spread":0.21879162043437922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2218267073","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":[0.080439076,0.0012823358,0.9137332,0.00031732864,0.0002987789,0.000082077895,0.00012815355,0.0015628446,0.0021562197],"genre_scores_gemma":[0.8339554,0.00065948424,0.15402904,0.0004010983,0.00024295523,0.00017650286,0.0010094502,0.00023248197,0.009293622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893945,0.00039040032,0.000062679406,0.00026355367,0.00021980364,0.00012418431],"domain_scores_gemma":[0.9964353,0.0018270276,0.0001089673,0.0006818547,0.00082829833,0.00011857925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002788608,0.0008921003,0.002007605,0.0012708058,0.00090032566,0.0009606558,0.0022773568,0.0016688077,0.0025079434],"category_scores_gemma":[0.005465953,0.0005002765,0.0011269769,0.0013487185,0.00066884607,0.0040075574,0.0025591152,0.0023662176,0.0010122977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003512347,0.0007353784,0.0024290562,0.000109704066,0.00037607466,0.00008718588,0.00017141162,0.32272452,0.006422722,0.0074110297,0.0068840785,0.6522976],"study_design_scores_gemma":[0.000005069858,0.00004266772,0.0002732874,0.000004198653,0.000021017606,0.000017223492,0.000013218284,0.9940726,0.0010077076,0.004252546,0.00028244345,0.000007976463],"about_ca_topic_score_codex":0.0026776982,"about_ca_topic_score_gemma":0.0033408408,"teacher_disagreement_score":0.002788608,"about_ca_system_score_codex":0.00056009064,"about_ca_system_score_gemma":0.00081745867,"threshold_uncertainty_score":0.014747739},"labels":[],"label_agreement":null},{"id":"W2218417636","doi":"10.1609/aaai.v25i1.7925","title":"Heterogeneous Transfer Learning with RBMs","year":2011,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Transfer of learning; Multi-task learning; Machine learning; Artificial intelligence; Boltzmann machine; Feature (linguistics); Task (project management); Semi-supervised learning; Feature learning; Online machine learning; Disjoint sets; Probabilistic logic; Restricted Boltzmann machine; Feature vector; Adaptation (eye); Deep learning; Mathematics","score_opus":0.09964339697449759,"score_gpt":0.2598105776249844,"score_spread":0.1601671806504868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2218417636","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":[0.016427934,0.00035019845,0.9794519,0.0003438217,0.00005837484,0.00009222928,0.00008017017,0.0013417292,0.0018535923],"genre_scores_gemma":[0.7562898,0.00026873953,0.23552872,0.00060974766,0.00013833011,0.00058477744,0.00042715677,0.00033558565,0.0058172513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99767333,0.001205063,0.00009865192,0.0005236934,0.00031140426,0.00018794673],"domain_scores_gemma":[0.996491,0.0019518503,0.00022211245,0.0007605418,0.00042373704,0.00015073779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043607606,0.0014083083,0.0020084488,0.000972197,0.00078145874,0.0013289244,0.003957685,0.0022786094,0.0044286074],"category_scores_gemma":[0.010975373,0.00078162114,0.0015304252,0.0011940825,0.0015211485,0.0035500978,0.0037130201,0.0025784995,0.0019622983],"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":[0.00028273626,0.00025393534,0.00091525854,0.00013400373,0.00017286617,0.00018090876,0.00021664934,0.75656474,0.001987835,0.038245343,0.0031781148,0.19786756],"study_design_scores_gemma":[0.000015335254,0.000025123383,0.00005411689,0.0000046877226,0.0000071236423,0.000011596495,0.000011714284,0.9715575,0.00041666004,0.027517937,0.00037116907,0.0000070459423],"about_ca_topic_score_codex":0.003199083,"about_ca_topic_score_gemma":0.0023029977,"teacher_disagreement_score":0.0044286074,"about_ca_system_score_codex":0.0012854757,"about_ca_system_score_gemma":0.0010188144,"threshold_uncertainty_score":0.02306223},"labels":[],"label_agreement":null},{"id":"W2218835453","doi":"10.1609/aaai.v29i1.9607","title":"Online Boosting Algorithms for Anytime Transfer and Multitask Learning","year":2015,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Boosting (machine learning); Computer science; Transfer of learning; Multi-task learning; Leverage (statistics); Machine learning; Artificial intelligence; Online machine learning; Instance-based learning; Semi-supervised learning; Algorithm; Task (project management)","score_opus":0.17525614189589536,"score_gpt":0.3293227549776878,"score_spread":0.15406661308179245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2218835453","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":[0.0037558626,0.00081419887,0.99323964,0.00022636325,0.00010065982,0.00006353289,0.000051819137,0.0005813032,0.0011665641],"genre_scores_gemma":[0.28612754,0.0012679647,0.7033962,0.0006950179,0.000658154,0.0007062458,0.00071762403,0.00043298185,0.0059982473],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977806,0.0009587131,0.00012412391,0.00041931635,0.00051293074,0.00020434152],"domain_scores_gemma":[0.99513996,0.0028003834,0.00028320737,0.0007886744,0.0007635668,0.00022421413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005368375,0.0015256628,0.002791435,0.0015639599,0.00093116873,0.0013877407,0.0034489857,0.0022280356,0.0046961675],"category_scores_gemma":[0.012857203,0.0006816312,0.0014268842,0.0017927514,0.0011644041,0.003043652,0.0025002456,0.0038253379,0.0025940582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028775304,0.00051109673,0.0015089286,0.00038773843,0.000206589,0.00013376324,0.00017958164,0.44818735,0.003039323,0.08064626,0.0151214115,0.4497903],"study_design_scores_gemma":[0.000026858625,0.00004934553,0.00014252115,0.000014866604,0.00001364283,0.000038864287,0.000010686486,0.94778454,0.00064098445,0.04905132,0.0022153382,0.00001104771],"about_ca_topic_score_codex":0.0015552619,"about_ca_topic_score_gemma":0.0015386635,"teacher_disagreement_score":0.005368375,"about_ca_system_score_codex":0.0011670481,"about_ca_system_score_gemma":0.0016803761,"threshold_uncertainty_score":0.028391063},"labels":[],"label_agreement":null},{"id":"W2227271892","doi":"10.1007/978-3-319-24486-0_13","title":"Multi-task and Lifelong Learning of Kernels","year":2015,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 Waterloo","funders":"","keywords":"Generalization error; Multi-task learning; Lifelong learning; Computer science; Kernel (algebra); Artificial intelligence; Task (project management); Classifier (UML); Machine learning; Generalization; Instance-based learning; Margin (machine learning); Support vector machine; Multiple kernel learning; Semi-supervised learning; Kernel method; Mathematics; Stability (learning theory); Discrete mathematics; Psychology; Engineering","score_opus":0.03340130369327997,"score_gpt":0.28944819858851745,"score_spread":0.25604689489523746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2227271892","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1117974,0.0015786603,0.88356596,0.00070467696,0.00015387793,0.0000507743,0.00021473854,0.0007647558,0.0011690686],"genre_scores_gemma":[0.90968055,0.0005001858,0.08090904,0.0002444243,0.00017011768,0.00009971276,0.00079073076,0.00018433268,0.0074208817],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99872607,0.00046265562,0.00005627276,0.0004350913,0.00013684071,0.00018309338],"domain_scores_gemma":[0.992591,0.0044521377,0.00038291205,0.0014129629,0.0007013621,0.0004596544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003739898,0.00084615964,0.0015258437,0.0006850446,0.0006209432,0.0013362246,0.0020213057,0.0023045233,0.0024435238],"category_scores_gemma":[0.015225446,0.0005903159,0.0008306599,0.00084146886,0.0009643027,0.003968278,0.0029834318,0.0031409962,0.0008433801],"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":[0.0010449567,0.0009433836,0.007880007,0.0004955612,0.000384832,0.00038396378,0.00042421828,0.40523675,0.019939484,0.038634103,0.013278623,0.511354],"study_design_scores_gemma":[0.000011874418,0.000066295834,0.0010401277,0.000011831462,0.0000183272,0.00006338802,0.000027818382,0.9745138,0.0014712611,0.022245696,0.00051256455,0.000017006463],"about_ca_topic_score_codex":0.003382163,"about_ca_topic_score_gemma":0.0041095586,"teacher_disagreement_score":0.003739898,"about_ca_system_score_codex":0.00082946196,"about_ca_system_score_gemma":0.0009645545,"threshold_uncertainty_score":0.019778669},"labels":[],"label_agreement":null},{"id":"W2237608149","doi":"10.1007/s00521-016-2189-8","title":"Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier","year":2016,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Ludong University; National Natural Science Foundation of China","keywords":"Hinge loss; Artificial intelligence; Feature vector; Computer science; Discriminative model; Mathematics; Gradient descent; Linear classifier; Classifier (UML); Pattern recognition (psychology); Data point; Semi-supervised learning; Optimization problem; Algorithm; Artificial neural network; Support vector machine","score_opus":0.027026351452036915,"score_gpt":0.27032530089362206,"score_spread":0.24329894944158514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2237608149","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":[0.010608617,0.000431332,0.98772687,0.00008078106,0.000062353865,0.00003149272,0.00008677285,0.0004937348,0.00047808004],"genre_scores_gemma":[0.50969315,0.0008520869,0.48346868,0.0003753142,0.0003283598,0.00019672683,0.001383173,0.00025108483,0.0034514836],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887854,0.00018469068,0.00006138202,0.0004783634,0.00023939849,0.00015771494],"domain_scores_gemma":[0.9984837,0.0005530853,0.00016440073,0.00029718404,0.00039494928,0.000106663116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001633939,0.0011221018,0.0031039964,0.0012162908,0.00048079147,0.0013246357,0.002205094,0.0015305331,0.001798316],"category_scores_gemma":[0.0037831531,0.00074425514,0.0015073222,0.0014484705,0.000715897,0.0021862865,0.0020402437,0.0019174169,0.0009151637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070257456,0.00041072734,0.0026489345,0.00035961214,0.0004311619,0.00017489222,0.0001707664,0.17371745,0.050073955,0.00802722,0.0069234744,0.7563593],"study_design_scores_gemma":[0.0000125610895,0.000055475535,0.00055728236,0.000010365474,0.00004138929,0.00007160531,0.000018320847,0.9887832,0.0050198454,0.0047673774,0.0006467209,0.000015937692],"about_ca_topic_score_codex":0.002961957,"about_ca_topic_score_gemma":0.0038910746,"teacher_disagreement_score":0.0031039964,"about_ca_system_score_codex":0.00049499585,"about_ca_system_score_gemma":0.0012180627,"threshold_uncertainty_score":0.008641243},"labels":[],"label_agreement":null},{"id":"W2248206634","doi":"","title":"Position Paper: Representation Search through Generate and Test","year":2013,"lang":"en","type":"article","venue":"Symposium on Abstraction, Reformulation and Approximation","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Representation (politics); Artificial intelligence; Machine learning; Feature learning; Search problem; Simple (philosophy); Artificial neural network; Element (criminal law); Theoretical computer science; Algorithm","score_opus":0.02071115116405118,"score_gpt":0.26619460171127557,"score_spread":0.2454834505472244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2248206634","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011414085,0.0014165704,0.96779233,0.0045853066,0.001126055,0.00018638161,0.00027898277,0.0025943117,0.0106060915],"genre_scores_gemma":[0.27596632,0.0015100101,0.6810084,0.003276294,0.0022268463,0.00042770535,0.002250452,0.002058638,0.031275373],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972658,0.0012116275,0.000105043335,0.000644381,0.0006214345,0.00015167409],"domain_scores_gemma":[0.9875416,0.008439149,0.0004286664,0.0019809287,0.0011980929,0.00041154466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048813066,0.0011005858,0.0012660325,0.0009628958,0.00085940695,0.0027241898,0.0030293483,0.0031226405,0.015098417],"category_scores_gemma":[0.028397538,0.0005347329,0.0008767353,0.0011396647,0.0020667512,0.007429593,0.0019004452,0.003272427,0.004638122],"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":[0.00049451203,0.00043645655,0.0017951448,0.00028287966,0.00011819392,0.00011639734,0.00019667535,0.115689285,0.0031172459,0.17880078,0.08833967,0.61061275],"study_design_scores_gemma":[0.00014677552,0.0003397799,0.00042224512,0.000091289134,0.00006440333,0.00021935577,0.000054915457,0.79934996,0.008358265,0.15368098,0.03720992,0.00006215139],"about_ca_topic_score_codex":0.0019483782,"about_ca_topic_score_gemma":0.0015180634,"teacher_disagreement_score":0.015098417,"about_ca_system_score_codex":0.001510054,"about_ca_system_score_gemma":0.0017916637,"threshold_uncertainty_score":0.050509274},"labels":[],"label_agreement":null},{"id":"W2286284940","doi":"10.1016/j.ins.2016.01.004","title":"Vehicle detection from highway satellite images via transfer learning","year":2016,"lang":"en","type":"article","venue":"Information Sciences","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":56,"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 Waterloo","funders":"","keywords":"Computer science; Transfer of learning; Detector; Artificial intelligence; Satellite; Aerial image; Computer vision; Domain (mathematical analysis); Key (lock); Coding (social sciences); Image (mathematics); Pattern recognition (psychology); Remote sensing; Real-time computing; Telecommunications","score_opus":0.015231242864811846,"score_gpt":0.23000025457700823,"score_spread":0.2147690117121964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2286284940","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":[0.077173844,0.0005589364,0.91931283,0.00024067657,0.00006275819,0.0000535281,0.00015965686,0.0013237312,0.0011139603],"genre_scores_gemma":[0.8244408,0.00050867186,0.16862912,0.00022708633,0.00013542848,0.00009739924,0.0009358222,0.00011808631,0.004907669],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996642,0.00008501711,0.000013516652,0.00011775573,0.000071477654,0.0000481356],"domain_scores_gemma":[0.9991398,0.00041091235,0.00007910314,0.0001684228,0.00016198592,0.000039820952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076671096,0.0007237683,0.0010858636,0.0011718945,0.0002662935,0.0005353313,0.0012623583,0.0012184317,0.001006145],"category_scores_gemma":[0.002412135,0.00037223278,0.0006978655,0.000924856,0.0005926797,0.0014982012,0.0011829772,0.0011774971,0.0006488721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035667993,0.00030784615,0.002313157,0.0002061071,0.0002192016,0.00014991952,0.000107030115,0.3088263,0.03461742,0.0040693996,0.00436169,0.6444652],"study_design_scores_gemma":[0.0000052477994,0.00002902307,0.00063874386,0.000003958384,0.0000111200625,0.0000290182,0.0000124142625,0.9906557,0.0044476693,0.003884396,0.00027635795,0.000006275321],"about_ca_topic_score_codex":0.0035947226,"about_ca_topic_score_gemma":0.0032735425,"teacher_disagreement_score":0.0035947226,"about_ca_system_score_codex":0.00044512632,"about_ca_system_score_gemma":0.0005821463,"threshold_uncertainty_score":0.00714761},"labels":[],"label_agreement":null},{"id":"W22873392","doi":"10.1007/978-3-642-22092-0_50","title":"Generalized Sparse Regularization with Application to fMRI Brain Decoding","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"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 British Columbia Hospital","funders":"","keywords":"Interpretability; Computer science; Curse of dimensionality; Regularization (linguistics); Lasso (programming language); Artificial intelligence; Machine learning; Neural coding; Sparse approximation; Support vector machine; Pattern recognition (psychology); Elastic net regularization; Feature selection","score_opus":0.02277957863194294,"score_gpt":0.2502469817624809,"score_spread":0.22746740313053795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W22873392","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024405057,0.0001529439,0.99658716,0.00013256808,0.000019882338,0.000013214139,0.00003093203,0.00035392356,0.000268903],"genre_scores_gemma":[0.08520791,0.0005149279,0.9089982,0.00016974774,0.00011910894,0.00012967866,0.0002741501,0.00050133996,0.00408487],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996196,0.00019403074,0.000019553305,0.000059620423,0.0000800273,0.000027123318],"domain_scores_gemma":[0.99868745,0.000856799,0.00006120902,0.00015770493,0.00019206556,0.00004477259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013181431,0.0009095308,0.0012404267,0.00067820697,0.0004138083,0.0007492637,0.0011254895,0.0019202305,0.0024178927],"category_scores_gemma":[0.0053789625,0.00066693145,0.0009688096,0.0012362807,0.0007162211,0.00094051904,0.0016852677,0.0018990004,0.00097445317],"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":[0.00022176604,0.0001434917,0.00038428823,0.00029093854,0.00017716749,0.00018553188,0.00019346985,0.43563396,0.03660928,0.037956167,0.008195722,0.4800082],"study_design_scores_gemma":[0.000009831349,0.000017141861,0.00009197529,0.0000048566053,0.0000099831705,0.00004637254,0.0000062305776,0.98283285,0.0026425654,0.013293833,0.0010351914,0.000009176007],"about_ca_topic_score_codex":0.0031794368,"about_ca_topic_score_gemma":0.0046117455,"teacher_disagreement_score":0.0031794368,"about_ca_system_score_codex":0.00026819963,"about_ca_system_score_gemma":0.00093223655,"threshold_uncertainty_score":0.008088648},"labels":[],"label_agreement":null},{"id":"W2293319454","doi":"","title":"Correcting covariate shift with the Frank-Wolfe algorithm","year":2015,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"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":"Covariate; Kernel (algebra); Matching (statistics); Computer science; Algorithm; Mathematics; Statistics; Econometrics","score_opus":0.1779524532938985,"score_gpt":0.34159124758799364,"score_spread":0.16363879429409514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293319454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00491892,0.000092431874,0.99423367,0.00009123355,0.000021491023,0.000022932405,0.000017399247,0.0003553128,0.00024659853],"genre_scores_gemma":[0.17778254,0.00013498064,0.81958187,0.00019983691,0.000048985967,0.00012626077,0.00014413988,0.00016971458,0.0018117136],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99783117,0.0008242122,0.00013662112,0.0005212782,0.0005631434,0.00012359062],"domain_scores_gemma":[0.9960167,0.0018774866,0.00034182295,0.001034382,0.0006124683,0.00011721401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042498806,0.00074111205,0.0015193751,0.0009976574,0.0008834894,0.0012140065,0.0018249325,0.002179965,0.0018270906],"category_scores_gemma":[0.019616354,0.0007177065,0.0008733697,0.0010177928,0.001238775,0.0027871113,0.002594838,0.0019018956,0.00082391396],"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":[0.00023855298,0.0001353235,0.0027577851,0.00015129591,0.00013853064,0.0001830466,0.0003122327,0.4554793,0.008195565,0.05838688,0.005405824,0.46861568],"study_design_scores_gemma":[0.000019972756,0.00004674588,0.00031169347,0.000010150604,0.000011225839,0.00010582499,0.00002002303,0.9676068,0.0032217063,0.02686654,0.0017598552,0.000019369229],"about_ca_topic_score_codex":0.00258281,"about_ca_topic_score_gemma":0.002420301,"teacher_disagreement_score":0.0042498806,"about_ca_system_score_codex":0.00077900296,"about_ca_system_score_gemma":0.0019809853,"threshold_uncertainty_score":0.022475839},"labels":[],"label_agreement":null},{"id":"W2295429881","doi":"10.15353/vsnl.v1i1.44","title":"Domain Adaptation and Transfer Learning in StochasticNets","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Transfer of learning; Field (mathematics); Domain (mathematical analysis); Artificial neural network; Domain adaptation; Transfer of training; Adaptation (eye); Instance-based learning; Inductive transfer","score_opus":0.02807160770159533,"score_gpt":0.25253800773274837,"score_spread":0.22446640003115303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295429881","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":[0.062362332,0.0005355374,0.9321932,0.00040151877,0.00008271778,0.00005024255,0.00010741638,0.0013694682,0.0028975292],"genre_scores_gemma":[0.8533658,0.00037167754,0.13954666,0.0003672652,0.00008646278,0.0001213995,0.0005748121,0.00016719628,0.0053986446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995196,0.0001835064,0.000021831602,0.00013796169,0.00008105756,0.00005612188],"domain_scores_gemma":[0.9990395,0.00047621332,0.00007656124,0.00021191273,0.00012776566,0.00006799686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012548473,0.0007086567,0.0007313511,0.00043545658,0.0003528449,0.0006865279,0.0013025786,0.0010872855,0.0020946425],"category_scores_gemma":[0.004393206,0.00034410556,0.00059026916,0.0004843225,0.00097609696,0.0018995345,0.0018523971,0.0014550934,0.00060789165],"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":[0.0001680762,0.00014275654,0.0013340936,0.000087895845,0.00006188801,0.00012851479,0.000108364366,0.79740447,0.0064178305,0.029185133,0.0033161992,0.16164474],"study_design_scores_gemma":[0.0000040312934,0.00002297296,0.00010382486,0.0000033575664,0.000002508507,0.000018391938,0.000006865261,0.98955476,0.000941115,0.008955386,0.00038317015,0.0000035673752],"about_ca_topic_score_codex":0.0040458194,"about_ca_topic_score_gemma":0.0033468667,"teacher_disagreement_score":0.0040458194,"about_ca_system_score_codex":0.0008197719,"about_ca_system_score_gemma":0.0007651045,"threshold_uncertainty_score":0.008044541},"labels":[],"label_agreement":null},{"id":"W2295582178","doi":"","title":"Deep Learning of Representations for Unsupervised and Transfer Learning.","year":2011,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":894,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Transfer of learning; Unsupervised learning; Artificial intelligence; Computer science; Machine learning; Exploit; Task (project management); Context (archaeology); Feature learning; Competitive learning; Distribution (mathematics); Mathematics","score_opus":0.04742475324231625,"score_gpt":0.2623767505798368,"score_spread":0.21495199733752052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295582178","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024632232,0.0007462402,0.9936993,0.0005080912,0.00008531246,0.000036421858,0.00016492011,0.00058800966,0.0017084394],"genre_scores_gemma":[0.33773845,0.0025906614,0.6472268,0.0007234645,0.00038164476,0.00050145114,0.0021077422,0.00045441277,0.008275401],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900144,0.00039664886,0.00005749373,0.0002485709,0.00022541026,0.00007043258],"domain_scores_gemma":[0.99729913,0.0012651423,0.00020541064,0.0008919143,0.00023518395,0.000103285965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021149635,0.0009811481,0.0009021982,0.0009426837,0.000523902,0.0018027399,0.0021175304,0.0016318774,0.003656174],"category_scores_gemma":[0.011106831,0.00069207157,0.0009936937,0.0012639613,0.0020592648,0.0046444465,0.0028129674,0.0038138996,0.0014487202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012504624,0.00015726485,0.0011433811,0.0004514044,0.00021091427,0.00017323384,0.000192346,0.23552121,0.0043549743,0.39417866,0.02221937,0.3412722],"study_design_scores_gemma":[0.000012685552,0.000031561704,0.00021932332,0.000052853844,0.000016312011,0.00006313464,0.000026472819,0.6341979,0.0019124852,0.35695222,0.0065005952,0.000014453958],"about_ca_topic_score_codex":0.0015670216,"about_ca_topic_score_gemma":0.0024975652,"teacher_disagreement_score":0.003656174,"about_ca_system_score_codex":0.0014053817,"about_ca_system_score_gemma":0.0012987859,"threshold_uncertainty_score":0.0122311115},"labels":[],"label_agreement":null},{"id":"W2296546450","doi":"10.1109/icip.2015.7350766","title":"Clustered Exemplar-SVM: Discovering sub-categories for visual recognition","year":2015,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer science; Support vector machine; Classifier (UML); Pattern recognition (psychology); Machine learning; Class (philosophy)","score_opus":0.08265065258435923,"score_gpt":0.297718458073159,"score_spread":0.21506780548879978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296546450","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012958288,0.00021120651,0.98397934,0.00009668458,0.00004185626,0.00007209594,0.00016811783,0.0017492145,0.000723172],"genre_scores_gemma":[0.22718206,0.00018765939,0.76876146,0.00020887256,0.00007080938,0.00017240082,0.0011688147,0.000261976,0.0019859618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900085,0.00018145563,0.00005218511,0.00034132585,0.00030937666,0.00011477085],"domain_scores_gemma":[0.9984459,0.00038659046,0.00014407815,0.00043585492,0.0004870955,0.000100564925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012675329,0.0009629853,0.0013484181,0.001892496,0.00053487637,0.00128275,0.0030075011,0.0017357037,0.0022649816],"category_scores_gemma":[0.0036924935,0.00040451525,0.0009614781,0.0016908234,0.00084087974,0.0020781693,0.0016872359,0.001915055,0.001775919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015328542,0.00028465182,0.0028728892,0.00017412053,0.00011297633,0.00009325122,0.00015193698,0.059533905,0.029401375,0.010480968,0.011606354,0.8851343],"study_design_scores_gemma":[0.000008346456,0.00007032339,0.0009007293,0.000018715518,0.000014531149,0.00013528546,0.00004853686,0.97034544,0.01254519,0.012526151,0.003367396,0.000019373083],"about_ca_topic_score_codex":0.002332083,"about_ca_topic_score_gemma":0.003271533,"teacher_disagreement_score":0.0030075011,"about_ca_system_score_codex":0.00065384794,"about_ca_system_score_gemma":0.0007694058,"threshold_uncertainty_score":0.0075771213},"labels":[],"label_agreement":null},{"id":"W2313667075","doi":"10.5244/c.29.37","title":"Multi-Task Transfer Methods to Improve One-Shot Learning for Multimedia Event Detection","year":2015,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Relevance (law); Event (particle physics); Set (abstract data type); Task (project management); A priori and a posteriori; Artificial intelligence; Machine learning; Transfer of learning","score_opus":0.11843499437329083,"score_gpt":0.37647860959777085,"score_spread":0.25804361522448005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313667075","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":[0.018938076,0.0008160694,0.9767368,0.00016811726,0.00013322971,0.00012099473,0.000118413926,0.0017920283,0.0011763986],"genre_scores_gemma":[0.5960681,0.0007524496,0.39182326,0.0006158564,0.00045314906,0.00041082824,0.0014738276,0.0004562902,0.007946247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883753,0.00034324182,0.000064725646,0.00042145004,0.00022028566,0.00011268702],"domain_scores_gemma":[0.99731284,0.001307968,0.00017332456,0.00046138366,0.0005899083,0.0001545636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023618664,0.0016979884,0.0014477376,0.0013138308,0.00057132007,0.0008290759,0.0027595586,0.0016248046,0.002976579],"category_scores_gemma":[0.00725642,0.00049233134,0.0011765895,0.0011881578,0.00063191366,0.0025732915,0.0019209692,0.0027337396,0.0018921889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037221235,0.0009970919,0.0019955868,0.00022498619,0.00027464752,0.0001698523,0.00019099173,0.16319697,0.016110938,0.0034391927,0.0076730815,0.8053544],"study_design_scores_gemma":[0.000009509574,0.000088778186,0.0004312337,0.0000070474644,0.000017685956,0.000037257058,0.000020040166,0.9922463,0.0030617623,0.0033748082,0.0006925875,0.000012961752],"about_ca_topic_score_codex":0.0034042185,"about_ca_topic_score_gemma":0.0031418707,"teacher_disagreement_score":0.0034042185,"about_ca_system_score_codex":0.0007639265,"about_ca_system_score_gemma":0.00083650753,"threshold_uncertainty_score":0.012490869},"labels":[],"label_agreement":null},{"id":"W2319177537","doi":"10.1177/1471301214561160","title":"Amanda Alders Pike, <i>Improving memory through creativity</i>","year":2014,"lang":"en","type":"article","venue":"Dementia","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Canadian Association of Occupational Therapists","funders":"","keywords":"Pike; Creativity; Psychology; Cognitive psychology; Cognitive science; Social psychology","score_opus":0.015237747439096129,"score_gpt":0.23371451754655795,"score_spread":0.21847677010746183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2319177537","genre_codex":"commentary","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":[0.005114564,0.08577408,0.033666372,0.73103875,0.087171674,0.00016060112,0.0010572003,0.0018192513,0.054197546],"genre_scores_gemma":[0.10773655,0.11557643,0.0473598,0.24461415,0.08336288,0.00047456656,0.0018033251,0.001948655,0.3971236],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990439,0.00022900068,0.000039086117,0.00013693048,0.0004793727,0.000071729984],"domain_scores_gemma":[0.99648273,0.0011999682,0.00013210971,0.00015894463,0.0014560876,0.00057015667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017380567,0.0006735637,0.00044007905,0.0012001524,0.0011303341,0.0024552506,0.00095446606,0.0022224085,0.009833708],"category_scores_gemma":[0.009244548,0.00026201413,0.00042316568,0.0007319905,0.0008394731,0.0019755636,0.0012556507,0.0033867338,0.005973375],"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":[0.000099485056,0.000044082026,0.00044662372,0.00016515577,0.00002145528,0.00006596734,0.00013833672,0.00020500095,0.000907189,0.0036878027,0.8837726,0.11044626],"study_design_scores_gemma":[0.00008224329,0.00011232001,0.0022979665,0.0003862809,0.000075608354,0.0006757865,0.0006511805,0.0016568484,0.006461068,0.019791814,0.96773213,0.000076609474],"about_ca_topic_score_codex":0.0081423195,"about_ca_topic_score_gemma":0.01582734,"teacher_disagreement_score":0.009833708,"about_ca_system_score_codex":0.00072411186,"about_ca_system_score_gemma":0.0009674499,"threshold_uncertainty_score":0.032897055},"labels":[],"label_agreement":null},{"id":"W2413867752","doi":"10.1016/j.neuroimage.2016.05.053","title":"Domain adaptation for Alzheimer's disease diagnostics","year":2016,"lang":"en","type":"article","venue":"NeuroImage","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Fujirebio Europe; Eisai; National Institute on Aging; Commonwealth Scientific and Industrial Research Organisation; Northern California Institute for Research and Education; Takeda Pharmaceutical Company; Pfizer; BioClinica; Nvidia; National Center for Research Resources; F. Hoffmann-La Roche; Medpace; Genentech; Alexander von Humboldt-Stiftung; Biogen Idec; Massachusetts General Hospital; Synarc; Roche; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Research Foundation; Merck; Alzheimer's Drug Discovery Foundation; Alzheimer's Association; National Cancer Institute; Servier; Genentech Foundation; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Harvard NeuroDiscovery Center; Foundation for the National Institutes of Health","keywords":"Computer science; Overfitting; Weighting; Classifier (UML); Artificial intelligence; Machine learning; Generalizability theory; Domain adaptation; Adaptation (eye); Pattern recognition (psychology); Data mining; Artificial neural network; Medicine; Statistics","score_opus":0.04434282300726376,"score_gpt":0.2709108850951128,"score_spread":0.22656806208784902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2413867752","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":[0.040772058,0.004721497,0.9505716,0.00055140915,0.00014401092,0.00007509226,0.0003526702,0.0019044164,0.0009073396],"genre_scores_gemma":[0.66408384,0.002897169,0.325145,0.0007171498,0.00024143477,0.00018869335,0.0020638765,0.0003528108,0.00430999],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994598,0.00023050608,0.000032721786,0.00015356553,0.00007641215,0.00004701469],"domain_scores_gemma":[0.9987276,0.0007210233,0.00007036394,0.00016471195,0.00026029904,0.000055959557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021629424,0.00087253714,0.0012097608,0.0014477265,0.00039098185,0.00079219893,0.0012414899,0.001460201,0.0010708567],"category_scores_gemma":[0.0048830044,0.00036207627,0.001107753,0.00084213226,0.0005205535,0.0009269794,0.0011185127,0.0018008995,0.0006810163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000552529,0.00042946753,0.004667685,0.0003684078,0.00050796656,0.0002324902,0.00011373171,0.2516041,0.017990762,0.0037119905,0.01180552,0.7080153],"study_design_scores_gemma":[0.000020573709,0.000083036364,0.0021370833,0.000029813344,0.000078844605,0.00023235282,0.000039980016,0.9767463,0.006497315,0.012273723,0.0018382495,0.000022714521],"about_ca_topic_score_codex":0.004703564,"about_ca_topic_score_gemma":0.00440252,"teacher_disagreement_score":0.004703564,"about_ca_system_score_codex":0.00052055524,"about_ca_system_score_gemma":0.00090937,"threshold_uncertainty_score":0.011438906},"labels":[],"label_agreement":null},{"id":"W2416782994","doi":"10.2139/ssrn.2782670","title":"Singular Ridge Regression with Homoscedastic Residuals: Generalization Error with Estimated Parameters","year":2016,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Nationale de la Recherche; Ottawa Hospital Research Institute","keywords":"Homoscedasticity; Generalization; Estimator; Regression; Mathematics; Regression analysis; Ridge; Applied mathematics; Statistics; Econometrics; Heteroscedasticity; Mathematical analysis","score_opus":0.02427458174755688,"score_gpt":0.27863587230510306,"score_spread":0.2543612905575462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2416782994","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025097718,0.0006340558,0.9727384,0.00031970438,0.000058064797,0.000017528157,0.00007004933,0.00040289934,0.0006615487],"genre_scores_gemma":[0.60675377,0.0013066274,0.3819777,0.0003795796,0.00024003316,0.00008931698,0.0007683317,0.000563547,0.007920986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890447,0.00043501484,0.000059069327,0.00035475078,0.0001784459,0.000068250316],"domain_scores_gemma":[0.9942947,0.0036383849,0.00028759343,0.0011676695,0.00044508945,0.00016660671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049131117,0.0010340525,0.0016897324,0.0005869089,0.00035078923,0.0010993021,0.0019087382,0.0029208127,0.0013181663],"category_scores_gemma":[0.020044813,0.000759714,0.0007807639,0.0008069455,0.0016459682,0.0027240016,0.0025124275,0.003448589,0.00064722926],"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":[0.00039280913,0.00015788211,0.0021872078,0.0003681627,0.00025261345,0.00024447383,0.00022330398,0.72171783,0.010996467,0.042952456,0.0059280293,0.21457875],"study_design_scores_gemma":[0.0000061220126,0.000019008632,0.00026916916,0.000010122721,0.000011476279,0.00003837795,0.000009229358,0.9857841,0.0008442574,0.012772891,0.00022684784,0.000008481548],"about_ca_topic_score_codex":0.0034974092,"about_ca_topic_score_gemma":0.00304292,"teacher_disagreement_score":0.0049131117,"about_ca_system_score_codex":0.00054168445,"about_ca_system_score_gemma":0.0010805905,"threshold_uncertainty_score":0.025983274},"labels":[],"label_agreement":null},{"id":"W2476987424","doi":"10.1142/9789813146976_0030","title":"FUZZY TRANSFER LEARNING IN DATA-SHORTAGE AND RAPIDLY CHANGING ENVIRONMENTS","year":2016,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"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 Alberta","funders":"","keywords":"Economic shortage; Computer science; Transfer of learning; Fuzzy logic; Transfer (computing); Artificial intelligence; Operating system","score_opus":0.02779511035448161,"score_gpt":0.24204117892888918,"score_spread":0.2142460685744076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2476987424","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":[0.11714249,0.00063178956,0.88022643,0.0003853763,0.000078659694,0.000039561553,0.00008465602,0.0003413167,0.0010697866],"genre_scores_gemma":[0.93649185,0.00030201333,0.0601012,0.00014077456,0.00008521488,0.000057274818,0.00021262628,0.000056896184,0.0025521398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952626,0.00013704912,0.000027972246,0.00015411449,0.00009079982,0.000063781794],"domain_scores_gemma":[0.9968569,0.0023507294,0.00014312715,0.00025710292,0.00028385108,0.00010827074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018999152,0.00045820762,0.0012649327,0.0006560257,0.00058188586,0.0008217151,0.0016083913,0.001414067,0.001415273],"category_scores_gemma":[0.008859246,0.00044885546,0.0004775274,0.0007474732,0.0010064931,0.0026171047,0.0016176638,0.0014218416,0.00025337416],"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":[0.00033038692,0.0002273195,0.002230425,0.00020232449,0.00010673838,0.0003359875,0.00023226213,0.8275634,0.005944717,0.011117967,0.0019059603,0.14980257],"study_design_scores_gemma":[0.00000529357,0.000024959705,0.00033992884,0.0000037104244,0.0000064097667,0.000028270028,0.00002425262,0.99060094,0.0008114006,0.0079726465,0.00017631029,0.000005802948],"about_ca_topic_score_codex":0.005245142,"about_ca_topic_score_gemma":0.003382817,"teacher_disagreement_score":0.005245142,"about_ca_system_score_codex":0.0007666105,"about_ca_system_score_gemma":0.0006137564,"threshold_uncertainty_score":0.010429263},"labels":[],"label_agreement":null},{"id":"W2533285514","doi":"10.1109/tic-sth.2009.5444486","title":"Addressing privacy constraints for efficient monitoring of network traffic for illicit images","year":2009,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Brandeis University","keywords":"Computer science; Estimator; Euclidean distance; The Internet; Classifier (UML); Artificial intelligence; Bandwidth (computing); Data mining; Machine learning; Computer network; Mathematics; Statistics","score_opus":0.06840890959558013,"score_gpt":0.32600201312452376,"score_spread":0.2575931035289436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2533285514","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":[0.0717364,0.00017122,0.9258526,0.00050720846,0.000024452691,0.000052713098,0.000058707086,0.00052485586,0.0010718558],"genre_scores_gemma":[0.87809867,0.00014955118,0.120537564,0.00011842448,0.00005225843,0.00007438309,0.00010458551,0.00004402784,0.00082062715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982609,0.0007924614,0.00009008579,0.00025979633,0.00046279817,0.00013400616],"domain_scores_gemma":[0.9892924,0.0063224914,0.0011919606,0.0016152994,0.0013522544,0.00022567016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003128671,0.00031840577,0.0007535377,0.0005758099,0.00072137476,0.0015037354,0.0012408481,0.00094695383,0.0008712192],"category_scores_gemma":[0.019075893,0.00038440418,0.0002384496,0.00046359416,0.0008706515,0.0043513924,0.0012773691,0.0014386915,0.00035024656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007090067,0.00040812246,0.016636627,0.0003301312,0.00009977616,0.00044743362,0.0006863119,0.286719,0.07750398,0.080313325,0.005541669,0.53060466],"study_design_scores_gemma":[0.000010675865,0.00005178954,0.0010900329,0.000009696663,0.0000073007345,0.0001468092,0.00008222261,0.9690485,0.01503559,0.0132291615,0.0012740409,0.000014210417],"about_ca_topic_score_codex":0.0009158114,"about_ca_topic_score_gemma":0.00097509404,"teacher_disagreement_score":0.003128671,"about_ca_system_score_codex":0.0008964037,"about_ca_system_score_gemma":0.0010583597,"threshold_uncertainty_score":0.01654625},"labels":[],"label_agreement":null},{"id":"W2557279148","doi":"10.1109/tfuzz.2016.2633376","title":"Fuzzy Regression Transfer Learning in Takagi–Sugeno Fuzzy Models","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":139,"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":"Australian Research Council","keywords":"Transfer of learning; Computer science; Artificial intelligence; Machine learning; Fuzzy logic; Regression; Regression analysis; Domain (mathematical analysis); Domain knowledge; Data mining; Mathematics; Statistics","score_opus":0.03235723835129048,"score_gpt":0.2486965241291165,"score_spread":0.21633928577782602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557279148","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":[0.03817338,0.0006349955,0.9583755,0.00019908058,0.00004545131,0.000045272947,0.000024311172,0.0002060819,0.0022959192],"genre_scores_gemma":[0.92033935,0.0005355627,0.07543464,0.00007807733,0.000048724098,0.0001410703,0.000060865987,0.000029370958,0.0033322957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994886,0.00017093403,0.000028463228,0.00013580394,0.00013647595,0.000039702718],"domain_scores_gemma":[0.99915206,0.0005491252,0.00009134694,0.000050709743,0.00013246083,0.000024270546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015207814,0.0007228212,0.0011072699,0.0005302787,0.0004851801,0.00096264394,0.001055746,0.0012334671,0.0012086533],"category_scores_gemma":[0.003981784,0.00037211107,0.00088316365,0.00059074844,0.0008182785,0.0012203081,0.0007067349,0.001278847,0.0002964677],"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":[0.000043300348,0.00005720314,0.0004634535,0.00008228669,0.00004775733,0.00010969305,0.00012309573,0.9471938,0.0017683684,0.013618129,0.00030706872,0.036185794],"study_design_scores_gemma":[0.0000022589008,0.000011448879,0.000053650227,0.00000306707,0.0000040329182,0.000007730971,0.00000413654,0.99684095,0.00022630562,0.0027440141,0.00009820668,0.0000041310927],"about_ca_topic_score_codex":0.0048547676,"about_ca_topic_score_gemma":0.0023478991,"teacher_disagreement_score":0.0048547676,"about_ca_system_score_codex":0.00073090417,"about_ca_system_score_gemma":0.00064634404,"threshold_uncertainty_score":0.009653032},"labels":[],"label_agreement":null},{"id":"W2560977758","doi":"","title":"Learning Deep Parsimonious Representations","year":2016,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"","keywords":"Interpretability; Cluster analysis; Conceptual clustering; Computer science; Artificial intelligence; Categorization; Machine learning; Generalization; Deep learning; Regularization (linguistics); Correlation clustering; Canopy clustering algorithm; Mathematics","score_opus":0.0174282915545141,"score_gpt":0.25559353851138034,"score_spread":0.23816524695686625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560977758","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":[0.04978753,0.00030440438,0.9469465,0.00049482996,0.000038896935,0.00003335889,0.00016316577,0.00057294086,0.0016584008],"genre_scores_gemma":[0.7629579,0.0004941889,0.22861657,0.0006550744,0.00009633833,0.00013516033,0.0007656937,0.00023515055,0.006043991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994311,0.00016811112,0.000021518243,0.00019930492,0.00010660356,0.00007324876],"domain_scores_gemma":[0.99880743,0.00056493917,0.00012374471,0.00034163424,0.000088084256,0.00007420834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009781884,0.0011725437,0.00096538325,0.00070117705,0.00043311942,0.0010934741,0.0017703301,0.0016853734,0.002168514],"category_scores_gemma":[0.004189286,0.0005603942,0.00072503334,0.0008227144,0.0011672637,0.0035668921,0.002200895,0.0035692928,0.00067972403],"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":[0.00022004501,0.00023336442,0.0017270023,0.00028344925,0.00016658695,0.00028554746,0.00036679325,0.56489503,0.023846904,0.12805958,0.0060598976,0.2738557],"study_design_scores_gemma":[0.000008910661,0.000046443784,0.0001304562,0.000015201364,0.000012699591,0.00004069737,0.000023272296,0.91804934,0.0016642838,0.07910993,0.00089099724,0.000007818681],"about_ca_topic_score_codex":0.0011578458,"about_ca_topic_score_gemma":0.0029515391,"teacher_disagreement_score":0.002168514,"about_ca_system_score_codex":0.00088935555,"about_ca_system_score_gemma":0.00067979726,"threshold_uncertainty_score":0.007254362},"labels":[],"label_agreement":null},{"id":"W2575899338","doi":"10.1609/aaai.v31i1.10911","title":"Policy Search with High-Dimensional Context Variables","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Japan Society for the Promotion of Science; Deutsche Forschungsgemeinschaft","keywords":"Dimensionality reduction; Principal component analysis; Computer science; Machine learning; Artificial intelligence; Curse of dimensionality; Context (archaeology); Entropy (arrow of time); Pattern recognition (psychology)","score_opus":0.08807321710670347,"score_gpt":0.31399613779126334,"score_spread":0.22592292068455988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2575899338","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03664319,0.00071425847,0.9596988,0.00031687377,0.000056988592,0.00007295538,0.00004897493,0.0008058692,0.0016420715],"genre_scores_gemma":[0.80941975,0.0003090674,0.18722461,0.0003960934,0.00008655421,0.00024649242,0.00022010173,0.00019045077,0.001906986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893755,0.0004528317,0.00005746407,0.00029021106,0.00015089748,0.000111000496],"domain_scores_gemma":[0.9980938,0.0012633502,0.00015305977,0.00021423284,0.00017567276,0.00010005543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016791046,0.0011054166,0.0021465793,0.0006936963,0.000625027,0.00087297126,0.0013444397,0.0015941627,0.0021007783],"category_scores_gemma":[0.007160951,0.0006983911,0.00083415466,0.0006700008,0.0011894696,0.0018684373,0.0016671043,0.0016918491,0.00042463033],"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":[0.00015597494,0.00014118373,0.0008240053,0.00011126337,0.00008224053,0.000097894204,0.00010009364,0.90717566,0.0016747087,0.019421024,0.0017241257,0.06849174],"study_design_scores_gemma":[0.0000141529335,0.00002085384,0.00005609281,0.000005189793,0.0000065855993,0.000011125071,0.0000062057143,0.99364954,0.00022487743,0.0058100787,0.00019091199,0.000004417468],"about_ca_topic_score_codex":0.0047144364,"about_ca_topic_score_gemma":0.005380782,"teacher_disagreement_score":0.0047144364,"about_ca_system_score_codex":0.0010780406,"about_ca_system_score_gemma":0.001971189,"threshold_uncertainty_score":0.009373963},"labels":[],"label_agreement":null},{"id":"W2580744997","doi":"10.1016/j.neuroimage.2017.01.066","title":"Predictive modelling using neuroimaging data in the presence of confounds","year":2017,"lang":"en","type":"article","venue":"NeuroImage","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":128,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Janssen Research and Development; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Eisai; Servier; U.S. Department of Defense; Eli Lilly and Company; Lundbeckfonden; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; IXICO; Takeda Pharmaceutical Company; AbbVie; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb Foundation; Wellcome Trust; Roche; Merck; Alzheimer's Drug Discovery Foundation; Wellcome; Fujirebio Europe; Alzheimer's Association; Foundation for the National Institutes of Health; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Johnson and Johnson; Meso Scale Diagnostics","keywords":"Neuroimaging; Weighting; Population; Confounding; Context (archaeology); Sample (material); Computer science; Artificial intelligence; Machine learning; Psychology; Statistics; Mathematics; Geography; Medicine","score_opus":0.1630712135925384,"score_gpt":0.3450921845634498,"score_spread":0.1820209709709114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580744997","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18095936,0.0015355953,0.81183743,0.0024628371,0.00014886275,0.00020508176,0.00075969135,0.0009087409,0.001182442],"genre_scores_gemma":[0.8389601,0.0006563332,0.15568058,0.00081478735,0.00026697764,0.00032522657,0.0017622877,0.00013085094,0.0014028467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950075,0.0031698092,0.00023200852,0.00086867437,0.00049512024,0.00022682887],"domain_scores_gemma":[0.946386,0.04672817,0.0019567246,0.0028777411,0.0015751917,0.00047629833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020958781,0.0014785187,0.0023419186,0.000912655,0.00063181255,0.002611701,0.0029958766,0.0029723027,0.001479337],"category_scores_gemma":[0.060088683,0.0008722147,0.001847302,0.0012711581,0.001956828,0.0029962214,0.002442312,0.005460699,0.00030365962],"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":[0.0006427759,0.00013001426,0.015004591,0.000203486,0.00026129466,0.0002942183,0.0003419723,0.9099487,0.0013096482,0.014130111,0.0014104975,0.05632273],"study_design_scores_gemma":[0.000027693515,0.00004692845,0.0011510197,0.000030058734,0.00003953215,0.000037081565,0.000022677192,0.9822645,0.0005988826,0.0153426565,0.00042470155,0.000014250425],"about_ca_topic_score_codex":0.0069573782,"about_ca_topic_score_gemma":0.0048072333,"teacher_disagreement_score":0.020958781,"about_ca_system_score_codex":0.0014477873,"about_ca_system_score_gemma":0.0013939663,"threshold_uncertainty_score":0.11084193},"labels":[],"label_agreement":null},{"id":"W2604587608","doi":"10.1609/aaai.v31i1.10848","title":"Fast Generalized Distillation for Semi-Supervised Domain Adaptation","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Domain adaptation; Computer science; Distillation; Labeled data; Support vector machine; Classifier (UML); Transfer of learning; Artificial intelligence; Machine learning; Adaptation (eye); Domain (mathematical analysis); Data source; Data mining; Pattern recognition (psychology); Mathematics","score_opus":0.12008076116521839,"score_gpt":0.3188042640720414,"score_spread":0.198723502906823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604587608","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":[0.013961738,0.00031883648,0.9832154,0.00015346543,0.000054501044,0.00006148861,0.000099164514,0.0015771723,0.00055829354],"genre_scores_gemma":[0.5235698,0.00032149247,0.47063458,0.00050735835,0.00014276376,0.00035836562,0.001304182,0.00031146302,0.0028499933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975097,0.0010200314,0.00012083594,0.00075502024,0.00044036368,0.00015412041],"domain_scores_gemma":[0.9964651,0.0017206094,0.00022745662,0.00089700404,0.00052448,0.00016533135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027136572,0.0012604726,0.0017657351,0.00086414494,0.00085996435,0.00092725205,0.0027192235,0.0015326958,0.0020932567],"category_scores_gemma":[0.006677892,0.00065537426,0.0010967342,0.0011903538,0.0015575953,0.0027087927,0.003498862,0.0035997066,0.0009795335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004631872,0.00038341896,0.0013549015,0.0002979703,0.00020053834,0.00018270494,0.00032627943,0.34639302,0.019599717,0.01712892,0.00907892,0.6045905],"study_design_scores_gemma":[0.000010116037,0.000027791672,0.000105023304,0.000004140882,0.0000051091047,0.000027537815,0.000012441158,0.98867214,0.0017576742,0.0088074375,0.0005609689,0.000009625084],"about_ca_topic_score_codex":0.002755516,"about_ca_topic_score_gemma":0.0037846067,"teacher_disagreement_score":0.002755516,"about_ca_system_score_codex":0.00075472635,"about_ca_system_score_gemma":0.001447861,"threshold_uncertainty_score":0.014351308},"labels":[],"label_agreement":null},{"id":"W2604992821","doi":"10.1609/aaai.v31i1.10898","title":"Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":28,"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 Waterloo","funders":"","keywords":"Covariate; Computer science; Inference; Domain (mathematical analysis); Domain adaptation; Adaptation (eye); Generative model; Machine learning; Artificial intelligence; Generative grammar; Algorithm; Data mining; Mathematics","score_opus":0.12357503461243029,"score_gpt":0.3013951643751564,"score_spread":0.1778201297627261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604992821","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0117748575,0.00016090705,0.98635477,0.00020833647,0.000031471685,0.00006803254,0.000104293424,0.0005422245,0.0007551324],"genre_scores_gemma":[0.5156268,0.00040939683,0.47206098,0.0008275305,0.00021783476,0.00054418045,0.0015946522,0.0003434662,0.008375264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982103,0.0007359368,0.0000667471,0.0006631901,0.00020616397,0.000117643474],"domain_scores_gemma":[0.99568236,0.0019014748,0.00024190244,0.0016098693,0.0004178599,0.00014661941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037722583,0.0008668928,0.0013191879,0.0006455643,0.00063889206,0.0010488743,0.0027524435,0.0019336011,0.002276791],"category_scores_gemma":[0.009682499,0.00064721965,0.0016272089,0.00084200146,0.0014592974,0.0027816272,0.003127837,0.0036187307,0.0014481994],"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":[0.00041178783,0.00043948946,0.0057678064,0.00022841046,0.00026284202,0.00030960215,0.0004881293,0.56617576,0.022279201,0.06862222,0.010803864,0.32421094],"study_design_scores_gemma":[0.00002542124,0.000054922708,0.0007287306,0.000010322791,0.00001837783,0.000121243036,0.000023467255,0.9641352,0.0031119976,0.029754637,0.0019953856,0.000020244706],"about_ca_topic_score_codex":0.0030372501,"about_ca_topic_score_gemma":0.004258647,"teacher_disagreement_score":0.0037722583,"about_ca_system_score_codex":0.00078448467,"about_ca_system_score_gemma":0.0013693267,"threshold_uncertainty_score":0.019949853},"labels":[],"label_agreement":null},{"id":"W2606931506","doi":"10.1007/978-3-319-57529-2_6","title":"Effective Multiclass Transfer for Hypothesis Transfer Learning","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"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":"Computer science; Transfer of learning; Exploit; Artificial intelligence; Classifier (UML); Machine learning; Training set; Domain adaptation; Transfer function; Knowledge transfer","score_opus":0.02616235754246228,"score_gpt":0.25487084506430857,"score_spread":0.2287084875218463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606931506","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034884887,0.0009261898,0.99317884,0.00015143472,0.00011638597,0.000032455828,0.000050825773,0.0007574062,0.0012980535],"genre_scores_gemma":[0.34876528,0.00171246,0.6151396,0.00063978264,0.0006459108,0.00049768575,0.0011365272,0.0007677381,0.030694943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885786,0.0004571039,0.000052825482,0.00027756262,0.00025455933,0.00010006921],"domain_scores_gemma":[0.9980683,0.0011152511,0.00006636454,0.00046130485,0.00022254273,0.00006627208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025081818,0.0010318953,0.0013992857,0.0008478978,0.00055889343,0.0009950029,0.0025461246,0.0022173612,0.0077010677],"category_scores_gemma":[0.0052553075,0.00052959763,0.0009816511,0.0010204192,0.0009935583,0.0028446782,0.0033686499,0.0032034898,0.0032112414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017724832,0.00019233518,0.00027850765,0.00029895187,0.00016254924,0.00009676738,0.00010138832,0.10690747,0.014121365,0.0422861,0.012580218,0.82279706],"study_design_scores_gemma":[0.000008537469,0.000059171252,0.00024328816,0.000019473959,0.000024484541,0.00006418815,0.000015517275,0.9417555,0.0058998265,0.049211252,0.0026835725,0.000015221948],"about_ca_topic_score_codex":0.0010180378,"about_ca_topic_score_gemma":0.0011085062,"teacher_disagreement_score":0.0077010677,"about_ca_system_score_codex":0.0007574413,"about_ca_system_score_gemma":0.00066546636,"threshold_uncertainty_score":0.025762618},"labels":[],"label_agreement":null},{"id":"W2608258611","doi":"10.48550/arxiv.1704.02998","title":"Weakly-Supervised Spatial Context Networks","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; ENCODE; Feature learning; Encoder; Spatial contextual awareness; Pattern recognition (psychology); Offset (computer science); Context (archaeology); Initialization; Representation (politics); Categorization; Machine learning","score_opus":0.0861122389193354,"score_gpt":0.19375044779115166,"score_spread":0.10763820887181627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608258611","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":[0.16026536,0.0010461819,0.82951874,0.0005492766,0.000105947394,0.00007360665,0.0005481886,0.0026217208,0.005270893],"genre_scores_gemma":[0.9217662,0.00021369128,0.07314961,0.00025703062,0.00006714003,0.000060732415,0.00059933745,0.00009992173,0.0037863436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969435,0.00007635978,0.000010150047,0.00014155339,0.000041115498,0.00003654388],"domain_scores_gemma":[0.99940765,0.00020143467,0.000075210795,0.00015457705,0.00011336862,0.00004772703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040364295,0.00084823306,0.0006926358,0.00041983818,0.000295976,0.000508454,0.0016248687,0.00090086414,0.0020912306],"category_scores_gemma":[0.0022316978,0.00035146056,0.0004974965,0.0003972673,0.0006785742,0.0015290931,0.0013529834,0.001219065,0.00046588914],"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":[0.00051498046,0.00023539297,0.005233173,0.00022236528,0.00013737821,0.00021817366,0.00018527702,0.61312574,0.026396638,0.023533301,0.0064717084,0.3237259],"study_design_scores_gemma":[0.000007791758,0.000049505117,0.00041888765,0.0000071878794,0.000009635853,0.000026776905,0.000008006692,0.9873667,0.0027837257,0.008761575,0.00055523403,0.0000048968955],"about_ca_topic_score_codex":0.0038834303,"about_ca_topic_score_gemma":0.008240636,"teacher_disagreement_score":0.0038834303,"about_ca_system_score_codex":0.0006646154,"about_ca_system_score_gemma":0.00057778356,"threshold_uncertainty_score":0.0077216625},"labels":[],"label_agreement":null},{"id":"W2613498939","doi":"10.1109/tpami.2018.2884462","title":"Incremental Learning Through Deep Adaptation","year":2018,"lang":"en","type":"preprint","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Air Force Office of Scientific Research; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Adaptation (eye); Quantization (signal processing); Task (project management); Network architecture; Artificial neural network; Machine learning; Domain adaptation; Range (aeronautics); Domain (mathematical analysis); Network performance; Deep neural networks; Algorithm; Classifier (UML); Mathematics; Engineering","score_opus":0.03901779770268217,"score_gpt":0.29170250476836557,"score_spread":0.2526847070656834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613498939","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":[0.025540404,0.00045397322,0.9680723,0.00016362092,0.00008378772,0.0000740821,0.00007985418,0.0024568746,0.003075066],"genre_scores_gemma":[0.6501115,0.00050277234,0.34128,0.00049246376,0.00012396659,0.0002978876,0.00053187815,0.00033479946,0.006324669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999629,0.000069501075,0.000018253651,0.00013080746,0.00010254611,0.000049761304],"domain_scores_gemma":[0.9991221,0.00035870244,0.00006051895,0.00024289651,0.00016233348,0.000053455682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008441987,0.00095228665,0.00091680797,0.00063231133,0.00031069905,0.0007003923,0.002150638,0.0008301866,0.0025275515],"category_scores_gemma":[0.0037866195,0.00052198593,0.00060662325,0.00059246965,0.0007293431,0.0019593579,0.0017806896,0.0017585008,0.00087687775],"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":[0.00016888905,0.0002175443,0.0018860189,0.00016781688,0.0001353042,0.00012713975,0.00013669,0.48322856,0.016770052,0.015568074,0.0074195196,0.47417444],"study_design_scores_gemma":[0.000010669715,0.000029480687,0.00017217455,0.000008586122,0.000012508613,0.00003001516,0.000009697984,0.98855513,0.0023849714,0.007750175,0.0010289556,0.00000750457],"about_ca_topic_score_codex":0.002460005,"about_ca_topic_score_gemma":0.004193884,"teacher_disagreement_score":0.0025275515,"about_ca_system_score_codex":0.00061559124,"about_ca_system_score_gemma":0.00073609495,"threshold_uncertainty_score":0.0084555745},"labels":[],"label_agreement":null},{"id":"W2619993245","doi":"10.1371/journal.pone.0187736","title":"Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Wyoming; National Aeronautics and Space Administration; Canadian Centre for Applied Research in Cancer Control; Wyoming Space Grant Consortium; National Science Foundation","keywords":"Forgetting; Modularity (biology); Neuromodulation; Computer science; Task (project management); Artificial intelligence; Artificial neural network; Modular design; Machine learning; Neuroscience; Cognitive psychology; Psychology; Engineering; Biology","score_opus":0.04847875368260687,"score_gpt":0.23978555067268287,"score_spread":0.191306796990076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619993245","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":[0.48674798,0.0011754943,0.50481534,0.0005235871,0.00017627077,0.00006128576,0.00009418004,0.0013850708,0.005020762],"genre_scores_gemma":[0.9731434,0.00027969797,0.025612257,0.00009065123,0.000010628227,0.00003052221,0.000034722787,0.000041479176,0.00075660966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998665,0.000017329925,0.000013400166,0.000041318694,0.000034838456,0.000026712056],"domain_scores_gemma":[0.9991959,0.00037319967,0.00016911827,0.000119861856,0.00008354032,0.00005849015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039143735,0.0004894644,0.00046901114,0.00025699026,0.00023325546,0.00045514494,0.00078259036,0.00056663936,0.0008854294],"category_scores_gemma":[0.002888246,0.00023051859,0.0006190207,0.00014998406,0.00064718514,0.0011448141,0.00062669395,0.0008272199,0.0001258957],"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":[0.00032486138,0.0001885945,0.002251854,0.00049450394,0.00016273357,0.0005100299,0.00024878027,0.5577157,0.3094821,0.031598866,0.0010514895,0.0959704],"study_design_scores_gemma":[0.00004074441,0.00027114528,0.0011620165,0.000021094704,0.000054563967,0.00016509292,0.000024107041,0.9166515,0.053497754,0.026506865,0.0015771625,0.00002797466],"about_ca_topic_score_codex":0.0010715832,"about_ca_topic_score_gemma":0.0011510421,"teacher_disagreement_score":0.0010715832,"about_ca_system_score_codex":0.00045824234,"about_ca_system_score_gemma":0.00033478026,"threshold_uncertainty_score":0.0033248067},"labels":[],"label_agreement":null},{"id":"W2645827484","doi":"10.1109/ccece.2017.7946733","title":"Fusion of transfer learning features and its application in image classification","year":2017,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":32,"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 Windsor","funders":"","keywords":"Artificial intelligence; Transfer of learning; Computer science; Pattern recognition (psychology); Autoencoder; Convolutional neural network; Support vector machine; Contextual image classification; Fuse (electrical); Feature extraction; Machine learning; Feature learning; Deep learning; Feature vector; Feature (linguistics); Image (mathematics)","score_opus":0.02558771438721698,"score_gpt":0.280573750903116,"score_spread":0.254986036515899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2645827484","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":[0.04076711,0.0008133417,0.95587313,0.00013981704,0.00006831629,0.00004508314,0.00007860524,0.0009542453,0.001260237],"genre_scores_gemma":[0.8436333,0.0006290475,0.15330124,0.0001088368,0.000095137955,0.00007576311,0.00036255035,0.00008506862,0.0017090895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993036,0.00011528224,0.00005064532,0.0002037568,0.00025555317,0.00007117276],"domain_scores_gemma":[0.9992086,0.00024254531,0.00009206948,0.000164015,0.0002544368,0.000038365488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011218196,0.0006226603,0.0009285538,0.001582806,0.00028358417,0.0006492436,0.00073164486,0.0008198836,0.0010186693],"category_scores_gemma":[0.0025172806,0.0002410484,0.0009513248,0.0017487328,0.0007020266,0.0016507194,0.0011615964,0.00096321513,0.00050342793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022525276,0.00017142916,0.0025478227,0.00012765145,0.00015464176,0.00015641765,0.000108193286,0.13131818,0.03527699,0.006850186,0.0018114318,0.82125175],"study_design_scores_gemma":[0.000007656903,0.00012358831,0.0020455422,0.000013662436,0.000047752572,0.00013304043,0.000028725071,0.97058433,0.01766645,0.007419632,0.0019046268,0.000024943498],"about_ca_topic_score_codex":0.0016219257,"about_ca_topic_score_gemma":0.0009002464,"teacher_disagreement_score":0.0016219257,"about_ca_system_score_codex":0.00053552166,"about_ca_system_score_gemma":0.00042836665,"threshold_uncertainty_score":0.005932808},"labels":[],"label_agreement":null},{"id":"W2735375434","doi":"10.1109/ijcnn.2017.7965853","title":"A weighted-resampling based transfer learning algorithm","year":2017,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Resampling; Computer science; Boosting (machine learning); Decision tree; Transfer of learning; Machine learning; Artificial intelligence; Naive Bayes classifier; Classifier (UML); Algorithm; Data mining; Support vector machine","score_opus":0.02851291744107313,"score_gpt":0.27172866384755406,"score_spread":0.24321574640648094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735375434","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":[0.0066053243,0.00037568726,0.99076617,0.000120595236,0.00008878704,0.0000984895,0.00004413393,0.0010122354,0.00088861],"genre_scores_gemma":[0.26172882,0.0004080207,0.73037964,0.00042990205,0.00026152155,0.0004930753,0.00067326764,0.0002607863,0.0053649736],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979824,0.00059649354,0.00013285957,0.00046052388,0.00065036654,0.0001773397],"domain_scores_gemma":[0.99845135,0.0005908267,0.00010904344,0.0002259133,0.000552702,0.00007006595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003587693,0.001165556,0.002672748,0.0019188117,0.0009780648,0.0010297848,0.0035130992,0.0019881309,0.0034844705],"category_scores_gemma":[0.006086885,0.00053681,0.0015092123,0.0016281356,0.00094014936,0.002414971,0.0018664757,0.0020686993,0.0016832807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025098101,0.0002682288,0.0011686415,0.00015013921,0.00018620092,0.00014164716,0.000110560104,0.30550852,0.0054196767,0.012360116,0.0067124804,0.6677228],"study_design_scores_gemma":[0.00002292066,0.0000586981,0.00015135054,0.0000071786744,0.000015533511,0.000058461952,0.000010264547,0.99133635,0.0013936525,0.005746783,0.0011874323,0.0000113268725],"about_ca_topic_score_codex":0.0037388236,"about_ca_topic_score_gemma":0.0023515217,"teacher_disagreement_score":0.0037388236,"about_ca_system_score_codex":0.0010308169,"about_ca_system_score_gemma":0.0016260118,"threshold_uncertainty_score":0.018973768},"labels":[],"label_agreement":null},{"id":"W2736761179","doi":"10.1016/j.patcog.2017.07.019","title":"On automated source selection for transfer learning in convolutional neural networks","year":2017,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institutes of Health; National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute; EMS Ingénierie","keywords":"Transfer of learning; Computer science; Convolutional neural network; Artificial intelligence; Selection (genetic algorithm); Machine learning; Pattern recognition (psychology)","score_opus":0.03693271865177594,"score_gpt":0.27347629166030857,"score_spread":0.23654357300853263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736761179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013027176,0.0005910969,0.9837853,0.00026510368,0.000053866068,0.000059893522,0.00007841092,0.0013114852,0.0008277288],"genre_scores_gemma":[0.50863737,0.0008523317,0.47706452,0.0005605072,0.0002869557,0.00040274276,0.0012536782,0.0008901448,0.0100517245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985083,0.0006370876,0.00009601587,0.0003246147,0.0002781864,0.00015573089],"domain_scores_gemma":[0.9930902,0.00494733,0.00018036432,0.0010110524,0.00058545696,0.00018558337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032356696,0.001466869,0.0022225396,0.0012084208,0.0010105757,0.0013556934,0.0036627112,0.002579688,0.004830047],"category_scores_gemma":[0.011638225,0.0008981841,0.001180435,0.0012227289,0.0016240329,0.0041700127,0.004908076,0.003477348,0.0012178215],"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":[0.0005429452,0.00037827977,0.0009885877,0.0002038554,0.00021075805,0.00015842463,0.00016143714,0.48447186,0.00669523,0.030993346,0.0073489775,0.46784627],"study_design_scores_gemma":[0.000017665265,0.000028063183,0.00012444313,0.0000065592712,0.000010444088,0.000015313664,0.000011791194,0.97849315,0.001111345,0.019878633,0.00029485943,0.0000078347875],"about_ca_topic_score_codex":0.0073405365,"about_ca_topic_score_gemma":0.00859212,"teacher_disagreement_score":0.0073405365,"about_ca_system_score_codex":0.001267333,"about_ca_system_score_gemma":0.0017418143,"threshold_uncertainty_score":0.017112076},"labels":[],"label_agreement":null},{"id":"W2742004750","doi":"10.24963/ijcai.2017/313","title":"Incomplete Attribute Learning with auxiliary labels","year":2017,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"China Scholarship Council; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Machine learning; Image (mathematics); Quadratic programming; Space (punctuation); Data mining; Pattern recognition (psychology); Mathematics; Mathematical optimization","score_opus":0.025613029406763882,"score_gpt":0.2572963196033041,"score_spread":0.23168329019654024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742004750","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":[0.027494922,0.00042220217,0.96961784,0.00029611247,0.000049958453,0.000049063237,0.00032590333,0.00099097,0.00075298845],"genre_scores_gemma":[0.62664586,0.00064830505,0.36418793,0.00052992953,0.00024960525,0.00027184075,0.0037608715,0.0002653232,0.0034403617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977944,0.00071888475,0.000096892676,0.00084801385,0.00039501404,0.00014677804],"domain_scores_gemma":[0.9933815,0.0034705282,0.00046101742,0.0017384319,0.0007160351,0.00023242555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003011911,0.0014096784,0.0027765026,0.0011973116,0.00081985554,0.0019427079,0.0035176186,0.0022075241,0.001588046],"category_scores_gemma":[0.009482837,0.00069679756,0.0016365586,0.001697947,0.0018526,0.005260678,0.002617762,0.0045721233,0.00093537284],"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":[0.00065473275,0.0005184188,0.0052642077,0.00056686444,0.00021791657,0.00035846245,0.0007165473,0.49163866,0.0132898,0.03080436,0.009895042,0.446075],"study_design_scores_gemma":[0.000017186409,0.00005025295,0.0005267811,0.00001844638,0.00002280156,0.00006291103,0.000062440566,0.9612909,0.0033934738,0.03317846,0.0013524212,0.000023947228],"about_ca_topic_score_codex":0.0030500598,"about_ca_topic_score_gemma":0.0032542015,"teacher_disagreement_score":0.0035176186,"about_ca_system_score_codex":0.0011742254,"about_ca_system_score_gemma":0.0011980325,"threshold_uncertainty_score":0.015928686},"labels":[],"label_agreement":null},{"id":"W2746002367","doi":"10.1109/fuzz-ieee.2017.8015558","title":"Fuzzy rule-based transfer learning for label space adaptation","year":2017,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Australian Research Council","keywords":"Computer science; Fuzzy rule; Artificial intelligence; Fuzzy logic; Adaptation (eye); Space (punctuation); Transfer of learning; Fuzzy set; Machine learning; Physics","score_opus":0.05558863651664425,"score_gpt":0.28893274619310605,"score_spread":0.2333441096764618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2746002367","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":[0.015127386,0.00033468253,0.98232055,0.00012610946,0.00006875881,0.000073473806,0.000041369913,0.00069545495,0.0012122318],"genre_scores_gemma":[0.72473645,0.00035489432,0.26903445,0.00036560264,0.00014318229,0.00025814556,0.0003329669,0.0001272738,0.0046469173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988882,0.00029632534,0.0000647792,0.00035992492,0.00031027515,0.00008044077],"domain_scores_gemma":[0.99757785,0.0012965809,0.00016618629,0.0003417313,0.0005442259,0.00007358272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023297586,0.00078477553,0.0012587592,0.0010683902,0.000673592,0.0010129074,0.0023720902,0.0015647857,0.0024610092],"category_scores_gemma":[0.007512195,0.00034841307,0.0010549703,0.0010531513,0.0009355275,0.0017992732,0.0012597499,0.002009924,0.0009539438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015434076,0.00033501853,0.0012589619,0.0001271516,0.00011193631,0.00017528122,0.00027470718,0.41965395,0.0069619264,0.0087724775,0.0027312962,0.5594429],"study_design_scores_gemma":[0.0000051606676,0.000015746407,0.00009773629,0.000003916807,0.000006057832,0.000019084158,0.000009050778,0.9946525,0.0010601886,0.0038646404,0.00025874545,0.0000071305494],"about_ca_topic_score_codex":0.0035553256,"about_ca_topic_score_gemma":0.002163774,"teacher_disagreement_score":0.0035553256,"about_ca_system_score_codex":0.0008652647,"about_ca_system_score_gemma":0.0008803638,"threshold_uncertainty_score":0.012321055},"labels":[],"label_agreement":null},{"id":"W2748618181","doi":"10.1109/cvpr.2017.542","title":"Zero-Shot Classification with Discriminative Semantic Representation Learning","year":2017,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Disjoint sets; Pattern recognition (psychology); Representation (politics); Matrix decomposition; Feature learning; Machine learning; Exploit; Domain (mathematical analysis); Mathematics","score_opus":0.09870648436760156,"score_gpt":0.33565372259784004,"score_spread":0.2369472382302385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748618181","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":[0.031294346,0.00042217138,0.96529025,0.00013413641,0.00006421614,0.00012531264,0.00014378638,0.001478544,0.0010472515],"genre_scores_gemma":[0.671767,0.00036757922,0.32042354,0.00045740782,0.0001684649,0.000254871,0.0023873372,0.00021239612,0.0039613917],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983107,0.00040722813,0.00007084524,0.000641889,0.0003866236,0.0001827502],"domain_scores_gemma":[0.9979954,0.0007433181,0.00016238332,0.00060846994,0.00036516497,0.0001253685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015147568,0.0013016056,0.0021816564,0.0018534599,0.00077281654,0.0010706027,0.0036919848,0.0016260713,0.0016456523],"category_scores_gemma":[0.004710422,0.00045065762,0.0012200867,0.0015203115,0.0015509843,0.003313191,0.0026302726,0.0023669007,0.0008754519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042638666,0.00088670215,0.0039094766,0.0003600339,0.00020941913,0.0002739769,0.0003932226,0.101050206,0.02262146,0.014639087,0.009946161,0.84528387],"study_design_scores_gemma":[0.000025481584,0.00012675203,0.0006634431,0.000016777516,0.000026564032,0.00015661462,0.00008665039,0.9681941,0.0075387997,0.021821594,0.001317391,0.000025916723],"about_ca_topic_score_codex":0.0034149934,"about_ca_topic_score_gemma":0.00503021,"teacher_disagreement_score":0.0036919848,"about_ca_system_score_codex":0.0008650804,"about_ca_system_score_gemma":0.0010636501,"threshold_uncertainty_score":0.008010924},"labels":[],"label_agreement":null},{"id":"W2752713396","doi":"10.1167/17.10.504","title":"Ruling out task difficulty in the context-generalization of texture perceptual learning","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Perception; Context effect; Context (archaeology); Computer vision; Generalization; Computer science; Perceptual learning; Pattern recognition (psychology); Cognitive psychology; Psychology; Mathematics; Geometry; Neuroscience; Geography","score_opus":0.02771521976879825,"score_gpt":0.31118566868295106,"score_spread":0.2834704489141528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752713396","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9808328,0.00019431175,0.016506966,0.00014553723,0.00008868608,0.000225189,0.00017640968,0.00016839067,0.0016617681],"genre_scores_gemma":[0.9856678,0.00008002256,0.011893328,0.00022717546,0.000020067855,0.00037376364,0.00030966598,0.0002369566,0.0011910655],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972691,0.00030513722,0.00045625368,0.0010068645,0.0007173101,0.00024528283],"domain_scores_gemma":[0.9892203,0.0043919743,0.0018108224,0.0031392158,0.00069311494,0.0007445557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024114833,0.0015398236,0.00092002953,0.00039555106,0.00038857252,0.0011359574,0.0013995497,0.0014777284,0.003999997],"category_scores_gemma":[0.0118463645,0.00079856894,0.00074587576,0.00020403576,0.0011611799,0.002107366,0.0020163283,0.0030617276,0.0007605318],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010196738,0.0006623918,0.0076158238,0.00030165983,0.00007329125,0.000090898946,0.00022548698,0.0004965942,0.9818226,0.00029838804,0.0001245812,0.0072686085],"study_design_scores_gemma":[0.00026681728,0.0070834355,0.2428082,0.00008742993,0.00019794812,0.0006797264,0.00013963137,0.01579147,0.727103,0.0033005103,0.0024291198,0.00011278593],"about_ca_topic_score_codex":0.0007223943,"about_ca_topic_score_gemma":0.001012684,"teacher_disagreement_score":0.003999997,"about_ca_system_score_codex":0.0004023015,"about_ca_system_score_gemma":0.00047780375,"threshold_uncertainty_score":0.013381362},"labels":[],"label_agreement":null},{"id":"W2753160622","doi":"","title":"Optimization as a Model for Few-Shot Learning","year":2017,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2447,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Initialization; Artificial intelligence; Meta learning (computer science); Convergence (economics); Deep learning; Metric (unit); Artificial neural network; Machine learning; Set (abstract data type); Parametrization (atmospheric modeling); Competitive learning; Class (philosophy); Domain (mathematical analysis); Learning to learn; Mathematics; Task (project management)","score_opus":0.1464190826716082,"score_gpt":0.4031299499396052,"score_spread":0.256710867267997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753160622","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":[0.0071795764,0.00030052342,0.99086726,0.0003162681,0.000028729864,0.00002963173,0.000069990936,0.00029427293,0.00091370655],"genre_scores_gemma":[0.66534585,0.00084501976,0.31901863,0.000605992,0.0001861584,0.0005774629,0.00063366664,0.00039147335,0.012395817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992717,0.0002874014,0.00003836458,0.00021257596,0.00013686495,0.00005313938],"domain_scores_gemma":[0.9984187,0.00097216817,0.000149404,0.00020681419,0.00017686786,0.000076070064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019014421,0.0008942978,0.0013186077,0.0006107751,0.0003196266,0.0011719452,0.0025557165,0.0021536322,0.0029136075],"category_scores_gemma":[0.007025933,0.00077287364,0.00085845107,0.0007377417,0.0012665989,0.003090303,0.0016242686,0.0029814355,0.00084672385],"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":[0.0000462469,0.000049161776,0.00034274807,0.00008383829,0.00004078158,0.000056527308,0.00007226926,0.90911937,0.0012849842,0.058948237,0.0016932271,0.028262611],"study_design_scores_gemma":[0.0000025769104,0.0000108474605,0.00002769922,0.00000397814,0.0000023929729,0.000010113538,0.000002427483,0.9815996,0.0001928304,0.017884312,0.00025961435,0.0000036745844],"about_ca_topic_score_codex":0.0025382084,"about_ca_topic_score_gemma":0.0026737906,"teacher_disagreement_score":0.0029136075,"about_ca_system_score_codex":0.0011911483,"about_ca_system_score_gemma":0.0007870852,"threshold_uncertainty_score":0.010055959},"labels":[],"label_agreement":null},{"id":"W2769651498","doi":"10.1109/iccvw.2017.111","title":"Homography Estimation from Image Pairs with Hierarchical Convolutional Networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":87,"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 Ottawa","funders":"","keywords":"Computer science; Homography; Context (archaeology); Set (abstract data type); Artificial intelligence; Process (computing); Hierarchy; Pattern recognition (psychology); Image (mathematics); Algorithm; Mathematics; Statistics","score_opus":0.01142604149830686,"score_gpt":0.23638719717126128,"score_spread":0.2249611556729544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769651498","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":[0.018088518,0.00037388902,0.9783692,0.00005467382,0.000041886382,0.00004009509,0.00010137071,0.001929358,0.0010009396],"genre_scores_gemma":[0.4881683,0.00043407248,0.50311035,0.00019270595,0.00008984825,0.00008656878,0.0014955549,0.000335744,0.006086897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990933,0.00014848133,0.00003167168,0.00037870085,0.00022691539,0.00012099131],"domain_scores_gemma":[0.9993906,0.00010767533,0.00009439029,0.00024734254,0.00011558004,0.00004441663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008503992,0.0018066448,0.0017352791,0.0014591028,0.0004505352,0.00092866836,0.0024546469,0.0012714729,0.0024568827],"category_scores_gemma":[0.0022069141,0.0009149933,0.0012503118,0.0014219534,0.0006166547,0.0022808262,0.0023815641,0.0019627204,0.001612434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002621796,0.00021238782,0.0017856046,0.00014720476,0.00031205357,0.00020043169,0.00017402819,0.28629893,0.026445162,0.007854672,0.0044704303,0.67183685],"study_design_scores_gemma":[0.000006947689,0.000034720295,0.0004331478,0.00001023309,0.000019320038,0.00006720788,0.000019379311,0.989129,0.0047162985,0.004607335,0.0009453485,0.00001113752],"about_ca_topic_score_codex":0.0086185215,"about_ca_topic_score_gemma":0.012814462,"teacher_disagreement_score":0.0086185215,"about_ca_system_score_codex":0.00073748303,"about_ca_system_score_gemma":0.0009985989,"threshold_uncertainty_score":0.017136753},"labels":[],"label_agreement":null},{"id":"W2773809710","doi":"","title":"A Semantic Loss Function for Deep Learning Under Weak Supervision","year":2017,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of Toronto","funders":"","keywords":"Computer science; Function (biology); Artificial intelligence; Deep learning; Natural language processing","score_opus":0.029817009378330657,"score_gpt":0.27512122399606287,"score_spread":0.24530421461773222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773809710","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060131624,0.0003454104,0.9920896,0.0002887796,0.00007644772,0.000028381068,0.000113199945,0.0003494052,0.0006955283],"genre_scores_gemma":[0.41312554,0.0012418549,0.56742275,0.0008020649,0.00043826664,0.00040837875,0.0019975887,0.00055578817,0.01400772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907494,0.00030247023,0.00006484509,0.00019654054,0.0002768427,0.000084402636],"domain_scores_gemma":[0.9986834,0.00054701674,0.00008544227,0.00026812768,0.0003294792,0.0000864402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027715955,0.0011258459,0.0015350999,0.00077984115,0.00059126806,0.0011979772,0.0021391618,0.0024833193,0.002257569],"category_scores_gemma":[0.005829403,0.0004884115,0.0009479274,0.00094769034,0.0012942812,0.0035045675,0.0029737374,0.0037479748,0.0009269366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038576624,0.00045535303,0.00088248425,0.0003850181,0.0001782039,0.00014682858,0.00011200156,0.3953939,0.010714839,0.16399652,0.018768702,0.4085804],"study_design_scores_gemma":[0.0000094978495,0.00004385776,0.00013600806,0.000015710404,0.000014246027,0.00003687415,0.000007464445,0.9468879,0.001342895,0.05061601,0.00087879924,0.000010736516],"about_ca_topic_score_codex":0.0020003514,"about_ca_topic_score_gemma":0.0024746151,"teacher_disagreement_score":0.0027715955,"about_ca_system_score_codex":0.0011124266,"about_ca_system_score_gemma":0.001665013,"threshold_uncertainty_score":0.014657795},"labels":[],"label_agreement":null},{"id":"W2776179661","doi":"10.1007/978-3-319-71249-9_27","title":"Crossprop: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"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 Alberta","funders":"","keywords":"Computer science; Stochastic gradient descent; Gradient descent; Representation (politics); Artificial intelligence; Meta learning (computer science); Artificial neural network; Domain (mathematical analysis); Feature (linguistics); Scaling; Algorithm; Machine learning; Mathematics","score_opus":0.03854797923379474,"score_gpt":0.284209923196719,"score_spread":0.24566194396292426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2776179661","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004017056,0.0013066931,0.99105084,0.00019313201,0.00013636553,0.00002614473,0.00011766907,0.0018173647,0.001334824],"genre_scores_gemma":[0.1540196,0.0015559075,0.8266501,0.00044623052,0.00025463337,0.0002965439,0.0013563227,0.0016501544,0.013770412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995433,0.00014729355,0.000026488338,0.00013327882,0.000107070075,0.00004262712],"domain_scores_gemma":[0.99924797,0.00040080608,0.00004258402,0.00013061851,0.0001378507,0.000040195693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015248959,0.0014423782,0.0015618994,0.00062235445,0.00040743715,0.0016144372,0.0029563478,0.0026288382,0.0050279535],"category_scores_gemma":[0.0030687193,0.0009640832,0.00089704996,0.0011486756,0.00076591934,0.002419508,0.0021225794,0.0032320053,0.0023808347],"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":[0.00012253897,0.00012041287,0.00031399264,0.00021178593,0.0001574676,0.0000863486,0.0000551539,0.62873775,0.003071748,0.025703682,0.019162051,0.3222571],"study_design_scores_gemma":[0.0000057548605,0.000011589626,0.000034652905,0.000010105981,0.000006530378,0.000010436331,0.0000021860096,0.9896827,0.00058292475,0.008800588,0.000847642,0.000004840162],"about_ca_topic_score_codex":0.0037013504,"about_ca_topic_score_gemma":0.0044302326,"teacher_disagreement_score":0.0050279535,"about_ca_system_score_codex":0.0007208456,"about_ca_system_score_gemma":0.00097092096,"threshold_uncertainty_score":0.016820192},"labels":[],"label_agreement":null},{"id":"W2783837693","doi":"10.1109/msp.2017.2763441","title":"Recent Advances in Zero-Shot Recognition: Toward Data-Efficient Understanding of Visual Content","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Magazine","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":172,"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 British Columbia","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Class (philosophy); Pattern recognition (psychology); Shot (pellet); Zero (linguistics); Machine learning; Training set; Set (abstract data type)","score_opus":0.21662575120421834,"score_gpt":0.34351921179909145,"score_spread":0.1268934605948731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783837693","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":[0.009600243,0.011534418,0.97362286,0.000721321,0.00022713047,0.000060731087,0.00031081465,0.0019004928,0.0020219078],"genre_scores_gemma":[0.25721154,0.025665596,0.7010359,0.0013671523,0.0011751308,0.0002189915,0.005004738,0.00057642674,0.007744474],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829465,0.00036478712,0.00010028843,0.0007064478,0.000443792,0.000089987945],"domain_scores_gemma":[0.9964438,0.0017673126,0.00019948429,0.0009088886,0.0005319063,0.00014857958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018764229,0.0014766515,0.0023202854,0.0017175841,0.00040838929,0.0021985206,0.0031824457,0.0015625226,0.0027688753],"category_scores_gemma":[0.0068966104,0.00062612194,0.0011068023,0.002146612,0.0015875085,0.0061173374,0.0027733212,0.003318019,0.002098795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017102923,0.00017931095,0.0010965612,0.00071131677,0.00010398149,0.00008107978,0.00020167664,0.025383912,0.014253613,0.0137332305,0.009179818,0.9349046],"study_design_scores_gemma":[0.000021571403,0.00022790341,0.002332099,0.00019948775,0.000087558365,0.0005659067,0.00025385918,0.8265184,0.02818476,0.111393854,0.030105017,0.000109521774],"about_ca_topic_score_codex":0.003419113,"about_ca_topic_score_gemma":0.003010707,"teacher_disagreement_score":0.003419113,"about_ca_system_score_codex":0.0008650648,"about_ca_system_score_gemma":0.00096991315,"threshold_uncertainty_score":0.009923637},"labels":[],"label_agreement":null},{"id":"W2808847742","doi":"10.1145/3219819.3220021","title":"Ranking Distillation","year":2018,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":165,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Ranking (information retrieval); Computer science; Rank (graph theory); Inference; Distillation; Learning to rank; Machine learning; Artificial intelligence; Ranking SVM; Information retrieval; Mathematics","score_opus":0.014478857751444854,"score_gpt":0.25016796714320455,"score_spread":0.2356891093917597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808847742","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015125183,0.00052417396,0.97630537,0.00049193593,0.00014443668,0.000100397236,0.00042807095,0.0041633784,0.0027170132],"genre_scores_gemma":[0.3980388,0.00045495917,0.58436453,0.0008155737,0.00029200615,0.00031665398,0.0031442535,0.0007277823,0.011845373],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99705553,0.0010728395,0.00017146845,0.00084856333,0.0006169248,0.00023471114],"domain_scores_gemma":[0.9948094,0.0020708824,0.00030805072,0.0016924995,0.00092920504,0.00019000367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027134907,0.0015208458,0.0018823094,0.0015393895,0.00080725586,0.0017042154,0.003346172,0.001671028,0.0068931133],"category_scores_gemma":[0.012267593,0.0008105175,0.0012568915,0.0017936006,0.0010600064,0.0050118626,0.0022999395,0.003347158,0.0037471326],"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":[0.00039772593,0.00051798986,0.003260564,0.00043322847,0.00022668733,0.00014253167,0.00016562862,0.27968174,0.006689691,0.04869591,0.01960986,0.64017844],"study_design_scores_gemma":[0.000040382158,0.00012621078,0.00027783526,0.000019583913,0.000023896995,0.000070022885,0.000019351915,0.96729887,0.004131288,0.02329292,0.0046690586,0.000030537016],"about_ca_topic_score_codex":0.004463258,"about_ca_topic_score_gemma":0.00906688,"teacher_disagreement_score":0.0068931133,"about_ca_system_score_codex":0.0010509654,"about_ca_system_score_gemma":0.002106094,"threshold_uncertainty_score":0.023059785},"labels":[],"label_agreement":null},{"id":"W2821744580","doi":"10.1007/978-3-030-30671-7_2","title":"M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":17,"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 British Columbia","funders":"","keywords":"Discriminative model; Computer science; MNIST database; Metric (unit); Artificial intelligence; Adaptation (eye); Pattern recognition (psychology); Domain adaptation; Domain (mathematical analysis); Task (project management); Code (set theory); Function (biology); Machine learning; Source code; Deep learning; Mathematics; Set (abstract data type)","score_opus":0.03465676586048811,"score_gpt":0.24084266097413398,"score_spread":0.20618589511364588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2821744580","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046596336,0.00046970887,0.9836278,0.00013180808,0.00015358427,0.000063772575,0.00037936348,0.009468268,0.0010461179],"genre_scores_gemma":[0.14741799,0.00043874406,0.83730716,0.00056145416,0.00015794387,0.00032096723,0.0036542031,0.001527285,0.008614253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998926,0.00028694273,0.000044563152,0.00040663243,0.00023565342,0.00010023192],"domain_scores_gemma":[0.9989586,0.00030148186,0.000045324585,0.00044783362,0.00015918477,0.00008751645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012577927,0.0015041878,0.0020391985,0.0011715317,0.0006673135,0.0012362854,0.0036834546,0.002070594,0.004741571],"category_scores_gemma":[0.003475277,0.00081746565,0.0014639087,0.0013295612,0.0007458058,0.0023483313,0.0041465093,0.003929007,0.0036825933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003110705,0.0003993771,0.0009574157,0.00023574287,0.0002892851,0.00014654481,0.00008291264,0.11407231,0.014374733,0.015565836,0.041776665,0.8117881],"study_design_scores_gemma":[0.000016402624,0.000039225743,0.00019888055,0.000010500671,0.000016916876,0.000060555954,0.000014272348,0.97558755,0.004517192,0.015913526,0.0036088768,0.0000161535],"about_ca_topic_score_codex":0.0057464503,"about_ca_topic_score_gemma":0.009576629,"teacher_disagreement_score":0.0057464503,"about_ca_system_score_codex":0.00085146254,"about_ca_system_score_gemma":0.0011906764,"threshold_uncertainty_score":0.015862167},"labels":[],"label_agreement":null},{"id":"W2883207092","doi":"","title":"Aggregated Learning: A Vector Quantization Approach to Learning with Neural Networks.","year":2018,"lang":"en","type":"preprint","venue":"NPARC","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vector quantization; Quantization (signal processing); Artificial neural network; Learning vector quantization; Equivalence (formal languages); Computer science; Artificial intelligence; Smoothing; Algorithm; Mathematics; Theoretical computer science; Machine learning; Discrete mathematics; Computer vision","score_opus":0.02594470968825211,"score_gpt":0.24579449965841707,"score_spread":0.21984978997016497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883207092","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":[0.0051965225,0.0007150376,0.99157387,0.0002768421,0.00008492565,0.000030959323,0.00011405326,0.00043255466,0.0015752873],"genre_scores_gemma":[0.54050326,0.0013846246,0.44983923,0.0006803876,0.00038733907,0.00029505027,0.00076855574,0.00029377165,0.0058478373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907035,0.0003479361,0.000052477866,0.00020189202,0.00026566235,0.00006167643],"domain_scores_gemma":[0.9982254,0.00081200653,0.00015929564,0.00041946207,0.00028167825,0.000102121645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019547374,0.0008571416,0.001085115,0.00091304036,0.00044655518,0.0014017867,0.002385062,0.0010232705,0.0030919532],"category_scores_gemma":[0.008105349,0.000401749,0.0005810504,0.0011994519,0.0014086041,0.004135484,0.0030881676,0.0028197798,0.00063946866],"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":[0.00013015434,0.000116173076,0.0011259458,0.00033041413,0.00016407578,0.000073262316,0.00025410688,0.45281598,0.003935874,0.28767544,0.009510953,0.24386773],"study_design_scores_gemma":[0.0000064942683,0.000025901674,0.0000894971,0.00001483624,0.000009440656,0.000014107808,0.000011160386,0.8553038,0.0010301745,0.14212133,0.0013665986,0.0000066523467],"about_ca_topic_score_codex":0.0021134496,"about_ca_topic_score_gemma":0.0024292911,"teacher_disagreement_score":0.0030919532,"about_ca_system_score_codex":0.0012396554,"about_ca_system_score_gemma":0.0008549018,"threshold_uncertainty_score":0.010343611},"labels":[],"label_agreement":null},{"id":"W2884092934","doi":"10.1613/jair.1.12105","title":"General Value Function Networks","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Machine Intelligence Institute; Institut de Valorisation des Données; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Recurrent neural network; Computer science; Function (biology); Truncation (statistics); State (computer science); Observable; Artificial intelligence; Construct (python library); Domain (mathematical analysis); Machine learning; Algorithm; Artificial neural network; Mathematics","score_opus":0.17378483023513694,"score_gpt":0.41161470207466855,"score_spread":0.2378298718395316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884092934","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":[0.0048456145,0.0007034108,0.9903195,0.0003285339,0.000056060613,0.000032734904,0.00013260407,0.00024809875,0.0033334992],"genre_scores_gemma":[0.5139069,0.0023074402,0.46672583,0.0005092653,0.00021907051,0.0004567803,0.0010545171,0.0002660844,0.014554148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99915683,0.00030314946,0.000049690865,0.00026153162,0.00014257098,0.000086245374],"domain_scores_gemma":[0.99843866,0.0009833758,0.00013120408,0.00014824147,0.00023736861,0.00006125607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015492603,0.0012000232,0.0011448277,0.00075038086,0.00050113583,0.0014855985,0.0022303348,0.0021903368,0.0050246576],"category_scores_gemma":[0.008255697,0.000639345,0.0010300928,0.000946185,0.0014079892,0.0034383785,0.001701999,0.0023383931,0.0013170049],"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":[0.000053097287,0.000036401536,0.0008621249,0.00013352818,0.000052924534,0.00012507655,0.00009267275,0.70223784,0.00097548554,0.21310517,0.0030955488,0.07923016],"study_design_scores_gemma":[0.000004469489,0.000009906749,0.0000661751,0.000013698708,0.000004674768,0.000021923377,0.000006113997,0.9055785,0.00021369774,0.09249068,0.0015829462,0.0000072707235],"about_ca_topic_score_codex":0.003776746,"about_ca_topic_score_gemma":0.0033932049,"teacher_disagreement_score":0.0050246576,"about_ca_system_score_codex":0.001440309,"about_ca_system_score_gemma":0.00093045837,"threshold_uncertainty_score":0.016809225},"labels":[],"label_agreement":null},{"id":"W2890732907","doi":"","title":"Supervised autoencoders: Improving generalization performance with unsupervised regularizers","year":2018,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":152,"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":"Generalization; Computer science; Regularization (linguistics); Artificial intelligence; Artificial neural network; Generalization error; Autoencoder; Stability (learning theory); Norm (philosophy); Machine learning; Encoder; Supervised learning; Unsupervised learning; Pattern recognition (psychology); Mathematics","score_opus":0.014898084041318364,"score_gpt":0.21568427080568167,"score_spread":0.2007861867643633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890732907","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":[0.05463598,0.00050808635,0.94112664,0.00035763587,0.000057431673,0.00006697112,0.0000721046,0.0011225295,0.00205261],"genre_scores_gemma":[0.70930713,0.00056503364,0.28499922,0.0004588916,0.00013807935,0.0001946288,0.00038814754,0.00028604025,0.0036628272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998672,0.0005258484,0.00008164458,0.00034980683,0.00028108468,0.00008965732],"domain_scores_gemma":[0.994091,0.003182151,0.00039872385,0.0016241123,0.00058124133,0.0001227482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038825858,0.0013292364,0.0011076845,0.0005570212,0.00045392953,0.0008601902,0.0015669424,0.0015419385,0.0012518538],"category_scores_gemma":[0.0135344025,0.00062097836,0.00082223595,0.00044431258,0.0013081079,0.0026921777,0.0021688237,0.0024214839,0.0005758459],"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":[0.00027675633,0.00023251784,0.0025217861,0.00015544001,0.00022254461,0.00010332461,0.00020950299,0.7546239,0.010999677,0.027921895,0.0032564432,0.19947626],"study_design_scores_gemma":[0.00000831436,0.000045230456,0.00017648202,0.000008537185,0.000010272819,0.000023176439,0.000008610152,0.98851985,0.0018567032,0.009086769,0.00025008057,0.0000059657727],"about_ca_topic_score_codex":0.0021596567,"about_ca_topic_score_gemma":0.003012485,"teacher_disagreement_score":0.0038825858,"about_ca_system_score_codex":0.0006619469,"about_ca_system_score_gemma":0.0011122025,"threshold_uncertainty_score":0.020533323},"labels":[],"label_agreement":null},{"id":"W2892122929","doi":"","title":"MetaGAN: an adversarial approach to few-shot learning","year":2018,"lang":"en","type":"article","venue":"Cambridge University Engineering Department Publications Database","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":283,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"","keywords":"Shot (pellet); Artificial intelligence; Computer science; Machine learning; Generator (circuit theory); Decision boundary; Adversarial system; Task (project management); Contextual image classification; Supervised learning; Simple (philosophy); One shot; Image (mathematics); Pattern recognition (psychology); Support vector machine; Engineering; Artificial neural network; Power (physics)","score_opus":0.025771131403065813,"score_gpt":0.2300618105105976,"score_spread":0.2042906791075318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892122929","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":[0.008069852,0.00035115538,0.9895107,0.0002796287,0.000050874125,0.00006114083,0.00007180151,0.00045121417,0.001153588],"genre_scores_gemma":[0.6454016,0.00061925576,0.34286934,0.0010835786,0.00031428854,0.00041239586,0.00067544833,0.00033482045,0.008289278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990017,0.000409039,0.000031722066,0.0002770686,0.00018609349,0.00009435832],"domain_scores_gemma":[0.9975459,0.0016447369,0.00016834235,0.00038572957,0.00015007073,0.00010511373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025422631,0.0013605853,0.0017652072,0.0009569301,0.00047618125,0.0010706903,0.0028997257,0.002065602,0.002392225],"category_scores_gemma":[0.0054901205,0.00076688343,0.0010189041,0.000578589,0.0018882729,0.0028580395,0.0030581248,0.0030764048,0.00062186737],"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":[0.00019819815,0.00013595236,0.0013098472,0.00020161807,0.00017793631,0.00015485522,0.00013893157,0.7893047,0.005807046,0.06817741,0.0040106387,0.1303828],"study_design_scores_gemma":[0.0000046490027,0.000030717823,0.00008132061,0.000009806056,0.000006620117,0.000035464873,0.000005085443,0.9758448,0.00083624496,0.022654388,0.00048301203,0.000007837045],"about_ca_topic_score_codex":0.0013097154,"about_ca_topic_score_gemma":0.0017379359,"teacher_disagreement_score":0.0028997257,"about_ca_system_score_codex":0.0010662874,"about_ca_system_score_gemma":0.0007548634,"threshold_uncertainty_score":0.0134449005},"labels":[],"label_agreement":null},{"id":"W2894854504","doi":"10.3390/app8122512","title":"Transfer Incremental Learning Using Data Augmentation","year":2018,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Flexibility (engineering); Transfer of learning; Feature selection; Incremental learning; Feature (linguistics); Deep learning; Class (philosophy); Selection (genetic algorithm); Mathematics","score_opus":0.13353945574292947,"score_gpt":0.33802085163833645,"score_spread":0.20448139589540698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894854504","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":[0.02310467,0.00065898936,0.9716555,0.00015507978,0.00011599363,0.000099419674,0.00012815176,0.0023538738,0.0017283997],"genre_scores_gemma":[0.6246375,0.0005758626,0.3698696,0.00029718966,0.00014048954,0.00030979098,0.00082775945,0.00019362407,0.003148217],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994816,0.00012175915,0.000034296027,0.00017806285,0.00014250966,0.000041787982],"domain_scores_gemma":[0.9984786,0.00073356397,0.00009530219,0.00041899248,0.00022320886,0.000050301252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008797994,0.0007898758,0.0009990042,0.00062339514,0.00028721066,0.0006969058,0.0019172736,0.00073101243,0.0017794871],"category_scores_gemma":[0.004058243,0.00035242297,0.0007367778,0.0008292111,0.0007341761,0.0018963116,0.0015642704,0.0014084586,0.0006582268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017868516,0.00027913248,0.0020116274,0.00025743598,0.0001340849,0.00018955221,0.00018362467,0.2518367,0.014234013,0.011110909,0.0058001736,0.7137841],"study_design_scores_gemma":[0.000008954494,0.000057100115,0.00023534859,0.00000909856,0.0000149392445,0.000057327314,0.000013325473,0.9853681,0.0045519997,0.007768538,0.001904335,0.000010989507],"about_ca_topic_score_codex":0.0019026018,"about_ca_topic_score_gemma":0.002314232,"teacher_disagreement_score":0.0019172736,"about_ca_system_score_codex":0.00045056635,"about_ca_system_score_gemma":0.00074492535,"threshold_uncertainty_score":0.005953014},"labels":[],"label_agreement":null},{"id":"W2895106137","doi":"10.48550/arxiv.1810.02334","title":"Unsupervised Learning via Meta-Learning","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":129,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Unsupervised learning; Computer science; Meta learning (computer science); Machine learning; Artificial intelligence; Cluster analysis; Embedding; Construct (python library); Variety (cybernetics); Task (project management); Feature learning; Conceptual clustering; Competitive learning; Fuzzy clustering","score_opus":0.12502169649809586,"score_gpt":0.19699164163688576,"score_spread":0.0719699451387899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895106137","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007191187,0.00027753235,0.9910154,0.00020687035,0.000020799329,0.000047834328,0.00009110542,0.00055872026,0.0005906039],"genre_scores_gemma":[0.40567365,0.0006299922,0.58808863,0.00047614027,0.00017410185,0.0005913823,0.0012921387,0.00038587968,0.0026880938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975388,0.0011222272,0.00011977406,0.0007927495,0.00030033302,0.00012610502],"domain_scores_gemma":[0.993232,0.003987524,0.00043687702,0.0016179603,0.00052817067,0.00019747141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034596913,0.0017590206,0.0017578204,0.001671136,0.0007576051,0.0020262299,0.0034505348,0.0019366338,0.0014439684],"category_scores_gemma":[0.012072373,0.0010706384,0.002141276,0.0014187098,0.0022313232,0.003995533,0.0032083597,0.0036282819,0.0008303789],"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":[0.00015888897,0.00022189584,0.003141972,0.00034236003,0.0004384108,0.00011516,0.0002469245,0.74217445,0.003939208,0.049041733,0.0036948484,0.19648407],"study_design_scores_gemma":[0.000009622385,0.000027556462,0.0001429494,0.000015836347,0.000015528622,0.000020954896,0.000011870585,0.9533325,0.0010559061,0.044813577,0.0005427434,0.000010892583],"about_ca_topic_score_codex":0.00150385,"about_ca_topic_score_gemma":0.0030813622,"teacher_disagreement_score":0.0034596913,"about_ca_system_score_codex":0.0015311372,"about_ca_system_score_gemma":0.0015507982,"threshold_uncertainty_score":0.018296838},"labels":[],"label_agreement":null},{"id":"W2895195933","doi":"10.1007/978-3-030-18305-9_66","title":"Generating Accurate Virtual Examples for Lifelong Machine Learning","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Computer science; Restricted Boltzmann machine; Artificial intelligence; Task (project management); Machine learning; Set (abstract data type); Lifelong learning; Boltzmann machine; Training set; Artificial neural network","score_opus":0.03258662774635546,"score_gpt":0.263475812019237,"score_spread":0.23088918427288152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895195933","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026521953,0.00061737763,0.964197,0.00017125181,0.00016910372,0.00012468593,0.0002506,0.0042917673,0.003656345],"genre_scores_gemma":[0.30884463,0.00038159543,0.6742837,0.00021375374,0.00009789608,0.000274032,0.0023082674,0.0007828117,0.012813341],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990706,0.00031796392,0.000045788172,0.00023713589,0.0002539331,0.00007471738],"domain_scores_gemma":[0.99737227,0.0011405097,0.000079244215,0.00083354575,0.00045398995,0.00012042587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012621658,0.0008649221,0.0011159201,0.00080507575,0.0005650142,0.0011106821,0.0027998856,0.0017644966,0.011372448],"category_scores_gemma":[0.005827574,0.0007043801,0.0005709099,0.0006715619,0.0005878525,0.0027654646,0.0031527246,0.0018392903,0.0048310854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003585278,0.00033528692,0.0010791698,0.00025584953,0.0000751566,0.0003236111,0.00019753764,0.13871141,0.015913531,0.012736505,0.02284671,0.80716676],"study_design_scores_gemma":[0.000013895515,0.000079845515,0.00016820413,0.000023720077,0.000011381169,0.00014254106,0.000041471212,0.9704878,0.009847313,0.014729066,0.004443619,0.000011270029],"about_ca_topic_score_codex":0.0010114994,"about_ca_topic_score_gemma":0.0021231328,"teacher_disagreement_score":0.011372448,"about_ca_system_score_codex":0.00047768123,"about_ca_system_score_gemma":0.00048925396,"threshold_uncertainty_score":0.03804463},"labels":[],"label_agreement":null},{"id":"W2895339967","doi":"10.1007/978-981-13-2291-4_78","title":"Tri-self-taught Learning of Artificial Neural Networks","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Generalization; Computer science; Simple (philosophy); Artificial neural network; Artificial intelligence; Machine learning; Test (biology); Mathematics","score_opus":0.008815636943748277,"score_gpt":0.20653954678410444,"score_spread":0.19772390984035615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895339967","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011950199,0.00087112497,0.97835827,0.00021770067,0.00018630677,0.000032177002,0.000052672764,0.0013380193,0.006993501],"genre_scores_gemma":[0.46094278,0.0009869336,0.50112647,0.0004538933,0.00031353856,0.0002113744,0.00060752436,0.00069797673,0.034659423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997323,0.00007949418,0.00001546788,0.00007757461,0.0000670885,0.00002797908],"domain_scores_gemma":[0.99910814,0.0003519539,0.00004904113,0.00020773332,0.00021933879,0.00006374065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007678025,0.00042536302,0.0006162687,0.00040335828,0.00029379997,0.0007551314,0.0017225698,0.0010314382,0.006183143],"category_scores_gemma":[0.0028556513,0.000376516,0.00052711426,0.00047423368,0.00060548855,0.0015810393,0.001851165,0.002056707,0.0017833028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012895437,0.00015360312,0.00064051244,0.00024379254,0.00010769375,0.00008618581,0.00016434683,0.29466054,0.009682594,0.07064809,0.0111544635,0.61232924],"study_design_scores_gemma":[0.000005495437,0.00003253793,0.000071026814,0.000014775805,0.0000069894136,0.000028578104,0.000007809095,0.97521824,0.0021914782,0.02046937,0.0019489254,0.0000047472454],"about_ca_topic_score_codex":0.0011900001,"about_ca_topic_score_gemma":0.0019500101,"teacher_disagreement_score":0.006183143,"about_ca_system_score_codex":0.000537761,"about_ca_system_score_gemma":0.0005162369,"threshold_uncertainty_score":0.02068466},"labels":[],"label_agreement":null},{"id":"W2901038558","doi":"10.1016/j.neuroimage.2019.03.026","title":"Unsupervised domain adaptation for medical imaging segmentation with self-ensembling","year":2019,"lang":"en","type":"preprint","venue":"NeuroImage","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Canada First Research Excellence Fund; Canadian Institutes of Health Research; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canada Foundation for Innovation; Réseau en Bio-Imagerie du Quebec","keywords":"Computer science; Segmentation; Generalization; Artificial intelligence; Domain adaptation; Domain (mathematical analysis); Task (project management); Adaptation (eye); Medical imaging; Machine learning; Modality (human–computer interaction); Deep learning; Image (mathematics); Image segmentation; Pattern recognition (psychology); Psychology; Mathematics","score_opus":0.022089714097448726,"score_gpt":0.2715188129530055,"score_spread":0.24942909885555678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901038558","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":[0.009286393,0.00046585588,0.9890455,0.00009155491,0.00003149743,0.00002403928,0.00003588451,0.0007374718,0.00028187694],"genre_scores_gemma":[0.37171987,0.0005249725,0.62241286,0.00030042953,0.00009751074,0.00018667139,0.0005746644,0.0005513964,0.003631627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957937,0.00017096878,0.000021467462,0.00012769495,0.00006201499,0.00003856478],"domain_scores_gemma":[0.99881256,0.0006943004,0.00006321854,0.00018641523,0.0001886893,0.00005477604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019061292,0.00082482473,0.0014968592,0.0008028617,0.00052907615,0.00081568386,0.0014999791,0.0018223772,0.0012283594],"category_scores_gemma":[0.004006983,0.00074953143,0.0010470304,0.0005660253,0.000894101,0.001254944,0.0015392005,0.0016899864,0.00062467955],"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":[0.00031573005,0.00017923689,0.0012301783,0.0002515782,0.0002310175,0.00011932841,0.00019705023,0.6717636,0.021867746,0.012346396,0.005500139,0.285998],"study_design_scores_gemma":[0.000004291706,0.000013529311,0.00012773424,0.000006483664,0.0000068448885,0.000025046922,0.0000049728856,0.99344265,0.0015218155,0.004575393,0.00026585947,0.0000053403705],"about_ca_topic_score_codex":0.0034672075,"about_ca_topic_score_gemma":0.005062769,"teacher_disagreement_score":0.0034672075,"about_ca_system_score_codex":0.00062513124,"about_ca_system_score_gemma":0.00091843726,"threshold_uncertainty_score":0.010080695},"labels":[],"label_agreement":null},{"id":"W2903787679","doi":"10.48550/arxiv.1812.08781","title":"Deep Metric Transfer for Label Propagation with Limited Annotated Data","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Generalization; Computer science; Metric (unit); Artificial intelligence; Constraint (computer-aided design); Transfer of learning; Object (grammar); Class (philosophy); Similarity (geometry); Machine learning; Semi-supervised learning; Pattern recognition (psychology); Scheme (mathematics); Image (mathematics); Mathematics","score_opus":0.15268409222838078,"score_gpt":0.21938462418409513,"score_spread":0.06670053195571435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903787679","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014062564,0.00016423147,0.9829146,0.00017831274,0.00003684885,0.000051737396,0.00009697386,0.0015220165,0.0009726869],"genre_scores_gemma":[0.4356109,0.00028973163,0.5565034,0.00039827538,0.00012341268,0.00029727977,0.0012012095,0.0004118066,0.005164005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983583,0.00064350065,0.000052410967,0.0005588557,0.0002771395,0.00010972162],"domain_scores_gemma":[0.99500895,0.0019948154,0.00040643328,0.0019222227,0.0004554385,0.00021204555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033513631,0.0014162773,0.0013744328,0.0010741558,0.0008045581,0.0011510908,0.0037118879,0.0019232194,0.0025213559],"category_scores_gemma":[0.011169675,0.00060300634,0.0008967368,0.0013375029,0.001984435,0.0052976417,0.0032648847,0.0039907466,0.0012330665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002905858,0.0006280881,0.002515349,0.0002851423,0.0001456184,0.00019006261,0.0004389734,0.41110623,0.01948543,0.046779588,0.0074102064,0.51072484],"study_design_scores_gemma":[0.000012538197,0.000060802526,0.0002171918,0.000009599452,0.000007740834,0.00004338309,0.000028187707,0.94425166,0.0059780804,0.04811466,0.0012626133,0.000013598174],"about_ca_topic_score_codex":0.0029098159,"about_ca_topic_score_gemma":0.003046521,"teacher_disagreement_score":0.0037118879,"about_ca_system_score_codex":0.0016203129,"about_ca_system_score_gemma":0.0011694707,"threshold_uncertainty_score":0.017723918},"labels":[],"label_agreement":null},{"id":"W2905954757","doi":"","title":"Frame Augmentation for Imbalanced Object Detection Datasets","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Object (grammar); Artificial intelligence; Frame (networking); Exploit; Perspective (graphical); Object detection; Class (philosophy); Set (abstract data type); Computer vision; Training set; Data set; Pattern recognition (psychology)","score_opus":0.011839027312742346,"score_gpt":0.30401579411248314,"score_spread":0.2921767667997408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905954757","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53219503,0.0072844895,0.38999954,0.0025711912,0.0025861948,0.001142938,0.029495094,0.026694305,0.00803135],"genre_scores_gemma":[0.71105707,0.0009507909,0.1951318,0.00077677035,0.00061140273,0.00084415585,0.084314235,0.00071311084,0.005600764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977697,0.000484118,0.00012301882,0.0008203117,0.0005663374,0.00023659258],"domain_scores_gemma":[0.9972537,0.0008127649,0.00026613724,0.0010634491,0.0004367775,0.00016710014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003382444,0.0026978154,0.0019777326,0.001966813,0.0011027273,0.0013944337,0.0028496129,0.0018592195,0.0027570939],"category_scores_gemma":[0.009882776,0.00053827895,0.0012256253,0.0018893767,0.001016258,0.002041043,0.002057115,0.002508109,0.0017547606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0039915484,0.0026110194,0.01273697,0.00090591726,0.000583182,0.000612626,0.0003859887,0.2156073,0.032691557,0.00416179,0.11621267,0.60949934],"study_design_scores_gemma":[0.0002444283,0.0006807174,0.011323321,0.00008454203,0.00010165217,0.0005949753,0.0002205111,0.9257197,0.027956896,0.010233036,0.022754919,0.00008528664],"about_ca_topic_score_codex":0.005762383,"about_ca_topic_score_gemma":0.007827004,"teacher_disagreement_score":0.005762383,"about_ca_system_score_codex":0.0015254711,"about_ca_system_score_gemma":0.00095113134,"threshold_uncertainty_score":0.017888308},"labels":[],"label_agreement":null},{"id":"W2908300307","doi":"10.1109/tmi.2018.2859478","title":"Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; University of California, San Diego; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Alzheimer's Association","keywords":"Artificial intelligence; Kernel (algebra); Computer science; Image segmentation; Weighting; Pattern recognition (psychology); Image (mathematics); Computer vision; Segmentation; Segmentation-based object categorization; Scale-space segmentation; Image texture; Mathematics; Medicine","score_opus":0.010779022156127879,"score_gpt":0.27250200641359057,"score_spread":0.2617229842574627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908300307","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":[0.010007232,0.00013623963,0.98883736,0.00006720986,0.0000119772085,0.00003337518,0.000014923599,0.0006472969,0.0002443636],"genre_scores_gemma":[0.40061507,0.00030102982,0.5961179,0.00018224564,0.0000679523,0.00020614172,0.00028695675,0.00032012296,0.0019026105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917513,0.00024149488,0.000060301045,0.00020218045,0.0002512316,0.00006968649],"domain_scores_gemma":[0.998456,0.0006832147,0.00015898289,0.00030916755,0.00033485267,0.00005782031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024084123,0.0009218349,0.0012936939,0.0015962222,0.00032769362,0.0008217056,0.001507343,0.0013313276,0.001159402],"category_scores_gemma":[0.006316328,0.000512873,0.000987582,0.0016141882,0.0010510514,0.0023724604,0.0018425831,0.0012974957,0.00070548325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020824977,0.00022559555,0.0014043717,0.00015591638,0.00020158481,0.00009649895,0.00015007352,0.4003149,0.034323584,0.010332468,0.001877419,0.55070937],"study_design_scores_gemma":[0.000006618165,0.000039439,0.00020908243,0.0000032325433,0.000009890253,0.000027410744,0.0000076393735,0.9884885,0.004286535,0.0065663625,0.00034554448,0.000009822341],"about_ca_topic_score_codex":0.0028465143,"about_ca_topic_score_gemma":0.0021519596,"teacher_disagreement_score":0.0028465143,"about_ca_system_score_codex":0.00095453067,"about_ca_system_score_gemma":0.00089829025,"threshold_uncertainty_score":0.012737095},"labels":[],"label_agreement":null},{"id":"W2912990207","doi":"","title":"Unsupervised Heterogeneous Domain Adaptation with Sparse Feature Transformation","year":2018,"lang":"en","type":"article","venue":"Asian Conference on Machine Learning","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Transformation (genetics); Artificial intelligence; Feature (linguistics); Adaptation (eye); Pattern recognition (psychology); Domain (mathematical analysis); Mathematics","score_opus":0.02507312202427669,"score_gpt":0.24436714503878534,"score_spread":0.21929402301450865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912990207","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":[0.019577092,0.00025732632,0.977683,0.00008877495,0.000065661305,0.000036491725,0.00013961426,0.0011616268,0.0009903912],"genre_scores_gemma":[0.61084294,0.00045188385,0.37834296,0.00040170862,0.0001299867,0.00019809189,0.002649581,0.0004596108,0.006523292],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994215,0.0001582119,0.00002194355,0.00022086831,0.00010854555,0.00006899615],"domain_scores_gemma":[0.999305,0.00021584942,0.00004533978,0.0002623614,0.00012300418,0.000048377817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058945006,0.00074894703,0.0011505581,0.00078159245,0.00042705034,0.00062000204,0.0012735807,0.00084529334,0.0017021794],"category_scores_gemma":[0.0019784914,0.0003345116,0.001090177,0.0012355349,0.00055178296,0.0013459347,0.0019906492,0.001378417,0.0010362506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054694165,0.0005115316,0.002519233,0.0001909674,0.000318691,0.00033978326,0.00022821085,0.1859633,0.068963945,0.012222461,0.014635163,0.7135598],"study_design_scores_gemma":[0.000016066806,0.000048478974,0.0005962712,0.000005328242,0.000027776126,0.00012979834,0.000038591843,0.9806445,0.0068736146,0.009761712,0.0018429514,0.000014941771],"about_ca_topic_score_codex":0.002142764,"about_ca_topic_score_gemma":0.0030166137,"teacher_disagreement_score":0.002142764,"about_ca_system_score_codex":0.00026920668,"about_ca_system_score_gemma":0.00055293884,"threshold_uncertainty_score":0.0056943893},"labels":[],"label_agreement":null},{"id":"W2913283038","doi":"10.1145/3241055","title":"Deep Semantic Mapping for Heterogeneous Multimedia Transfer Learning Using Co-Occurrence Data","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":31,"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 British Columbia; McMaster University; St. Francis Xavier University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China-Yunnan Joint Fund","keywords":"Computer science; Transfer of learning; Deep learning; Artificial intelligence; Semantic matching; Feature learning; Representation (politics); Matching (statistics); Domain (mathematical analysis); Feature (linguistics); Subspace topology; Bridge (graph theory); Natural language processing; Mathematics","score_opus":0.0813685521780696,"score_gpt":0.3331669746437586,"score_spread":0.251798422465689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913283038","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022767354,0.00029025233,0.9748028,0.00015809304,0.00004206066,0.000058679536,0.000105885694,0.0007242067,0.0010507407],"genre_scores_gemma":[0.77788794,0.00060070166,0.21473221,0.00030171967,0.00008958797,0.00027579622,0.000981104,0.000143478,0.0049874466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994261,0.00011842178,0.000029346018,0.00024111778,0.00011110723,0.000073953255],"domain_scores_gemma":[0.9994203,0.00021957414,0.000067049405,0.00013661938,0.000110728,0.00004574413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011031481,0.0010585473,0.0009096352,0.0013985471,0.00050087314,0.00075940095,0.0018786024,0.0012617361,0.0019530533],"category_scores_gemma":[0.0027605272,0.00038148917,0.001133913,0.0015278974,0.0009409981,0.003031185,0.0023190493,0.0024130186,0.00055229384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021769742,0.00040907625,0.0021416962,0.00017462077,0.00015008928,0.0002717649,0.00024720607,0.4050187,0.014264643,0.025333803,0.004483737,0.5472869],"study_design_scores_gemma":[0.0000047396366,0.000025515577,0.00019426645,0.0000051205043,0.000009602567,0.000021041673,0.000025949837,0.9856107,0.0023314832,0.011167963,0.00059690635,0.0000066406906],"about_ca_topic_score_codex":0.0059717274,"about_ca_topic_score_gemma":0.005210804,"teacher_disagreement_score":0.0059717274,"about_ca_system_score_codex":0.0012465596,"about_ca_system_score_gemma":0.001202047,"threshold_uncertainty_score":0.0118739605},"labels":[],"label_agreement":null},{"id":"W2915058596","doi":"10.48550/arxiv.1902.02497","title":"CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Discriminative model; Convolutional neural network; Artificial intelligence; Interpretation (philosophy); Machine learning; Class (philosophy); Regularization (linguistics); Pattern recognition (psychology); Deep neural networks; Deep learning","score_opus":0.036229989059410675,"score_gpt":0.1854060361997821,"score_spread":0.14917604714037142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915058596","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":[0.014718335,0.00031057728,0.98161465,0.00025714928,0.00006480092,0.000030956166,0.0002557762,0.0012636814,0.0014840204],"genre_scores_gemma":[0.73231125,0.00056989247,0.2571085,0.0005183379,0.00018153345,0.00015402153,0.001737513,0.00054623757,0.006872734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955577,0.00014082891,0.000013747462,0.0001432983,0.000092226626,0.0000541747],"domain_scores_gemma":[0.9993112,0.00021022414,0.00010016878,0.00019354495,0.00012476851,0.000060106224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071187894,0.001276919,0.00065180194,0.0006250035,0.00028290015,0.0009831201,0.0019224726,0.0011882138,0.0023015714],"category_scores_gemma":[0.0023907665,0.00046961557,0.0008792212,0.00061188743,0.00092535705,0.0018643147,0.0015353542,0.002761735,0.00059955387],"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":[0.00045892203,0.00015573436,0.002262496,0.00019209091,0.00015190238,0.00032706722,0.00032014295,0.56317157,0.023440642,0.06211709,0.011514424,0.33588797],"study_design_scores_gemma":[0.000010681377,0.000029936757,0.00023414695,0.000009443999,0.000013483622,0.000032124866,0.000012263286,0.9700677,0.0029163677,0.025521226,0.0011418656,0.000010825833],"about_ca_topic_score_codex":0.003123714,"about_ca_topic_score_gemma":0.0049203783,"teacher_disagreement_score":0.003123714,"about_ca_system_score_codex":0.0007889366,"about_ca_system_score_gemma":0.0010564015,"threshold_uncertainty_score":0.007699549},"labels":[],"label_agreement":null},{"id":"W2917776700","doi":"10.48550/arxiv.1902.07104","title":"Adaptive Cross-Modal Few-Shot Learning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Leverage (statistics); Artificial intelligence; Modalities; Discriminative model; Margin (machine learning); Machine learning; Metric (unit); Modal; Modality (human–computer interaction); Context (archaeology); Feature (linguistics); Feature learning; Focus (optics); Pattern recognition (psychology); Natural language processing","score_opus":0.11255020707853557,"score_gpt":0.21912869051236705,"score_spread":0.10657848343383149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917776700","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030081658,0.0009852513,0.9660167,0.00021319323,0.00007308514,0.00012317597,0.00016314897,0.0011587966,0.0011849304],"genre_scores_gemma":[0.7992342,0.0005318604,0.19199193,0.0007110552,0.00022174389,0.00039295413,0.0013301444,0.00026425518,0.0053219427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827886,0.00049344794,0.000080601654,0.00070945255,0.0002923298,0.00014546743],"domain_scores_gemma":[0.9973271,0.0013937968,0.0002243549,0.00045951124,0.000405891,0.00018939607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025660365,0.0012426949,0.0023970262,0.0019264257,0.0006921049,0.0011493389,0.0039946525,0.0019565327,0.0023886906],"category_scores_gemma":[0.006820317,0.00067328516,0.0015169423,0.0016319241,0.0013416244,0.0036263464,0.0024969308,0.0026350154,0.0009034959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005916349,0.0009609091,0.0056354124,0.00056489825,0.0005931761,0.0002681798,0.0005175311,0.37253505,0.016303072,0.015292449,0.007600497,0.57913727],"study_design_scores_gemma":[0.000010700198,0.000072944415,0.00046861934,0.000015335248,0.000024603214,0.00006488416,0.000030285915,0.9859409,0.0018854453,0.010915659,0.00055147253,0.000019121062],"about_ca_topic_score_codex":0.002949051,"about_ca_topic_score_gemma":0.004162412,"teacher_disagreement_score":0.0039946525,"about_ca_system_score_codex":0.0011510805,"about_ca_system_score_gemma":0.0008580862,"threshold_uncertainty_score":0.013570666},"labels":[],"label_agreement":null},{"id":"W2921087533","doi":"10.1016/j.neunet.2021.10.008","title":"Interpolation Consistency Training for Semi-supervised Learning","year":2019,"lang":"en","type":"preprint","venue":"Neural Networks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":137,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Finnish Center for Artificial Intelligence; Academy of Finland; Compute Canada","keywords":"Overfitting; Computer science; Interpolation (computer graphics); Regularization (linguistics); Benchmark (surveying); Machine learning; Artificial intelligence; Artificial neural network; Consistency (knowledge bases); Extrapolation; Mathematics; Statistics","score_opus":0.05758556039426875,"score_gpt":0.2817844642051455,"score_spread":0.22419890381087676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921087533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031953007,0.00011871846,0.9954373,0.00008796832,0.000025696738,0.000028726718,0.00004583871,0.00057251554,0.00048801664],"genre_scores_gemma":[0.30930734,0.0003184316,0.6853324,0.00048090811,0.00023778895,0.00041594054,0.00085610186,0.0005217389,0.0025293825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766695,0.000989691,0.000108121094,0.0004804431,0.0006453623,0.000109494744],"domain_scores_gemma":[0.9931386,0.0036968605,0.00050145236,0.0015632861,0.0008826988,0.0002169582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044676145,0.0010662535,0.0014383339,0.0009988934,0.0005676232,0.001003971,0.003053057,0.0015109342,0.0031444782],"category_scores_gemma":[0.016142001,0.00065713364,0.00082850026,0.0010549291,0.0020788605,0.0023402283,0.003147985,0.0034817527,0.0010246788],"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":[0.00034963715,0.00026279778,0.0020553747,0.00035858247,0.00018228104,0.00014252249,0.00020875636,0.62477493,0.008811783,0.073780105,0.008247771,0.28082547],"study_design_scores_gemma":[0.000009727267,0.00003575134,0.00010328827,0.000011162156,0.000004205924,0.000024833667,0.000005491919,0.9729149,0.0015801056,0.02459596,0.000707739,0.0000069105176],"about_ca_topic_score_codex":0.0014322605,"about_ca_topic_score_gemma":0.0019183251,"teacher_disagreement_score":0.0044676145,"about_ca_system_score_codex":0.0009177965,"about_ca_system_score_gemma":0.0014710664,"threshold_uncertainty_score":0.02362734},"labels":[],"label_agreement":null},{"id":"W2921599942","doi":"10.1109/wacv.2019.00133","title":"Learning Receptive Field Size by Learning Filter Size","year":2019,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Samsung","keywords":"Computer science; Filter (signal processing); Benchmark (surveying); Artificial intelligence; Convolutional neural network; Kernel (algebra); Receptive field; Backpropagation; Pattern recognition (psychology); Path (computing); Field (mathematics); Artificial neural network; Computer vision; Mathematics","score_opus":0.007249357642049703,"score_gpt":0.22397393056430687,"score_spread":0.21672457292225716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921599942","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":[0.076244466,0.00025789088,0.9205439,0.00014410517,0.000037186022,0.000044240678,0.0000766806,0.0011462221,0.0015052098],"genre_scores_gemma":[0.71037954,0.00031482955,0.2858215,0.00022554507,0.00004969014,0.00016050809,0.00029162838,0.00023118521,0.002525509],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997917,0.000028114806,0.000011537655,0.0001023493,0.000040033116,0.00002620963],"domain_scores_gemma":[0.9994893,0.00021279631,0.00006985508,0.000109555804,0.00008892743,0.0000295644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054000027,0.0006653639,0.00057191745,0.0004660428,0.00019580235,0.00052010413,0.0010246515,0.0007557314,0.0011016194],"category_scores_gemma":[0.002096563,0.00033948736,0.0004620595,0.0003668376,0.00056143705,0.0017617432,0.000588546,0.00084111886,0.00046524275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002208932,0.00023364405,0.0043723322,0.000159877,0.00010508504,0.000095964766,0.00011035326,0.31660002,0.15428635,0.010468274,0.0035809742,0.5097662],"study_design_scores_gemma":[0.00001797815,0.00007257089,0.0009827043,0.0000095148,0.00001969387,0.00007433856,0.0000139976855,0.9676042,0.022632381,0.0075031943,0.0010531684,0.000016363314],"about_ca_topic_score_codex":0.0021081835,"about_ca_topic_score_gemma":0.0027543881,"teacher_disagreement_score":0.0021081835,"about_ca_system_score_codex":0.00065385294,"about_ca_system_score_gemma":0.00074698153,"threshold_uncertainty_score":0.0047439933},"labels":[],"label_agreement":null},{"id":"W2924907019","doi":"10.24963/ijcai.2019/478","title":"A Principled Approach for Learning Task Similarity in Multitask Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Similarity (geometry); Generalization; Multi-task learning; Task (project management); Set (abstract data type); Artificial neural network; Divergence (linguistics); Perspective (graphical); Feature (linguistics)","score_opus":0.017760350503698074,"score_gpt":0.2506778170462925,"score_spread":0.23291746654259443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924907019","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":[0.0016583994,0.000103918086,0.9975453,0.0001242512,0.00002293402,0.000046620244,0.000016250315,0.00009976173,0.00038252142],"genre_scores_gemma":[0.33094993,0.00052740215,0.66337395,0.00062098075,0.0003278939,0.0008629455,0.00023380907,0.00020218846,0.0029008128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955492,0.001945696,0.00021002739,0.0010526546,0.0010584297,0.00018406016],"domain_scores_gemma":[0.9949008,0.0029394135,0.000445199,0.0009838941,0.00044356316,0.000287052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058890637,0.0015793564,0.0018232412,0.001453811,0.0008904787,0.0015518189,0.0036409944,0.0023882554,0.0021285962],"category_scores_gemma":[0.014206355,0.00096002355,0.001638208,0.001553423,0.002713878,0.0039840085,0.005141628,0.0049292394,0.0007632876],"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":[0.00031332378,0.00040088443,0.0014240564,0.00047442454,0.00036316254,0.0001659451,0.00040620993,0.54815084,0.008444986,0.18858382,0.0046733543,0.24659894],"study_design_scores_gemma":[0.000025080593,0.000113989656,0.00018791047,0.000014503496,0.000020294156,0.00005839777,0.000013025458,0.874768,0.0012128756,0.12226046,0.0013064779,0.000018952745],"about_ca_topic_score_codex":0.00096851646,"about_ca_topic_score_gemma":0.0010740899,"teacher_disagreement_score":0.0058890637,"about_ca_system_score_codex":0.0013004625,"about_ca_system_score_gemma":0.0017024209,"threshold_uncertainty_score":0.031144679},"labels":[],"label_agreement":null},{"id":"W2944987634","doi":"10.65109/jyrc5358","title":"Robot Learning by Collaborative Network Training: A Self-Supervised Method using Ranking","year":2019,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Dalhousie University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Ranking (information retrieval); Task (project management); Reinforcement learning; Artificial neural network; Inverse kinematics; Robotics; Robot; Process (computing); Engineering","score_opus":0.024451187075531122,"score_gpt":0.28035208644747295,"score_spread":0.2559008993719418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944987634","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":[0.009645822,0.00019336543,0.9865589,0.00011846,0.000056955476,0.000103324026,0.00005870602,0.0018739842,0.001390526],"genre_scores_gemma":[0.39426374,0.00017398616,0.5960784,0.0004763951,0.00014537256,0.0005598065,0.0007181819,0.000660458,0.0069236197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987595,0.0004316585,0.000051085048,0.00038147005,0.00023568225,0.00014052617],"domain_scores_gemma":[0.9973386,0.0011674793,0.00024332509,0.0005003174,0.0005951828,0.00015510347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023918017,0.0015651195,0.0016938733,0.0009031826,0.0007192879,0.00090026605,0.003708204,0.0020686807,0.0025588935],"category_scores_gemma":[0.0053297374,0.0008082773,0.0009501549,0.0007994091,0.0008663276,0.0015721103,0.0016682532,0.002063904,0.0012462124],"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":[0.00019945626,0.00032077744,0.0013923071,0.00012766795,0.00017258078,0.00012390169,0.00012127333,0.6639239,0.004813994,0.005167814,0.0075875283,0.31604877],"study_design_scores_gemma":[0.0000083138675,0.000031088868,0.000056907757,0.0000038753624,0.000006485427,0.000014157844,0.0000043942396,0.9975878,0.0007715202,0.001172213,0.00033924222,0.00000407548],"about_ca_topic_score_codex":0.0051208325,"about_ca_topic_score_gemma":0.008375042,"teacher_disagreement_score":0.0051208325,"about_ca_system_score_codex":0.000906311,"about_ca_system_score_gemma":0.001263273,"threshold_uncertainty_score":0.0126491785},"labels":[],"label_agreement":null},{"id":"W2945192089","doi":"10.1609/aaai.v34i04.6039","title":"Bivariate Beta-LSTM","year":2020,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Defense Acquisition Program Administration; Korea Advanced Institute of Science and Technology; Agency for Defense Development","keywords":"Sigmoid function; Bivariate analysis; Beta distribution; Computer science; Function (biology); Prior probability; Skewness; Algorithm; Artificial intelligence; Image (mathematics); Probabilistic logic; Term (time); Mathematics; Pattern recognition (psychology); Statistics; Machine learning; Bayesian probability; Physics; Artificial neural network","score_opus":0.15833930927051848,"score_gpt":0.316150595604048,"score_spread":0.15781128633352953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945192089","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03318777,0.00072378607,0.955186,0.0004894886,0.00014769734,0.000026382168,0.0008579751,0.002507477,0.0068733743],"genre_scores_gemma":[0.8662447,0.0008622525,0.121940054,0.0003868813,0.00012862362,0.000092366325,0.0014292994,0.0003528939,0.008562917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978644,0.00004144455,0.000010395512,0.00009110834,0.00003894451,0.000031681957],"domain_scores_gemma":[0.9995987,0.00015021357,0.000038501476,0.00006959208,0.00011110225,0.000031939122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003885638,0.00077777315,0.0005496298,0.0004167437,0.0001981617,0.000652972,0.0010109445,0.0006299348,0.0047367443],"category_scores_gemma":[0.0018561771,0.0003325361,0.00047667106,0.0006896352,0.00040744746,0.0016077652,0.0007845213,0.0012095344,0.0015689909],"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":[0.00043028136,0.00011011971,0.002732317,0.00024015972,0.0001252613,0.00027566147,0.00017089867,0.5150195,0.030742055,0.054440733,0.013664527,0.38204855],"study_design_scores_gemma":[0.0000063517637,0.000025329902,0.0004377575,0.000010076942,0.000014583061,0.00006347219,0.00000788648,0.9792796,0.003378612,0.015285053,0.0014832742,0.000008016596],"about_ca_topic_score_codex":0.0024273607,"about_ca_topic_score_gemma":0.0034466886,"teacher_disagreement_score":0.0047367443,"about_ca_system_score_codex":0.00063135655,"about_ca_system_score_gemma":0.0006800584,"threshold_uncertainty_score":0.015846014},"labels":[],"label_agreement":null},{"id":"W2946064590","doi":"10.1007/978-3-030-20257-6_23","title":"Image Classification Using Deep Neural Networks: Transfer Learning and the Handling of Unknown Images","year":2019,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Queen's University","funders":"","keywords":"Transfer of learning; Computer science; Artificial intelligence; Scratch; Robustness (evolution); Artificial neural network; Machine learning; Deep learning; Contextual image classification; Training set; Pattern recognition (psychology); Deep neural networks; Set (abstract data type); Image (mathematics)","score_opus":0.03968660611179555,"score_gpt":0.28742525563879534,"score_spread":0.24773864952699978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946064590","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006355171,0.0054346477,0.98143905,0.000583858,0.0002675568,0.000023467937,0.00011504961,0.0008089786,0.004972206],"genre_scores_gemma":[0.23722482,0.011124544,0.7030071,0.00047022145,0.0008066435,0.000104116305,0.000876996,0.0005954818,0.045790166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997663,0.00003868439,0.000013187696,0.000068273504,0.00008797111,0.000025619518],"domain_scores_gemma":[0.9995029,0.00022879128,0.00004198553,0.00009817159,0.000109400586,0.000018802213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062770175,0.00075257354,0.0009731803,0.00077112234,0.00024535568,0.0013133629,0.0013811456,0.0012586561,0.00342465],"category_scores_gemma":[0.001663507,0.00044399357,0.0006456286,0.0015908616,0.00081507623,0.0024083583,0.0011108239,0.0021575564,0.0015227331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006601228,0.000077347555,0.00033393138,0.00030877066,0.000057150555,0.00007981837,0.000069418595,0.09834986,0.0133021725,0.045527946,0.021484502,0.82034314],"study_design_scores_gemma":[0.0000028045295,0.00001991502,0.00025814935,0.000028905086,0.000012077192,0.00008650996,0.000016376182,0.9409529,0.005805163,0.046504095,0.0063000754,0.000013175509],"about_ca_topic_score_codex":0.0020953715,"about_ca_topic_score_gemma":0.0023431713,"teacher_disagreement_score":0.00342465,"about_ca_system_score_codex":0.0008403555,"about_ca_system_score_gemma":0.00040409277,"threshold_uncertainty_score":0.011456549},"labels":[],"label_agreement":null},{"id":"W2947548407","doi":"10.48550/arxiv.1905.12787","title":"The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":73,"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 Waterloo","funders":"","keywords":"Overfitting; Boosting (machine learning); Early stopping; Artificial intelligence; AdaBoost; Gradient boosting; Machine learning; Estimator; Mathematics; Support vector machine; Cross-validation; Generalization error; Computer science; Ensemble learning; Regularization (linguistics); Bias of an estimator; Random forest; Algorithm; Statistics; Minimum-variance unbiased estimator; Artificial neural network","score_opus":0.040544860570770946,"score_gpt":0.20001928367184565,"score_spread":0.1594744231010747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947548407","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008382157,0.13539095,0.8504137,0.0017685439,0.0023307,0.00010837669,0.00022510914,0.001006702,0.007917679],"genre_scores_gemma":[0.034458436,0.24602379,0.6847748,0.005147169,0.014164283,0.0012554035,0.001436727,0.0012603074,0.011479099],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99252605,0.0034596615,0.00055891194,0.0009747764,0.0022747144,0.00020587689],"domain_scores_gemma":[0.99192274,0.006304325,0.00035405668,0.00047123124,0.0008354908,0.000112140864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010489304,0.0037367868,0.0035980328,0.0043250374,0.00069341116,0.004059912,0.0027554466,0.0036590623,0.0052693286],"category_scores_gemma":[0.016650043,0.0017788496,0.0029560036,0.008058252,0.0027840196,0.0054324022,0.0022350694,0.0074058035,0.006035467],"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":[0.00007640841,0.00015781961,0.0013823967,0.0026953574,0.00040155175,0.0002277799,0.00041887988,0.05458426,0.0014020846,0.30875474,0.07594507,0.55395365],"study_design_scores_gemma":[0.0000308957,0.00023672597,0.0016146736,0.0012215658,0.00014569597,0.0009933362,0.00009016324,0.116183385,0.0014679773,0.60143644,0.27640313,0.00017606784],"about_ca_topic_score_codex":0.00216569,"about_ca_topic_score_gemma":0.0012618251,"teacher_disagreement_score":0.010489304,"about_ca_system_score_codex":0.0018181959,"about_ca_system_score_gemma":0.0014314689,"threshold_uncertainty_score":0.055473387},"labels":[],"label_agreement":null},{"id":"W2947612752","doi":"10.48550/arxiv.1905.12588","title":"Meta-Learning Representations for Continual Learning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":25,"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":"Forgetting; Computer science; Artificial intelligence; Machine learning; Competitive learning; Representation (politics); Artificial neural network; Function (biology); Proactive learning; Online learning; Feature learning; Incremental learning; Robot learning; Multimedia","score_opus":0.18021389863979412,"score_gpt":0.23086067998152826,"score_spread":0.05064678134173414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947612752","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":[0.01951676,0.00029420384,0.97736746,0.00033916952,0.000043500477,0.000035993686,0.00005698454,0.0009624798,0.0013834407],"genre_scores_gemma":[0.65399605,0.0002900024,0.34036615,0.00028062036,0.00010585852,0.00020098974,0.0003314461,0.00021315696,0.004215645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995321,0.0001457647,0.000031101343,0.00015477864,0.00009068292,0.00004563334],"domain_scores_gemma":[0.9977715,0.0009973997,0.00019205199,0.0006483906,0.00026557976,0.00012500804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014908024,0.0006288158,0.0007780912,0.0007508608,0.00044220983,0.0010552568,0.0021651532,0.0012483394,0.0030520554],"category_scores_gemma":[0.0073156967,0.00050181244,0.0006052955,0.00055950554,0.0011896357,0.0034265788,0.002110707,0.0025318337,0.00071663724],"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":[0.00022717328,0.00029991596,0.0019503754,0.00022043835,0.000106439315,0.00014095404,0.00038080505,0.49770308,0.0064578014,0.11535028,0.0055419253,0.37162086],"study_design_scores_gemma":[0.000010479957,0.000030806124,0.0000800017,0.00001292893,0.0000069088246,0.000028089024,0.000017549723,0.9506003,0.000973603,0.047237474,0.0009936936,0.000008039555],"about_ca_topic_score_codex":0.0011750455,"about_ca_topic_score_gemma":0.0018415774,"teacher_disagreement_score":0.0030520554,"about_ca_system_score_codex":0.000864359,"about_ca_system_score_gemma":0.00070307904,"threshold_uncertainty_score":0.010210156},"labels":[],"label_agreement":null},{"id":"W2948227433","doi":"","title":"How to Initialize your Network? Robust Initialization for WeightNorm & ResNets","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut de Valorisation des Données","keywords":"Initialization; Computer science; Residual; Robustness (evolution); Normalization (sociology); Generalization; Artificial intelligence; Algorithm; Machine learning; Mathematics","score_opus":0.16468919588074907,"score_gpt":0.21705242977774092,"score_spread":0.05236323389699185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948227433","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032293286,0.0010712463,0.9546359,0.0012559495,0.00036393502,0.00014689747,0.00026412815,0.00648173,0.0034868743],"genre_scores_gemma":[0.47248027,0.0007022833,0.5165955,0.0011269515,0.00015082731,0.00031440958,0.00097541814,0.0021041334,0.005550241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933344,0.0002011951,0.000039625073,0.00023566188,0.00010331323,0.000086704174],"domain_scores_gemma":[0.9984267,0.0005580467,0.0001621846,0.0004547912,0.0003005046,0.000097798315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023816547,0.0017806251,0.0008979145,0.000764536,0.0006398073,0.001210842,0.0016462229,0.0018920952,0.0040567648],"category_scores_gemma":[0.013011179,0.0007419434,0.0006379225,0.000644221,0.0011705679,0.0039500627,0.0014635168,0.003166459,0.0028941268],"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":[0.00060967694,0.00017531028,0.0035495055,0.00034314563,0.00019726389,0.00030347268,0.0003662207,0.4599072,0.026723338,0.033628833,0.028831983,0.4453641],"study_design_scores_gemma":[0.000051475097,0.000089028064,0.00065968913,0.00009919199,0.000039252544,0.00016624908,0.00007811141,0.92497426,0.03198463,0.0345655,0.007245973,0.00004671234],"about_ca_topic_score_codex":0.003251788,"about_ca_topic_score_gemma":0.0055459263,"teacher_disagreement_score":0.0040567648,"about_ca_system_score_codex":0.0010111507,"about_ca_system_score_gemma":0.0009379659,"threshold_uncertainty_score":0.013571203},"labels":[],"label_agreement":null},{"id":"W2950537964","doi":"","title":"Prototypical Networks for Few-shot Learning","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Machine learning; Metric (unit); Class (philosophy); Shot (pellet); One shot; Simple (philosophy); Inductive bias; Set (abstract data type); Metric space; Space (punctuation); Training set; Mathematics; Multi-task learning; Task (project management); Engineering","score_opus":0.13778688343207546,"score_gpt":0.22858102792160254,"score_spread":0.09079414448952708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950537964","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":[0.0276541,0.0009526045,0.9647123,0.0006027306,0.00010401899,0.00013915641,0.00047060745,0.0017432617,0.0036211293],"genre_scores_gemma":[0.62574995,0.00081546756,0.36019078,0.0011287638,0.000261129,0.000553404,0.0032685527,0.00040129898,0.0076306635],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986084,0.0004337202,0.000047967143,0.0005975803,0.00021296124,0.00009931434],"domain_scores_gemma":[0.99741346,0.0011765476,0.00018333433,0.0007502075,0.00032240924,0.0001539676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018900251,0.0014509727,0.0012598963,0.0012108012,0.0010412927,0.0013218272,0.003674523,0.0027049875,0.003928391],"category_scores_gemma":[0.007874032,0.000631715,0.0007925184,0.0011659636,0.0016045513,0.0045507797,0.0031617517,0.0030098625,0.0014984324],"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":[0.0004906004,0.00046229613,0.0035251484,0.0005216396,0.00023175406,0.00026061072,0.00046017682,0.3614242,0.011698062,0.09005247,0.020214941,0.5106581],"study_design_scores_gemma":[0.00001380107,0.000054921922,0.00022373816,0.000015828484,0.000013428949,0.000074138545,0.000030235458,0.9105469,0.0018218126,0.08472613,0.0024666148,0.000012428825],"about_ca_topic_score_codex":0.0029111067,"about_ca_topic_score_gemma":0.0052026557,"teacher_disagreement_score":0.003928391,"about_ca_system_score_codex":0.0015495148,"about_ca_system_score_gemma":0.0007881562,"threshold_uncertainty_score":0.013141811},"labels":[],"label_agreement":null},{"id":"W2950600632","doi":"","title":"Searching for Efficient Multi-Scale Architectures for Dense Image Prediction","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Pascal (unit); Parsing; Artificial intelligence; Scalability; Segmentation; Machine learning; Image segmentation; Programming language","score_opus":0.08126254275898451,"score_gpt":0.22841545278799857,"score_spread":0.14715291002901407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950600632","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":[0.06228972,0.0010187216,0.93101263,0.00061163475,0.000051723455,0.000058190974,0.00012839507,0.0025873152,0.0022417263],"genre_scores_gemma":[0.6179295,0.0006680802,0.3765452,0.00045483222,0.000073464114,0.00022788429,0.00072644633,0.00029631547,0.0030782637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969506,0.000081565406,0.000016930451,0.00010744099,0.000055649572,0.00004335663],"domain_scores_gemma":[0.9991003,0.00043432924,0.000081316946,0.0002062335,0.0001351491,0.000042558568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079796615,0.0009276284,0.0010851,0.0006391832,0.00045858562,0.0007898186,0.0016967924,0.0013087534,0.0022707765],"category_scores_gemma":[0.0030198595,0.00075454084,0.0008125071,0.0007702568,0.00077018415,0.002848155,0.0013670119,0.0020897465,0.0010807831],"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":[0.00011731225,0.0001519224,0.0015846825,0.00015935337,0.00008784322,0.00009365516,0.00013866743,0.6926232,0.010576073,0.018701106,0.0057875076,0.26997876],"study_design_scores_gemma":[0.000005678722,0.000017238997,0.0000756139,0.0000065388563,0.0000056942918,0.000011644127,0.000013335475,0.98941505,0.0009792849,0.009098642,0.0003676883,0.0000036021702],"about_ca_topic_score_codex":0.0039512953,"about_ca_topic_score_gemma":0.0072554415,"teacher_disagreement_score":0.0039512953,"about_ca_system_score_codex":0.0008995771,"about_ca_system_score_gemma":0.0008872358,"threshold_uncertainty_score":0.007856548},"labels":[],"label_agreement":null},{"id":"W2951397438","doi":"10.48550/arxiv.1705.02737","title":"MIDA: Multiple Imputation using Denoising Autoencoders","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Imputation (statistics); Missing data; Computer science; Artificial intelligence; Noise reduction; Data mining; Pattern recognition (psychology); Machine learning","score_opus":0.1481265403744293,"score_gpt":0.22424229799734685,"score_spread":0.07611575762291756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951397438","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023107112,0.00045439496,0.9936156,0.0002659476,0.00009053643,0.000035748362,0.00034257816,0.0023887344,0.0004958135],"genre_scores_gemma":[0.11813871,0.0008584536,0.8682976,0.0009292308,0.00030868748,0.00031055626,0.0044760555,0.00083469227,0.005846086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808383,0.00072348595,0.00009936427,0.0005888528,0.00038298714,0.00012153693],"domain_scores_gemma":[0.9966342,0.0016209296,0.00024928647,0.0008181557,0.0005270916,0.00015042826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045848866,0.001500067,0.0021465232,0.0013571797,0.00074385316,0.0019805233,0.0043440065,0.0023904536,0.0037536833],"category_scores_gemma":[0.012491135,0.0011998022,0.001942158,0.0015339559,0.0008638712,0.002953471,0.003471776,0.005658438,0.0037114804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048941403,0.00044868817,0.0059033697,0.00048367458,0.0009107484,0.00040658773,0.00028596216,0.32499728,0.007331922,0.029605344,0.0487166,0.5804204],"study_design_scores_gemma":[0.00001816135,0.0000275364,0.00042770142,0.000034294055,0.00002677327,0.00008240623,0.000018538598,0.96879447,0.0018523755,0.024941854,0.0037508693,0.000025028961],"about_ca_topic_score_codex":0.0041930014,"about_ca_topic_score_gemma":0.007903245,"teacher_disagreement_score":0.0045848866,"about_ca_system_score_codex":0.00088386325,"about_ca_system_score_gemma":0.001918882,"threshold_uncertainty_score":0.024247527},"labels":[],"label_agreement":null},{"id":"W2951470200","doi":"10.48550/arxiv.1311.1780","title":"Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal; Canadian Institute for Advanced Research","funders":"","keywords":"Pooling; Activation function; Computer science; Convolutional neural network; Perceptron; Artificial intelligence; Norm (philosophy); Artificial neural network; Benchmark (surveying); Unit sphere; Unit (ring theory); Pattern recognition (psychology); Deep learning; Algorithm; Mathematics; Combinatorics","score_opus":0.08332173844637374,"score_gpt":0.20469583721888845,"score_spread":0.12137409877251472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951470200","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015607334,0.0014553942,0.9770408,0.00028819346,0.00010195411,0.000051653195,0.00027808672,0.0023214873,0.00285495],"genre_scores_gemma":[0.62424135,0.0013382802,0.360493,0.00046028817,0.0001973202,0.00032993744,0.0015877906,0.0005549901,0.0107970005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992601,0.00015268124,0.000052769352,0.0002264396,0.00020846396,0.00009953262],"domain_scores_gemma":[0.9993987,0.00020560568,0.00008135737,0.00014915553,0.000119332275,0.00004573429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016165568,0.0018485032,0.0011284251,0.00060506194,0.00041732175,0.0013455958,0.0024469527,0.0013687029,0.003402831],"category_scores_gemma":[0.0035931792,0.00057549967,0.0010793359,0.00078325666,0.001018141,0.0036812902,0.0016797418,0.0018047935,0.0010883528],"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":[0.00028704142,0.00012934978,0.0008088279,0.00032040218,0.00018748948,0.00020642899,0.0000944776,0.5853518,0.014693544,0.06524751,0.0066800322,0.32599306],"study_design_scores_gemma":[0.000005822988,0.000043175874,0.000099311204,0.000010476341,0.000012174415,0.000022996695,0.0000054524876,0.9768803,0.0028703082,0.018916003,0.0011247573,0.000009165127],"about_ca_topic_score_codex":0.0045899097,"about_ca_topic_score_gemma":0.006257472,"teacher_disagreement_score":0.0045899097,"about_ca_system_score_codex":0.0018113779,"about_ca_system_score_gemma":0.0011488189,"threshold_uncertainty_score":0.013142586},"labels":[],"label_agreement":null},{"id":"W2951995852","doi":"10.48550/arxiv.1109.3737","title":"Learning where to Attend with Deep Architectures for Image Tracking","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Artificial intelligence; Eye tracking; Computer vision; Gaze; Orientation (vector space); Fixation (population genetics); Reinforcement learning; Object (grammar); Mathematics","score_opus":0.06465436479536825,"score_gpt":0.19603915105148675,"score_spread":0.1313847862561185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951995852","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":[0.043876667,0.00044031057,0.9532881,0.00034222507,0.000036567715,0.000016641729,0.0000679372,0.0009879194,0.0009435998],"genre_scores_gemma":[0.7768711,0.0004681297,0.21623805,0.00023847341,0.00006302059,0.000069060756,0.00023208151,0.00013404219,0.005686125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998124,0.000035310262,0.0000074795857,0.00006842502,0.000030057621,0.000046309546],"domain_scores_gemma":[0.99949574,0.00025486576,0.000059194324,0.00009213836,0.000059802995,0.00003818215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006011878,0.00073092984,0.0007294877,0.0003852669,0.0003910929,0.0007702679,0.0015499088,0.0015621166,0.0014791285],"category_scores_gemma":[0.002064463,0.0005462694,0.0007636754,0.0006552453,0.0006703214,0.0017743423,0.0012158065,0.0018247858,0.00039489302],"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":[0.00016673078,0.00015312046,0.0013698528,0.00006797777,0.0000969602,0.00010166081,0.00011642703,0.8119304,0.0150643,0.015408868,0.0019914731,0.1535322],"study_design_scores_gemma":[0.000002608896,0.000010461981,0.00008826308,0.0000017221695,0.000003668475,0.0000062425243,0.0000024322017,0.9924028,0.0009455868,0.006408857,0.00012491779,0.0000024851186],"about_ca_topic_score_codex":0.006483095,"about_ca_topic_score_gemma":0.008390616,"teacher_disagreement_score":0.006483095,"about_ca_system_score_codex":0.0010389774,"about_ca_system_score_gemma":0.0007372905,"threshold_uncertainty_score":0.012890756},"labels":[],"label_agreement":null},{"id":"W2952111767","doi":"10.48550/arxiv.1206.5538","title":"Representation Learning: A Review and New Perspectives","year":2012,"lang":"en","type":"review","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Feature learning; Artificial intelligence; Representation (politics); Machine learning; Computer science; Inference; Prior probability; Nonlinear dimensionality reduction; Unsupervised learning; Deep learning; Feature (linguistics); External Data Representation; Semi-supervised learning; Bayesian probability; Dimensionality reduction","score_opus":0.24703935995434667,"score_gpt":0.2658379097500409,"score_spread":0.01879854979569423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952111767","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":[0.00011418135,0.9941749,0.0017825618,0.0014858549,0.00031391895,0.00000412204,0.000023215365,0.000015713635,0.0020855614],"genre_scores_gemma":[0.0016710123,0.9951079,0.0010996639,0.00045826015,0.00095631473,0.000009159369,0.000047774436,0.000007200167,0.00064269966],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99953747,0.00012143787,0.000047917725,0.00009993333,0.00016415116,0.00002903426],"domain_scores_gemma":[0.9975509,0.0016916647,0.00011966248,0.000100773024,0.00045080713,0.000086170665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014563543,0.0009187418,0.0015484182,0.0036132794,0.00048311145,0.0024230336,0.001705535,0.0019277793,0.0053130393],"category_scores_gemma":[0.0034020806,0.00052030495,0.0005160327,0.006665069,0.0017046819,0.0054070028,0.0011679694,0.002712077,0.0030965568],"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":[0.000051163628,0.0000886632,0.00029424526,0.010653099,0.0000900652,0.0001244401,0.00015882685,0.0014975392,0.00045236756,0.07288871,0.081328556,0.8323723],"study_design_scores_gemma":[0.000009335932,0.00004819047,0.0005298092,0.0032213808,0.00004116984,0.0006246784,0.00013224296,0.0006677597,0.00019128661,0.045704916,0.94879705,0.000032205266],"about_ca_topic_score_codex":0.001960244,"about_ca_topic_score_gemma":0.0021545917,"teacher_disagreement_score":0.0053130393,"about_ca_system_score_codex":0.0013955907,"about_ca_system_score_gemma":0.0016178533,"threshold_uncertainty_score":0.017773867},"labels":[],"label_agreement":null},{"id":"W2962714319","doi":"10.1109/iccv.2015.483","title":"Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":339,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Samsung; Natural Sciences and Engineering Research Council of Canada; California Institute of Technology","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Embedding; Deep learning; Natural language processing; Machine learning; Pattern recognition (psychology)","score_opus":0.10633029381701785,"score_gpt":0.29941683982085193,"score_spread":0.19308654600383407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962714319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78734016,0.0028777532,0.19659767,0.00083414925,0.0003087013,0.00013741017,0.0036421497,0.0042740568,0.003987936],"genre_scores_gemma":[0.9671494,0.00030792618,0.023625141,0.0001392198,0.000055412263,0.00004937812,0.0054037194,0.00005346196,0.00321637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995926,0.00008410085,0.000021757474,0.00015919149,0.00006628461,0.000076022356],"domain_scores_gemma":[0.9983134,0.00089498935,0.00016625429,0.00014661902,0.00036784657,0.00011085045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086936116,0.0014051461,0.00068632053,0.0013741067,0.00029633517,0.0007868441,0.0013943731,0.001296702,0.0010761301],"category_scores_gemma":[0.0037718462,0.00040252245,0.0005974079,0.0008241546,0.0005273143,0.0020153862,0.0005628263,0.0011505011,0.0006206772],"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":[0.000733214,0.00077615184,0.02996995,0.00039656038,0.00019308801,0.0004911846,0.00014150838,0.61442983,0.010875272,0.0041442774,0.020139012,0.31770992],"study_design_scores_gemma":[0.0000059765234,0.00002487657,0.0009508304,0.000009642692,0.000007648903,0.000017271872,0.000012379948,0.99576557,0.0016372397,0.0013871612,0.00017652374,0.000004803802],"about_ca_topic_score_codex":0.014852055,"about_ca_topic_score_gemma":0.021557394,"teacher_disagreement_score":0.014852055,"about_ca_system_score_codex":0.0016227245,"about_ca_system_score_gemma":0.00062686775,"threshold_uncertainty_score":0.02953118},"labels":[],"label_agreement":null},{"id":"W2962791800","doi":"","title":"Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models","year":2017,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":21,"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 Waterloo","funders":"","keywords":"Conjugate gradient method; Conjugate residual method; Gradient descent; Conjugate; Derivation of the conjugate gradient method; Convergence (economics); Mathematics; Inference; Computation; Stochastic gradient descent; Nonlinear conjugate gradient method; Biconjugate gradient method; Computer science; Mathematical optimization; Applied mathematics; Algorithm; Artificial intelligence; Artificial neural network; Mathematical analysis","score_opus":0.1595042624709313,"score_gpt":0.3705196349275402,"score_spread":0.2110153724566089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962791800","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010539144,0.00005998309,0.9983669,0.000078992445,0.000015090573,0.000022571037,0.00001674617,0.00013460603,0.0002512075],"genre_scores_gemma":[0.14216788,0.00030428043,0.8535613,0.00031309345,0.00013155781,0.00024905524,0.00029747863,0.0005413104,0.0024340833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978198,0.0011558578,0.00010090454,0.0004607348,0.00035576505,0.000107026324],"domain_scores_gemma":[0.9940263,0.00436844,0.00030812717,0.00072298147,0.00041511908,0.0001591248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050255517,0.001400335,0.0018371686,0.0015379229,0.0010073023,0.0024219044,0.00387117,0.0020699361,0.0031514422],"category_scores_gemma":[0.018603183,0.0013825531,0.0018120791,0.0015505567,0.0024335505,0.0037223115,0.004171195,0.0045921565,0.00085762935],"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":[0.000121552985,0.000100682984,0.0013792326,0.0002153994,0.00025489694,0.00013069941,0.0002097464,0.5971611,0.0024585275,0.2197072,0.0046061226,0.1736548],"study_design_scores_gemma":[0.0000070499705,0.000008932861,0.00005935575,0.000009174259,0.000007935775,0.000016011547,0.0000068964905,0.9385165,0.00061110855,0.060034007,0.0007141939,0.000008810942],"about_ca_topic_score_codex":0.0068719285,"about_ca_topic_score_gemma":0.00775411,"teacher_disagreement_score":0.0068719285,"about_ca_system_score_codex":0.0017860458,"about_ca_system_score_gemma":0.0031019284,"threshold_uncertainty_score":0.02657795},"labels":[],"label_agreement":null},{"id":"W2963031676","doi":"10.1109/cvpr.2018.00575","title":"Between-Class Learning for Image Classification","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":190,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Core Research for Evolutional Science and Technology; Canadian Institute for Advanced Research","keywords":"Mixing (physics); Generalization; Computer science; Image (mathematics); Convolutional neural network; Artificial intelligence; Contextual image classification; Pattern recognition (psychology); Class (philosophy); Feature (linguistics); Feature extraction; Generalization error; Deep learning; Artificial neural network; Machine learning; Mathematics","score_opus":0.07627495516437881,"score_gpt":0.3249330674113987,"score_spread":0.2486581122470199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963031676","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042274375,0.0016338843,0.9878076,0.0005430959,0.00026514602,0.00010567178,0.00022423924,0.0018902421,0.003302678],"genre_scores_gemma":[0.25339285,0.0015768515,0.73109585,0.0011042538,0.0009395072,0.00051958294,0.002176338,0.000583808,0.008611016],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957877,0.0011228719,0.00017642434,0.0013594612,0.001310125,0.0002433632],"domain_scores_gemma":[0.99552226,0.0017906696,0.00030547022,0.0014339708,0.00076926773,0.00017833321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034502575,0.0021977895,0.0019730313,0.002351098,0.001066831,0.0020919892,0.0050470037,0.002898306,0.00752259],"category_scores_gemma":[0.009507238,0.0005975136,0.001647314,0.0024006595,0.0018364079,0.0057194848,0.0029624838,0.005307697,0.0046099178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023362064,0.00032259885,0.0012064627,0.0003591328,0.0002147511,0.00009418607,0.00008576753,0.055644568,0.0065016816,0.040162418,0.018294645,0.87688005],"study_design_scores_gemma":[0.000026614363,0.00008580176,0.00046357198,0.000040516436,0.000035314064,0.00014677894,0.00003416876,0.89277446,0.008262189,0.0865112,0.011585433,0.000034012483],"about_ca_topic_score_codex":0.0034667235,"about_ca_topic_score_gemma":0.002845068,"teacher_disagreement_score":0.00752259,"about_ca_system_score_codex":0.0017697731,"about_ca_system_score_gemma":0.0011778038,"threshold_uncertainty_score":0.025165558},"labels":[],"label_agreement":null},{"id":"W2963800105","doi":"10.48550/arxiv.1311.4486","title":"Discriminative Density-ratio Estimation","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Discriminative model; Robustness (evolution); Computer science; Covariate; Benchmark (surveying); Artificial intelligence; Decision boundary; Matching (statistics); Density estimation; Regression; Machine learning; Pattern recognition (psychology); Mathematics; Statistics; Estimator; Support vector machine","score_opus":0.08058350045344147,"score_gpt":0.1981074887314474,"score_spread":0.11752398827800593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963800105","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043666875,0.00019397227,0.9943721,0.00007834102,0.000018188926,0.00004336163,0.00004472395,0.0005047796,0.0003778759],"genre_scores_gemma":[0.3505149,0.00058092456,0.64328074,0.00038529205,0.00017007413,0.00026959804,0.0009961254,0.00033774998,0.0034645875],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99744177,0.0008926844,0.000113356225,0.0008344446,0.0005773859,0.00014043956],"domain_scores_gemma":[0.9957504,0.00218783,0.00037281995,0.00091030914,0.0006637148,0.00011494575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033425877,0.0010437996,0.0021209174,0.0014966327,0.0005233398,0.00124247,0.0032778846,0.0015379807,0.0019437623],"category_scores_gemma":[0.01683877,0.0007731775,0.0012187684,0.0013756276,0.0012463536,0.0023471597,0.0020681678,0.002011997,0.0014203902],"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":[0.00029147376,0.0002870953,0.0055513848,0.00043790936,0.0002509471,0.00021153137,0.00029783032,0.2431214,0.014881689,0.04045307,0.00949054,0.6847251],"study_design_scores_gemma":[0.000017691678,0.000045177458,0.0010137444,0.000016753373,0.000022059643,0.00023197716,0.000027333886,0.97017753,0.0050522005,0.021268137,0.0021025946,0.00002479225],"about_ca_topic_score_codex":0.0023717766,"about_ca_topic_score_gemma":0.0021919592,"teacher_disagreement_score":0.0033425877,"about_ca_system_score_codex":0.0009556081,"about_ca_system_score_gemma":0.0010388681,"threshold_uncertainty_score":0.017677486},"labels":[],"label_agreement":null},{"id":"W2964026991","doi":"","title":"Meta-Learning for Semi-Supervised Few-Shot Classification","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":203,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Shot (pellet); Meta learning (computer science); Machine learning; Supervised learning; Pattern recognition (psychology); Task (project management); Engineering; Artificial neural network","score_opus":0.2584993892308796,"score_gpt":0.22784022950027516,"score_spread":0.030659159730604457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964026991","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":[0.01643514,0.0023994609,0.9754862,0.0004139228,0.00016355459,0.00011044221,0.00046741214,0.0038636671,0.0006602694],"genre_scores_gemma":[0.4885459,0.0010256589,0.49437806,0.0007845886,0.0005719283,0.00061804586,0.006345627,0.0011063366,0.006623851],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958546,0.0016432013,0.00026220185,0.0014089074,0.00053379405,0.0002973665],"domain_scores_gemma":[0.9857144,0.009393475,0.00067663175,0.0027988385,0.00094175676,0.00047489657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057791173,0.0023476412,0.0056885793,0.003047593,0.0014952017,0.0023040103,0.0074482104,0.0052528507,0.0034639833],"category_scores_gemma":[0.01663613,0.0015256061,0.0027695296,0.0029710452,0.0018383005,0.0057980777,0.004268745,0.0051613026,0.0022359034],"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":[0.0010133812,0.0011556192,0.002652881,0.00083339826,0.0008816983,0.0002796289,0.00030734757,0.26204756,0.00852214,0.016042145,0.02314808,0.6831162],"study_design_scores_gemma":[0.000019062249,0.000052472726,0.00022129736,0.000020459684,0.000029302895,0.000041994474,0.000024005214,0.9743807,0.0012937185,0.023350278,0.00055003195,0.000016702943],"about_ca_topic_score_codex":0.004949427,"about_ca_topic_score_gemma":0.0070260097,"teacher_disagreement_score":0.0074482104,"about_ca_system_score_codex":0.001880482,"about_ca_system_score_gemma":0.0019275628,"threshold_uncertainty_score":0.030563235},"labels":[],"label_agreement":null},{"id":"W2964032613","doi":"","title":"Unsupervised Learning via Meta-Learning","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Unsupervised learning; Computer science; Machine learning; Meta learning (computer science); Artificial intelligence; Cluster analysis; Embedding; Competitive learning; Feature learning; Task (project management); Conceptual clustering; Construct (python library); Semi-supervised learning; Variety (cybernetics); Multi-task learning; Fuzzy clustering","score_opus":0.10082794456664698,"score_gpt":0.18558686678158073,"score_spread":0.08475892221493375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964032613","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":[0.007341775,0.00027156642,0.99089503,0.00019747649,0.000020429014,0.000047483027,0.000085767475,0.00056215265,0.00057835935],"genre_scores_gemma":[0.40555266,0.00059617293,0.5885459,0.00045387677,0.00016102128,0.0005813111,0.0012171034,0.00036825988,0.002523587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976426,0.0010812872,0.000117113814,0.00074600877,0.00028955392,0.00012336274],"domain_scores_gemma":[0.9935191,0.0038276985,0.00041199444,0.0015348241,0.0005199837,0.00018638663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034217818,0.0017261821,0.0017441913,0.0016491737,0.0007555489,0.0019325303,0.0034131666,0.0018560173,0.001410691],"category_scores_gemma":[0.011446821,0.0010446203,0.0021157549,0.0013793882,0.0021332004,0.0038748498,0.0031007964,0.0034544591,0.00079745456],"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":[0.00015474192,0.00021859456,0.0030131086,0.0003167809,0.00041844242,0.000111161746,0.00023355709,0.7481455,0.0037386762,0.044379856,0.0034452272,0.19582431],"study_design_scores_gemma":[0.000009504504,0.000027669275,0.00013494158,0.000014956949,0.00001559087,0.000020318008,0.000011469388,0.9578334,0.001027904,0.04038291,0.0005109104,0.000010438695],"about_ca_topic_score_codex":0.0015217441,"about_ca_topic_score_gemma":0.0031558778,"teacher_disagreement_score":0.0034217818,"about_ca_system_score_codex":0.0014896543,"about_ca_system_score_gemma":0.0015444285,"threshold_uncertainty_score":0.018096328},"labels":[],"label_agreement":null},{"id":"W2964059481","doi":"10.5555/3327757.3327863","title":"Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding","year":2018,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; University of Ottawa; Université de Montréal","funders":"","keywords":"Computer science; Backtracking; Artificial intelligence; Term (time); State (computer science); Long short term memory; Computation; Machine learning; Recurrent neural network; Artificial neural network; Algorithm","score_opus":0.0282015315563719,"score_gpt":0.2634632335627978,"score_spread":0.23526170200642593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964059481","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":[0.045876075,0.00038403945,0.9476237,0.00028799512,0.00014384692,0.0000759489,0.00012999527,0.003574659,0.0019036749],"genre_scores_gemma":[0.7361471,0.00029062634,0.25305954,0.00045581406,0.00013268576,0.00011307772,0.00041663964,0.00024918688,0.009135366],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974126,0.000042555457,0.000015709913,0.000108749206,0.00005741123,0.000034257624],"domain_scores_gemma":[0.9991146,0.00039404875,0.000106244566,0.00018566457,0.00013003981,0.00006936753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066227594,0.00079560676,0.0007087586,0.00047547772,0.00038336543,0.00067807606,0.001963866,0.0011248469,0.00316267],"category_scores_gemma":[0.0032949662,0.00048638196,0.00046482877,0.0005546258,0.0006346926,0.0016686146,0.0011847527,0.0016071216,0.00084391795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003848993,0.00034611678,0.0024272655,0.0001763185,0.00008505415,0.00035207547,0.000331862,0.24265762,0.02460835,0.011198331,0.008237668,0.7091945],"study_design_scores_gemma":[0.000022349754,0.00004780518,0.00040407816,0.000011979001,0.00001797901,0.00007852047,0.000014664876,0.9838997,0.006128387,0.008127625,0.0012341713,0.000012772527],"about_ca_topic_score_codex":0.005473416,"about_ca_topic_score_gemma":0.007680823,"teacher_disagreement_score":0.005473416,"about_ca_system_score_codex":0.000583648,"about_ca_system_score_gemma":0.0011581188,"threshold_uncertainty_score":0.010883093},"labels":[],"label_agreement":null},{"id":"W2964067969","doi":"","title":"An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks","year":2014,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":499,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Forgetting; Dropout (neural networks); Task (project management); Computer science; Artificial neural network; Artificial intelligence; Activation function; Function (biology); Machine learning; Cognitive psychology; Psychology; Engineering","score_opus":0.053879151793615486,"score_gpt":0.1989776642835671,"score_spread":0.14509851248995162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964067969","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96339923,0.0013448679,0.032722313,0.0006083396,0.00004820835,0.00008854898,0.0002118012,0.00021528559,0.0013614185],"genre_scores_gemma":[0.9961027,0.000110175664,0.003174945,0.00005704598,0.0000136373465,0.000026792175,0.00026014505,0.000019772218,0.00023487091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971251,0.0017990756,0.00018878198,0.0003488712,0.00037231864,0.00016590505],"domain_scores_gemma":[0.8921385,0.085801594,0.005991539,0.009651542,0.004997246,0.0014196687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014880472,0.00058315037,0.0007877328,0.00096196233,0.00059102464,0.00086780527,0.0018464722,0.001377156,0.0014210126],"category_scores_gemma":[0.12924455,0.0003531358,0.00046736724,0.000815582,0.0017111633,0.002471592,0.0012469621,0.0024632765,0.00018748807],"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":[0.0024440615,0.0015881509,0.2268742,0.000896565,0.0007174072,0.0006954211,0.0009950809,0.61781096,0.0035350602,0.021465514,0.008689072,0.11428841],"study_design_scores_gemma":[0.000086930995,0.0007001695,0.040875588,0.00013903859,0.00007828435,0.00035242425,0.00018210338,0.9338351,0.0026472046,0.020091401,0.00095531624,0.00005648255],"about_ca_topic_score_codex":0.0029507254,"about_ca_topic_score_gemma":0.003089244,"teacher_disagreement_score":0.014880472,"about_ca_system_score_codex":0.001094483,"about_ca_system_score_gemma":0.00046240212,"threshold_uncertainty_score":0.07869643},"labels":[],"label_agreement":null},{"id":"W2964121937","doi":"","title":"Bayesian Model-Agnostic Meta-Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":204,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Reinforcement learning; Machine learning; Overfitting; Meta learning (computer science); Robustness (evolution); Bayesian probability; Bayesian inference; Inference; Learning classifier system; Artificial neural network","score_opus":0.04605557115013093,"score_gpt":0.2724186225374055,"score_spread":0.22636305138727458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964121937","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039644465,0.00022183011,0.9946371,0.00014155716,0.000023433493,0.00002714144,0.000049768067,0.00037542733,0.0005592323],"genre_scores_gemma":[0.59757733,0.00048670566,0.3967128,0.00056529714,0.00014013132,0.0004119242,0.0005572208,0.0003784381,0.0031701499],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851173,0.0004946544,0.00008442846,0.00045491237,0.0003260371,0.00012822927],"domain_scores_gemma":[0.99688137,0.0017107895,0.00032595234,0.00050968444,0.00042213927,0.0001501273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030315062,0.0018277612,0.0026897283,0.0014004316,0.0006132465,0.0019822207,0.0044791684,0.0022695928,0.0021700459],"category_scores_gemma":[0.009067141,0.0013716298,0.001991501,0.0010846639,0.0015033889,0.0037028473,0.0026873387,0.0035810561,0.0007938713],"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":[0.000108090346,0.000117244716,0.0008945017,0.00016401247,0.00029488563,0.000093350485,0.00010752323,0.8799258,0.0031242517,0.022061111,0.0016186184,0.09149055],"study_design_scores_gemma":[0.0000062704507,0.000018038525,0.00005603698,0.000009988171,0.000013369946,0.000016998587,0.0000044606436,0.98686403,0.0005264732,0.012207539,0.0002681446,0.000008565313],"about_ca_topic_score_codex":0.0023123492,"about_ca_topic_score_gemma":0.0035730666,"teacher_disagreement_score":0.0044791684,"about_ca_system_score_codex":0.0013894784,"about_ca_system_score_gemma":0.0015837234,"threshold_uncertainty_score":0.016032338},"labels":[],"label_agreement":null},{"id":"W2964271895","doi":"","title":"Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data","year":2018,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Domain (mathematical analysis); Image (mathematics); Artificial intelligence; Segmentation; Image segmentation; Pattern recognition (psychology); Mathematics","score_opus":0.07715892650561458,"score_gpt":0.3242514679354985,"score_spread":0.24709254142988396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964271895","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":[0.11758275,0.0011632775,0.87414396,0.00070435397,0.00015181197,0.00016170196,0.0004784574,0.0018227282,0.003791016],"genre_scores_gemma":[0.7910136,0.00049343007,0.19874229,0.0010226205,0.0001046178,0.00030569636,0.002068745,0.00027042057,0.005978695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992336,0.00027726375,0.000024093562,0.00028000685,0.00009836985,0.00008671304],"domain_scores_gemma":[0.9982791,0.0009167259,0.00011023479,0.00044865758,0.0001710872,0.00007425487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017445899,0.001501825,0.0014661212,0.00075677456,0.0005350013,0.00087961176,0.0019257923,0.0017235006,0.0020468165],"category_scores_gemma":[0.005248987,0.00060242525,0.00096741266,0.00067544135,0.0015338449,0.0033489144,0.0024784647,0.0025217552,0.00065820734],"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":[0.0006679269,0.0003556289,0.0061220154,0.00032759493,0.0002821303,0.00038976106,0.00033300093,0.5978578,0.018968115,0.02968371,0.009894793,0.33511755],"study_design_scores_gemma":[0.000018408702,0.0001003219,0.00047946948,0.00002007391,0.000016968872,0.00012470731,0.000029872182,0.97118384,0.0029975176,0.023779014,0.001230586,0.000019255136],"about_ca_topic_score_codex":0.0019727168,"about_ca_topic_score_gemma":0.0034418816,"teacher_disagreement_score":0.0020468165,"about_ca_system_score_codex":0.000667518,"about_ca_system_score_gemma":0.00082355406,"threshold_uncertainty_score":0.009226322},"labels":[],"label_agreement":null},{"id":"W2964416840","doi":"10.24963/ijcai.2019/246","title":"Augmenting Transfer Learning with Semantic Reasoning","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thales (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Transfer of learning; Exploit; Semantics (computer science); Artificial intelligence; Semantic Web; Quality (philosophy); Transfer (computing); Semantic computing; Knowledge transfer; Machine learning; Natural language processing; Knowledge management; Programming language","score_opus":0.016208337042840377,"score_gpt":0.23511160214740143,"score_spread":0.21890326510456104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964416840","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":[0.017421596,0.00035885192,0.97913396,0.00037122355,0.000090925445,0.00005025941,0.00009368424,0.0011444499,0.0013350691],"genre_scores_gemma":[0.77207977,0.0005177349,0.22200388,0.0005037512,0.00027326692,0.0002050684,0.0007808876,0.00028825604,0.0033473272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780077,0.0008163142,0.00011188667,0.00063173816,0.00047807948,0.00016118826],"domain_scores_gemma":[0.9943112,0.0032233535,0.00029500213,0.0014472791,0.00057120534,0.00015191885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038392034,0.0020080493,0.0017687719,0.0015983293,0.00074255955,0.001758577,0.0029671057,0.002545455,0.003050364],"category_scores_gemma":[0.015452617,0.00060722406,0.001495434,0.0015689589,0.0022317986,0.0069067352,0.006127182,0.0036231035,0.0010916527],"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":[0.00027465128,0.00051812344,0.0017697805,0.00027917625,0.0003038269,0.00020978446,0.00028917502,0.5963289,0.0069148904,0.04209585,0.0037867175,0.34722918],"study_design_scores_gemma":[0.000012680337,0.000045185527,0.00013410085,0.000008889601,0.000015411988,0.00001630655,0.000016234506,0.9347681,0.0018169453,0.06258695,0.0005673208,0.000011837185],"about_ca_topic_score_codex":0.0024063978,"about_ca_topic_score_gemma":0.001883678,"teacher_disagreement_score":0.0038392034,"about_ca_system_score_codex":0.0010143153,"about_ca_system_score_gemma":0.0012299618,"threshold_uncertainty_score":0.020303905},"labels":[],"label_agreement":null},{"id":"W2965555521","doi":"10.1109/cvpr.2019.00969","title":"Variational Prototyping-Encoder: One-Shot Learning With Prototypical Images","year":2019,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Embedding; Computer science; Metric (unit); Artificial intelligence; Encoder; Set (abstract data type); Task (project management); Similarity (geometry); Representation (politics); Class (philosophy); Image (mathematics); Computer vision; Machine learning; Pattern recognition (psychology); Programming language","score_opus":0.027151165955319103,"score_gpt":0.2532330208240338,"score_spread":0.2260818548687147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965555521","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03325377,0.0006868446,0.9625962,0.00025945663,0.000106253465,0.00013307751,0.00022748746,0.0014587932,0.0012781383],"genre_scores_gemma":[0.6160175,0.00047980587,0.37469113,0.00057990605,0.00013466444,0.00037393803,0.0019125763,0.00031370643,0.0054967226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991466,0.00032304463,0.000033070683,0.0002860415,0.00013482837,0.00007632645],"domain_scores_gemma":[0.9977574,0.0013725339,0.000105752435,0.00039611978,0.00024322724,0.0001250468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00181826,0.0012364462,0.0014035433,0.0005772518,0.00039632898,0.00089108344,0.0031452242,0.0018865463,0.0024001834],"category_scores_gemma":[0.006447644,0.00072642,0.0008412195,0.00064768404,0.0010632651,0.002207789,0.0021198783,0.0026376357,0.0006918751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005599084,0.00035687047,0.0024406726,0.0005833537,0.00021226965,0.0003170142,0.00032701963,0.41711774,0.019318715,0.019659402,0.010619487,0.5284876],"study_design_scores_gemma":[0.000012908707,0.00006610118,0.00012817116,0.000009745063,0.0000074205564,0.00005954535,0.000014527137,0.9919492,0.002466436,0.0047404957,0.0005353093,0.000010118351],"about_ca_topic_score_codex":0.003661069,"about_ca_topic_score_gemma":0.00473645,"teacher_disagreement_score":0.003661069,"about_ca_system_score_codex":0.00070103275,"about_ca_system_score_gemma":0.00080566626,"threshold_uncertainty_score":0.009615958},"labels":[],"label_agreement":null},{"id":"W2966697812","doi":"","title":"Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning.","year":2019,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Reproducibility; Metric (unit); Computer science; Stability (learning theory); Artificial intelligence; Mathematics; Machine learning; Statistics; Engineering","score_opus":0.07488014056526597,"score_gpt":0.34860978593865394,"score_spread":0.27372964537338795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966697812","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055833526,0.001908354,0.93818253,0.00048576988,0.00024394291,0.00011359804,0.00040188196,0.0014422283,0.0013881684],"genre_scores_gemma":[0.8191258,0.00047101473,0.17353845,0.00031358306,0.00034975083,0.0003279876,0.0027370187,0.001081726,0.002054696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98141676,0.008559635,0.0013373436,0.0049702865,0.0031533162,0.00056256546],"domain_scores_gemma":[0.82711786,0.12362503,0.006814529,0.024956994,0.015265984,0.0022196067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034734067,0.0012118621,0.002372307,0.0030064918,0.0019971128,0.0034412176,0.0055777715,0.0030754984,0.0017272761],"category_scores_gemma":[0.19347377,0.0009987607,0.0014377745,0.0024400337,0.003942666,0.0067113773,0.004908528,0.004289088,0.0008469405],"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":[0.0032785544,0.0006354977,0.035434958,0.0016750089,0.002043364,0.00053058536,0.0016018051,0.44594553,0.024066512,0.096001156,0.016175477,0.37261155],"study_design_scores_gemma":[0.00003402537,0.00013792436,0.0038545553,0.000049389717,0.00008036629,0.00014017052,0.00009894645,0.9379247,0.006501077,0.050233815,0.00089165935,0.000053374908],"about_ca_topic_score_codex":0.00455617,"about_ca_topic_score_gemma":0.0039027503,"teacher_disagreement_score":0.034734067,"about_ca_system_score_codex":0.0020649622,"about_ca_system_score_gemma":0.00221395,"threshold_uncertainty_score":0.18369353},"labels":[],"label_agreement":null},{"id":"W2967588839","doi":"10.1007/978-3-030-27202-9_41","title":"Strategies for Improving Single-Head Continual Learning Performance","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of Waterloo","funders":"","keywords":"Computer science; Forgetting; Artificial intelligence; Machine learning; Classifier (UML); Obstacle; Binary classification; Incremental learning; Head (geology); Binary number; Support vector machine","score_opus":0.02496720690867954,"score_gpt":0.24944168186995247,"score_spread":0.22447447496127293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967588839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16969618,0.0012735348,0.8035204,0.0003950176,0.00025713502,0.00028419076,0.0001442467,0.0068886746,0.01754059],"genre_scores_gemma":[0.82059765,0.00032723867,0.1688104,0.0001600356,0.0000973863,0.00018885436,0.00019874555,0.00037740427,0.009242192],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995622,0.00008670575,0.000024390376,0.00016585692,0.0000933336,0.000067522575],"domain_scores_gemma":[0.99876815,0.0005478941,0.000086341744,0.00019237239,0.00027157922,0.00013369466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009210821,0.00095792307,0.0007140314,0.0006009346,0.00043682382,0.0012140318,0.0015702485,0.00084540615,0.007861737],"category_scores_gemma":[0.0037535587,0.0002696863,0.00027895576,0.00051851425,0.00042847,0.0017888629,0.0018127295,0.0012648773,0.0025643592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005972681,0.0008090228,0.001149475,0.00014094813,0.000060271817,0.000044160948,0.00015933864,0.02450856,0.048690695,0.00457928,0.0031706998,0.91609025],"study_design_scores_gemma":[0.00017176397,0.0011552982,0.003518298,0.0000584422,0.00018759718,0.00019047475,0.00033556405,0.90419406,0.06261084,0.022101164,0.0053998684,0.00007663525],"about_ca_topic_score_codex":0.0012768717,"about_ca_topic_score_gemma":0.0020649778,"teacher_disagreement_score":0.007861737,"about_ca_system_score_codex":0.00039556724,"about_ca_system_score_gemma":0.000704781,"threshold_uncertainty_score":0.026300132},"labels":[],"label_agreement":null},{"id":"W2967743080","doi":"10.1109/aike.2019.00044","title":"Empirical Comparison between Autoencoders and Traditional Dimensionality Reduction Methods","year":2019,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Dimensionality reduction; Autoencoder; Pattern recognition (psychology); Artificial neural network; Isomap; Random projection; Principal component analysis; Curse of dimensionality","score_opus":0.1519110726912168,"score_gpt":0.4022847091316727,"score_spread":0.2503736364404559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967743080","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":[0.43946287,0.023799479,0.52254033,0.0011786665,0.00032028052,0.00015275167,0.0006500923,0.001308424,0.010587099],"genre_scores_gemma":[0.8505234,0.0038764277,0.14135769,0.00016245444,0.0001897516,0.00012364742,0.0012493986,0.00018904753,0.0023282212],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969248,0.0013227987,0.0002271236,0.00046175512,0.000932313,0.00013119231],"domain_scores_gemma":[0.97943145,0.0156691,0.0005406563,0.0017459576,0.0024327738,0.00018008142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065547875,0.0011440325,0.000906859,0.0019281419,0.0004051257,0.0011161658,0.0009708942,0.0009773388,0.0015233612],"category_scores_gemma":[0.024474725,0.00034888505,0.00056438654,0.0014250564,0.00085362874,0.0022898254,0.0010660767,0.0011224332,0.00045719955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009347076,0.00033712108,0.020060422,0.0007127372,0.0007021833,0.00013189594,0.00031320538,0.36157572,0.003304463,0.01832112,0.0054456033,0.5881609],"study_design_scores_gemma":[0.00003710857,0.00023272577,0.008820736,0.00013413937,0.00007072829,0.000163506,0.00010761823,0.9777564,0.0027639465,0.0077582034,0.0021280975,0.000026705293],"about_ca_topic_score_codex":0.0026187047,"about_ca_topic_score_gemma":0.0028999737,"teacher_disagreement_score":0.0065547875,"about_ca_system_score_codex":0.0010213883,"about_ca_system_score_gemma":0.00076748396,"threshold_uncertainty_score":0.034665406},"labels":[],"label_agreement":null},{"id":"W2969186079","doi":"","title":"Generalized Zero-Shot Learning via Aligned Variational Autoencoders","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"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":"Shot (pellet); Zero (linguistics); Artificial intelligence; Computer science; One shot; Mathematics; Materials science; Engineering","score_opus":0.025382658716132055,"score_gpt":0.25556078062443544,"score_spread":0.2301781219083034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969186079","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":[0.011567979,0.00028420557,0.98719925,0.00011453205,0.000036001977,0.000018443401,0.000047268044,0.00017713054,0.00055521185],"genre_scores_gemma":[0.6484911,0.00066319393,0.33914426,0.0004295047,0.00019186833,0.00021455325,0.0008229202,0.0003498782,0.009692815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993356,0.00021830843,0.000033529006,0.00019777824,0.00013254733,0.00008228151],"domain_scores_gemma":[0.9983543,0.0010078457,0.00011295499,0.00021111043,0.00021902744,0.000094808456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014128259,0.00093430764,0.0019173375,0.00064076966,0.00044721505,0.0011010136,0.0027508002,0.0020629202,0.0022172884],"category_scores_gemma":[0.0044185063,0.0009836508,0.001117913,0.0006984024,0.0017102755,0.002507348,0.0025917569,0.0020130407,0.0005243581],"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":[0.00027643034,0.00016783945,0.00085632043,0.0002543205,0.00026331624,0.00011116894,0.00018741637,0.7761289,0.008358977,0.06654385,0.003290899,0.14356056],"study_design_scores_gemma":[0.0000059431154,0.000017627284,0.0000779971,0.0000046668474,0.000006548322,0.00001393812,0.000006168157,0.984835,0.00047992737,0.014359551,0.00018482866,0.000007820352],"about_ca_topic_score_codex":0.0045845495,"about_ca_topic_score_gemma":0.0057801586,"teacher_disagreement_score":0.0045845495,"about_ca_system_score_codex":0.0007722379,"about_ca_system_score_gemma":0.0013368102,"threshold_uncertainty_score":0.009115756},"labels":[],"label_agreement":null},{"id":"W2969211464","doi":"10.1109/iccv.2019.00040","title":"Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Kwanjeong Educational Foundation; National Science Foundation","keywords":"Forgetting; Leverage (statistics); Overfitting; Computer science; Artificial intelligence; Machine learning; Code (set theory); Labeled data; Distillation; Artificial neural network; Task (project management); Generalization; Mathematics; Engineering","score_opus":0.04958758441825139,"score_gpt":0.281441131426253,"score_spread":0.2318535470080016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969211464","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":[0.11518314,0.0017856448,0.8738088,0.0006807098,0.0002796582,0.00013534522,0.00047861427,0.0061256257,0.0015223884],"genre_scores_gemma":[0.7895901,0.0006715273,0.20124257,0.0008915566,0.00029667185,0.0002059622,0.0024932877,0.00045767767,0.004150586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981797,0.00043537238,0.00012656882,0.0006174046,0.00047658733,0.00016437998],"domain_scores_gemma":[0.98852056,0.004830069,0.00075605756,0.00420983,0.0012200098,0.000463536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003849429,0.0019280527,0.0022094923,0.0011881386,0.0009215952,0.001227967,0.003906534,0.0017980618,0.0012652418],"category_scores_gemma":[0.0169654,0.0008711723,0.0009577478,0.0011385179,0.0016677022,0.0046862243,0.0031561656,0.0035726014,0.0009017908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097641494,0.000715943,0.01564302,0.00064678147,0.0003295048,0.00096816756,0.000707343,0.29046512,0.02797708,0.009020937,0.020335238,0.63221455],"study_design_scores_gemma":[0.00004314849,0.00014978444,0.0012015122,0.00004084048,0.000045041474,0.00030983592,0.00005715799,0.96634644,0.014808156,0.014739638,0.0022200712,0.000038390754],"about_ca_topic_score_codex":0.004549069,"about_ca_topic_score_gemma":0.008915912,"teacher_disagreement_score":0.004549069,"about_ca_system_score_codex":0.00085488346,"about_ca_system_score_gemma":0.0016982477,"threshold_uncertainty_score":0.020357966},"labels":[],"label_agreement":null},{"id":"W2971133559","doi":"","title":"Universal Boosting Variational Inference","year":2019,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"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 British Columbia","funders":"","keywords":"Mathematics; Mathematical optimization; Boosting (machine learning); Hellinger distance; Degeneracy (biology); Applied mathematics; Algorithm; Computer science; Artificial intelligence","score_opus":0.016146962566080145,"score_gpt":0.24058967803656828,"score_spread":0.22444271547048814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971133559","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016688616,0.000111270005,0.99677247,0.000086308784,0.000026075515,0.000027329017,0.000040795636,0.00036614778,0.000900759],"genre_scores_gemma":[0.27198526,0.00042656742,0.71966594,0.0005534626,0.00020210954,0.00028615724,0.0006894519,0.00061044196,0.005580735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983463,0.0007099282,0.00006533255,0.00032536377,0.00040768407,0.00014526016],"domain_scores_gemma":[0.9972905,0.0014241118,0.00015248162,0.000557465,0.0004171501,0.0001582588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039180974,0.001187501,0.0021261992,0.0010149784,0.00083438394,0.0015268705,0.0030274664,0.0015481631,0.0043638134],"category_scores_gemma":[0.011304002,0.0010422842,0.0014256713,0.0010881864,0.0013996869,0.0018615385,0.002883944,0.0026021602,0.0014294268],"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":[0.00009530057,0.00006783153,0.0013838615,0.00019375718,0.00019870681,0.000094574636,0.00012443902,0.68163496,0.0027300427,0.1703802,0.007743443,0.13535288],"study_design_scores_gemma":[0.0000049794467,0.000005787431,0.000051333755,0.0000071588834,0.000006781509,0.000013736178,0.0000032185558,0.9659372,0.00039039666,0.03256197,0.0010133263,0.0000040983173],"about_ca_topic_score_codex":0.0038329477,"about_ca_topic_score_gemma":0.004957164,"teacher_disagreement_score":0.0043638134,"about_ca_system_score_codex":0.0015785656,"about_ca_system_score_gemma":0.0019854552,"threshold_uncertainty_score":0.020721138},"labels":[],"label_agreement":null},{"id":"W2971176100","doi":"","title":"Online Continual Learning with Maximal Interfered Retrieval","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":206,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Forgetting; Generative grammar; Machine learning; Generative model; Matching (statistics); Artificial intelligence; Sampling (signal processing)","score_opus":0.03820403800000038,"score_gpt":0.17070374967230337,"score_spread":0.13249971167230298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971176100","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":[0.04605657,0.001170001,0.94678223,0.0004692251,0.000096487856,0.00011160522,0.00013833957,0.0028718957,0.0023036005],"genre_scores_gemma":[0.7877246,0.00037609454,0.20383523,0.00076249987,0.00023197573,0.00026894416,0.00061499554,0.00038884542,0.005796787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866056,0.00039429023,0.000087278495,0.00037894933,0.00031621108,0.00016268274],"domain_scores_gemma":[0.9961429,0.0019299877,0.00025754014,0.0010686669,0.00036301804,0.00023782524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002617269,0.0011860975,0.0019981605,0.0006736359,0.00066347665,0.0013398615,0.004019041,0.0019106478,0.003215685],"category_scores_gemma":[0.011370336,0.0007507814,0.0009681955,0.0008240125,0.0018443615,0.003787972,0.00357169,0.0024824208,0.0014058535],"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":[0.0015112326,0.0006120484,0.0029777624,0.00043202713,0.00026722378,0.00037491912,0.0005331756,0.48257035,0.012997004,0.027236724,0.009218218,0.46126932],"study_design_scores_gemma":[0.00006410325,0.00014291106,0.00018181003,0.000016296031,0.000024584224,0.000094531526,0.000029813596,0.9760618,0.0027980388,0.019656252,0.00090725865,0.000022533863],"about_ca_topic_score_codex":0.0029421886,"about_ca_topic_score_gemma":0.0035579011,"teacher_disagreement_score":0.004019041,"about_ca_system_score_codex":0.00086912146,"about_ca_system_score_gemma":0.0016548542,"threshold_uncertainty_score":0.013841629},"labels":[],"label_agreement":null},{"id":"W2972873299","doi":"10.48550/arxiv.1909.05352","title":"Domain Aggregation Networks for Multi-Source Domain Adaptation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"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":"Domain adaptation; Domain (mathematical analysis); Adaptation (eye); Computer science; Artificial intelligence; Psychology; Mathematics; Neuroscience","score_opus":0.09661974135682251,"score_gpt":0.21061121945581573,"score_spread":0.11399147809899322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972873299","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022081824,0.0006750294,0.97436136,0.00025661106,0.00006189059,0.000059270424,0.00010905399,0.0009842417,0.0014107561],"genre_scores_gemma":[0.67626417,0.0006570322,0.31570175,0.00065609487,0.0001597119,0.00032467514,0.0012061051,0.00027882447,0.0047517316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992131,0.0002848725,0.00003213106,0.0002861184,0.00011921015,0.00006447559],"domain_scores_gemma":[0.9980964,0.0010617153,0.0001193804,0.0003755572,0.00026110435,0.00008597729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021987045,0.0012160955,0.0010744203,0.000967549,0.00059444085,0.0008469413,0.001846974,0.0014248773,0.0016450869],"category_scores_gemma":[0.005855251,0.00055128406,0.0008238837,0.0010503503,0.00094605837,0.0026738439,0.0022942275,0.0029300442,0.0007557869],"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":[0.00024620624,0.00028179403,0.0023989526,0.00015699839,0.00018695938,0.00020978614,0.0002468681,0.6412521,0.008735102,0.018338066,0.0068899943,0.32105714],"study_design_scores_gemma":[0.0000049046503,0.00001717235,0.00016137214,0.000006275411,0.0000078530675,0.00002608237,0.000016113048,0.98769,0.0011555514,0.01013691,0.0007715977,0.0000061525725],"about_ca_topic_score_codex":0.003320586,"about_ca_topic_score_gemma":0.004109004,"teacher_disagreement_score":0.003320586,"about_ca_system_score_codex":0.0010715786,"about_ca_system_score_gemma":0.00071507704,"threshold_uncertainty_score":0.011628032},"labels":[],"label_agreement":null},{"id":"W2973077827","doi":"10.1109/tnnls.2019.2935608","title":"Domain Adaptation With Neural Embedding Matching","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":180,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Postdoctoral Program for Innovative Talents; Canada Research Chairs; Natural Science Foundation of Hubei Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Embedding; Computer science; Artificial intelligence; Matching (statistics); Representation (politics); Artificial neural network; Domain (mathematical analysis); Domain adaptation; Feature learning; Generalization; Exploit; Benchmark (surveying); Machine learning; Theoretical computer science; Pattern recognition (psychology); Mathematics; Classifier (UML)","score_opus":0.011616049659989035,"score_gpt":0.22261856925905973,"score_spread":0.2110025195990707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973077827","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":[0.013650205,0.00030786626,0.98418194,0.00009730237,0.000046760197,0.00005692955,0.00006839551,0.0007064685,0.0008841322],"genre_scores_gemma":[0.5837804,0.00055713445,0.40900323,0.0003929733,0.00011820938,0.00029999335,0.0011370583,0.00021214833,0.004498815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889,0.0002997234,0.00006310719,0.00047547868,0.00020142982,0.000070262715],"domain_scores_gemma":[0.9989416,0.00033808986,0.000111822796,0.00034235467,0.00021824609,0.00004791904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012090874,0.000989666,0.0011987522,0.0011830876,0.0003818006,0.0007444258,0.0018737823,0.001281042,0.0013490848],"category_scores_gemma":[0.0039630453,0.00034683806,0.0011859485,0.0014657297,0.00072309095,0.0026315127,0.0020016741,0.0018675714,0.00074965716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016445652,0.00031073298,0.0021312407,0.00015362723,0.0001847894,0.00017309282,0.00018148936,0.39030686,0.012922358,0.013621572,0.006030254,0.5738196],"study_design_scores_gemma":[0.000008790381,0.000038030856,0.0002829548,0.000006126658,0.000013377513,0.00006324037,0.000022172628,0.9864964,0.002682938,0.009246546,0.0011270149,0.0000122827705],"about_ca_topic_score_codex":0.0021927592,"about_ca_topic_score_gemma":0.0017776071,"teacher_disagreement_score":0.0021927592,"about_ca_system_score_codex":0.0006294622,"about_ca_system_score_gemma":0.0006737531,"threshold_uncertainty_score":0.0063943267},"labels":[],"label_agreement":null},{"id":"W2973285246","doi":"10.48550/arxiv.1909.08203","title":"Dual Adversarial Co-Learning for Multi-Domain Text Classification","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Carleton University","funders":"","keywords":"Adversarial system; Computer science; Dual (grammatical number); Generalization; Artificial intelligence; Domain (mathematical analysis); Machine learning; Feature (linguistics); Feature extraction; Pattern recognition (psychology); Labeled data; Mathematics","score_opus":0.14913194538944088,"score_gpt":0.24044271242964546,"score_spread":0.09131076704020458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973285246","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010662976,0.00034231506,0.9872327,0.00030210838,0.000055244684,0.00003584266,0.000062169864,0.00047280354,0.0008338994],"genre_scores_gemma":[0.7505979,0.00039234347,0.23917875,0.00064260006,0.00030872715,0.0002654406,0.00093962124,0.00020349941,0.007471198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980286,0.00076757127,0.0000712307,0.0005672758,0.00038551158,0.00017980862],"domain_scores_gemma":[0.9956995,0.0025701914,0.00039053903,0.00068027346,0.00044924626,0.00021025148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029276756,0.0013808854,0.0015672819,0.0010624465,0.00062297523,0.001124036,0.002422403,0.0021867652,0.0021640307],"category_scores_gemma":[0.0061656223,0.00049649266,0.00094744307,0.0011565181,0.0016362508,0.0028082712,0.0027805562,0.0035656486,0.0012250299],"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":[0.00046765016,0.00036425598,0.0020506477,0.00016940122,0.00017279574,0.00021662677,0.00013945106,0.762121,0.0069962027,0.028368609,0.0065926737,0.19234075],"study_design_scores_gemma":[0.000004446988,0.000020018231,0.000059957212,0.000002819274,0.0000037318478,0.000017008755,0.00000444252,0.99292094,0.00079399423,0.0058752475,0.0002930178,0.000004392229],"about_ca_topic_score_codex":0.0013123975,"about_ca_topic_score_gemma":0.0010168412,"teacher_disagreement_score":0.0029276756,"about_ca_system_score_codex":0.0009961548,"about_ca_system_score_gemma":0.0007952251,"threshold_uncertainty_score":0.015483201},"labels":[],"label_agreement":null},{"id":"W2975674317","doi":"10.48550/arxiv.1909.11722","title":"A Theoretical Analysis of the Number of Shots in Few-Shot Learning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Samsung","keywords":"Shot (pellet); Computer science; Hyperparameter; Task (project management); Set (abstract data type); Meta learning (computer science); Artificial intelligence; Machine learning; One shot; Simple (philosophy); Test (biology); Pattern recognition (psychology); Engineering","score_opus":0.05509535242507704,"score_gpt":0.21291863349188556,"score_spread":0.1578232810668085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975674317","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012495983,0.0019862712,0.9813137,0.0010588609,0.0001068809,0.00010366051,0.00018106701,0.00041176658,0.002341797],"genre_scores_gemma":[0.549057,0.005222942,0.4310468,0.001685541,0.0012312056,0.0016285738,0.0013475522,0.00084667397,0.00793363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99298906,0.0020135562,0.00044901142,0.0023821224,0.0016710361,0.00049517065],"domain_scores_gemma":[0.9429802,0.047231868,0.00204304,0.0041599246,0.0025597299,0.0010252035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013659335,0.0024165576,0.0035013268,0.0031889083,0.0022753756,0.004330007,0.007323336,0.0051043276,0.0053142817],"category_scores_gemma":[0.06333573,0.0019935046,0.001968642,0.002740573,0.006642079,0.013863179,0.005090874,0.007391593,0.0011967255],"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":[0.00043450974,0.0003791613,0.0055285143,0.0013531053,0.00037166988,0.00039235,0.0008178165,0.51757795,0.004238487,0.30582112,0.005955422,0.15712997],"study_design_scores_gemma":[0.0000172261,0.00010692171,0.0006839796,0.00012178174,0.00004813391,0.00016128291,0.000056900015,0.80743015,0.0015366222,0.18816149,0.0016339554,0.000041660238],"about_ca_topic_score_codex":0.0037101177,"about_ca_topic_score_gemma":0.00260157,"teacher_disagreement_score":0.013659335,"about_ca_system_score_codex":0.0044735163,"about_ca_system_score_gemma":0.0020650644,"threshold_uncertainty_score":0.07223827},"labels":[],"label_agreement":null},{"id":"W2977479414","doi":"10.1109/ijcnn.2019.8852426","title":"Preempting Catastrophic Forgetting in Continual Learning Models by Anticipatory Regularization","year":2019,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Forgetting; Computer science; Regularization (linguistics); Artificial intelligence; Discriminative model; Machine learning; Task (project management); Anticipation (artificial intelligence); Artificial neural network; Cognitive psychology; Engineering","score_opus":0.01633576027562482,"score_gpt":0.2232339221020295,"score_spread":0.2068981618264047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977479414","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":[0.09625175,0.00043851513,0.89837587,0.0008819949,0.000085776024,0.00005815082,0.000040826282,0.0008375807,0.0030295684],"genre_scores_gemma":[0.9309462,0.0003183366,0.062249094,0.0003462468,0.00009031104,0.00014376621,0.00009244875,0.00012755189,0.0056860177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950886,0.00016136753,0.000029356679,0.0001209649,0.00009329088,0.0000862288],"domain_scores_gemma":[0.997227,0.0014219368,0.0003671944,0.00051464763,0.0002899784,0.0001792738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024157704,0.0010862951,0.0009600295,0.00035701145,0.00035511295,0.0010047633,0.0023278855,0.0015062174,0.0014582284],"category_scores_gemma":[0.0068731937,0.00059077994,0.00075204956,0.0003420731,0.0019585663,0.002273956,0.0015561441,0.0032414368,0.0003566966],"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":[0.00014460586,0.00015330862,0.0014496136,0.00007142607,0.00007528881,0.000111531845,0.0001588515,0.94128895,0.0035662851,0.019952951,0.0011196982,0.031907424],"study_design_scores_gemma":[0.0000080168375,0.000023863407,0.00007217274,0.0000046308337,0.000005826203,0.000010032493,0.000003868096,0.9918711,0.0003655537,0.0074766674,0.00015353214,0.0000047073263],"about_ca_topic_score_codex":0.0036112815,"about_ca_topic_score_gemma":0.005090836,"teacher_disagreement_score":0.0036112815,"about_ca_system_score_codex":0.00084092986,"about_ca_system_score_gemma":0.0010651347,"threshold_uncertainty_score":0.012776017},"labels":[],"label_agreement":null},{"id":"W2979740303","doi":"10.1109/embc.2019.8856726","title":"Alzheimer's Disease Brain Network Classification Using Improved Transfer Feature Learning with Joint Distribution Adaptation","year":2019,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research","keywords":"Classifier (UML); Transfer of learning; Computer science; Artificial intelligence; Domain adaptation; Pattern recognition (psychology); Joint probability distribution; Feature vector; Machine learning; Population; Mathematics; Statistics","score_opus":0.03467072036595287,"score_gpt":0.23897754422329676,"score_spread":0.2043068238573439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979740303","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":[0.18724182,0.0006192734,0.8086537,0.0002547281,0.00007624911,0.00010218186,0.00015874684,0.0017416077,0.0011516601],"genre_scores_gemma":[0.90564376,0.00018598414,0.09155674,0.0001686713,0.00006348342,0.00012061799,0.0006042647,0.00005572,0.0016007795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948394,0.00014112223,0.000030036572,0.00017592029,0.00010024506,0.0000687563],"domain_scores_gemma":[0.9993224,0.00025040528,0.000060315335,0.00013249516,0.0001926674,0.00004182925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011976748,0.00064848695,0.0009933056,0.0010713872,0.00039658503,0.000517179,0.00106726,0.0008377985,0.00065851194],"category_scores_gemma":[0.0023902284,0.0001861961,0.0009627467,0.00083532906,0.0004522747,0.0013052678,0.0009644667,0.0010530986,0.00028557348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034188383,0.00056641747,0.008785496,0.00006533482,0.00023427664,0.00025534208,0.00013601435,0.21469495,0.016509954,0.0019072767,0.004216696,0.75228626],"study_design_scores_gemma":[0.000008647213,0.000052879546,0.0012564576,0.0000026598523,0.000015932515,0.000054400956,0.000012750985,0.9943657,0.0022279168,0.0017374819,0.00025511265,0.000010017128],"about_ca_topic_score_codex":0.0034661368,"about_ca_topic_score_gemma":0.0030553637,"teacher_disagreement_score":0.0034661368,"about_ca_system_score_codex":0.0005999603,"about_ca_system_score_gemma":0.0005237188,"threshold_uncertainty_score":0.0068919063},"labels":[],"label_agreement":null},{"id":"W2979888181","doi":"10.48550/arxiv.1910.04153","title":"High Mutual Information in Representation Learning with Symmetric Variational Inference","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mutual information; Parameterized complexity; Inference; Computer science; Conditional mutual information; Kullback–Leibler divergence; Encoder; Entropy (arrow of time); Information theory; Bounded function; Representation (politics); Joint probability distribution; Feature learning; Decoding methods; Boltzmann machine; Theoretical computer science; Artificial intelligence; Algorithm; Mathematics; Deep learning","score_opus":0.05126136843675917,"score_gpt":0.19338096346464104,"score_spread":0.14211959502788185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979888181","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":[0.0055203363,0.00036046407,0.9918561,0.00041702538,0.000024399,0.000022739201,0.00007470888,0.00017854224,0.0015456259],"genre_scores_gemma":[0.52501655,0.0013383266,0.46398106,0.00072290073,0.00033089626,0.00038602256,0.00073766604,0.00047920487,0.007007333],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972773,0.0014960534,0.000102108555,0.00046816765,0.00051605614,0.00014030824],"domain_scores_gemma":[0.9939482,0.004439792,0.000365009,0.00077183446,0.0003150523,0.00016005711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00410999,0.0011347429,0.0013783252,0.0010016499,0.00074402295,0.0018342485,0.0023949938,0.0018205877,0.0023661938],"category_scores_gemma":[0.016527317,0.0009302802,0.001108586,0.001163562,0.0030769755,0.0045363167,0.004296021,0.0038044255,0.0006834607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011616661,0.00008097032,0.0008935743,0.00021633408,0.00013714806,0.00017109764,0.00028156498,0.389711,0.0028145437,0.5404033,0.0035190992,0.06165522],"study_design_scores_gemma":[0.000008177902,0.000021771475,0.00008322437,0.000013777164,0.0000071706777,0.000025981934,0.000009621983,0.7054249,0.00063833955,0.29289028,0.0008654973,0.0000111837735],"about_ca_topic_score_codex":0.002039771,"about_ca_topic_score_gemma":0.0021191563,"teacher_disagreement_score":0.00410999,"about_ca_system_score_codex":0.0016549557,"about_ca_system_score_gemma":0.0014896245,"threshold_uncertainty_score":0.021735966},"labels":[],"label_agreement":null},{"id":"W2980081640","doi":"10.1007/978-3-030-32239-7_17","title":"Task Adaptive Metric Space for Medium-Shot Medical Image Classification","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Metric (unit); Artificial intelligence; Baseline (sea); Task (project management); Machine learning; Shot (pellet); Domain (mathematical analysis); Meta learning (computer science); Image (mathematics); Space (punctuation); Data mining; Pattern recognition (psychology); Mathematics","score_opus":0.03696009152504456,"score_gpt":0.284568985770106,"score_spread":0.2476088942450614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980081640","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":[0.0062807635,0.0008507773,0.9913078,0.0001232491,0.000051980718,0.000038824463,0.00016750499,0.00063490384,0.0005441865],"genre_scores_gemma":[0.31742948,0.0020089475,0.66742676,0.0003310489,0.00027113385,0.0003322969,0.0027229697,0.00044965305,0.009027763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993327,0.00022657143,0.000040436797,0.00014632987,0.00019200188,0.000061877174],"domain_scores_gemma":[0.99915135,0.00035893268,0.00004807889,0.00015175853,0.00022955757,0.00006033159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011032037,0.00081679993,0.0016075271,0.0009388662,0.00032659018,0.00087795797,0.0019004805,0.0010803718,0.0023184954],"category_scores_gemma":[0.002706694,0.00031025178,0.0008971817,0.0013619609,0.0004786781,0.0011602803,0.0016441325,0.0016016603,0.0011890461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001857119,0.0001547769,0.00044321513,0.00020635297,0.00010377879,0.000053157193,0.00007317448,0.10582,0.014746229,0.008860808,0.010887833,0.85846496],"study_design_scores_gemma":[0.0000048650313,0.000075475175,0.0004100025,0.000011619541,0.000011262645,0.00008267103,0.000021193442,0.9845389,0.0030976194,0.009783968,0.0019484631,0.000014016856],"about_ca_topic_score_codex":0.0039400775,"about_ca_topic_score_gemma":0.0035296553,"teacher_disagreement_score":0.0039400775,"about_ca_system_score_codex":0.0006447241,"about_ca_system_score_gemma":0.000838708,"threshold_uncertainty_score":0.007834256},"labels":[],"label_agreement":null},{"id":"W2981720610","doi":"10.1109/iccv.2019.00149","title":"Moment Matching for Multi-Source Domain Adaptation","year":2019,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1574,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"","keywords":"Computer science; Matching (statistics); Source code; Domain (mathematical analysis); Benchmarking; Adaptation (eye); Domain adaptation; Moment (physics); Transfer of learning; Feature (linguistics); Artificial intelligence; Code (set theory); Data mining; Machine learning; Pattern recognition (psychology); Set (abstract data type); Mathematics; Statistics","score_opus":0.03986913152636868,"score_gpt":0.27700985680883244,"score_spread":0.23714072528246377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981720610","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":[0.014728798,0.0005724727,0.9791576,0.00021286041,0.000104897284,0.00006944219,0.0007581033,0.0027197276,0.0016760702],"genre_scores_gemma":[0.45978278,0.0007163972,0.52503926,0.000537334,0.00018201473,0.00032283826,0.0072626,0.0006420711,0.005514727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989672,0.00027498996,0.00004855241,0.0004345589,0.00018996769,0.000084813495],"domain_scores_gemma":[0.9979845,0.00072709774,0.00014621695,0.00076818804,0.0002871629,0.00008670704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001533574,0.00095150387,0.001052577,0.0014807872,0.0005382074,0.0009611759,0.0020631352,0.0014234912,0.0031023931],"category_scores_gemma":[0.006538243,0.00046103593,0.001112282,0.0016817662,0.00091864466,0.0026390685,0.002306469,0.00245775,0.0018174674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005169886,0.00032819953,0.0038943193,0.0003869168,0.00025102522,0.00032496697,0.00024800916,0.32786316,0.032718726,0.026825393,0.024825241,0.58181703],"study_design_scores_gemma":[0.000017997107,0.0000368899,0.00095892214,0.000017496175,0.000017967577,0.00015733858,0.00003651896,0.95694536,0.008776713,0.02783212,0.0051726815,0.000029956089],"about_ca_topic_score_codex":0.0029035998,"about_ca_topic_score_gemma":0.0038481716,"teacher_disagreement_score":0.0031023931,"about_ca_system_score_codex":0.00086668396,"about_ca_system_score_gemma":0.00082411454,"threshold_uncertainty_score":0.01037854},"labels":[],"label_agreement":null},{"id":"W2983826605","doi":"10.1162/neco_a_01246","title":"Toward Training Recurrent Neural Networks for Lifelong Learning","year":2019,"lang":"en","type":"article","venue":"Neural Computation","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Forgetting; Computer science; Lifelong learning; Artificial intelligence; Benchmark (surveying); Machine learning; Context (archaeology); Artificial neural network; Recurrent neural network; Psychology; Cognitive psychology","score_opus":0.06471803080604753,"score_gpt":0.29083965293383823,"score_spread":0.2261216221277907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983826605","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":[0.12159368,0.0009187493,0.8725485,0.0005183538,0.000066295965,0.00006995788,0.00012555615,0.001530578,0.0026282452],"genre_scores_gemma":[0.87013495,0.00022351695,0.126668,0.00019104456,0.000033027598,0.00013060601,0.00026808734,0.00010745324,0.0022432865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964917,0.00010951368,0.000020523194,0.00010148664,0.000076342636,0.000042991825],"domain_scores_gemma":[0.99850285,0.00072594587,0.00015159135,0.00023986916,0.00028783502,0.000091838934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014875266,0.00074397406,0.00068468467,0.0004196167,0.00026247845,0.0007327334,0.0015778554,0.0010529537,0.0013502088],"category_scores_gemma":[0.0050713248,0.00034846985,0.0003706759,0.000352782,0.0005901044,0.0019566426,0.0012626144,0.0016624066,0.00039843103],"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":[0.000090041736,0.00012089846,0.0013934006,0.00009164636,0.000054596618,0.0000669614,0.00009957734,0.8879589,0.0041922885,0.0077796956,0.0015531474,0.09659889],"study_design_scores_gemma":[0.000002093944,0.000020029422,0.000051873554,0.0000036104038,0.0000020223965,0.0000050265467,0.0000036622475,0.99665415,0.00058609765,0.002551462,0.00011759177,0.0000023355067],"about_ca_topic_score_codex":0.003385139,"about_ca_topic_score_gemma":0.005605184,"teacher_disagreement_score":0.003385139,"about_ca_system_score_codex":0.0008119347,"about_ca_system_score_gemma":0.0008095587,"threshold_uncertainty_score":0.007866859},"labels":[],"label_agreement":null},{"id":"W2984353870","doi":"10.1007/s10994-019-05855-6","title":"A survey on semi-supervised learning","year":2019,"lang":"en","type":"article","venue":"Machine Learning","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2562,"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 British Columbia","funders":"Universiteit Leiden","keywords":"Artificial intelligence; Machine learning; Computer science; Supervised learning; Unsupervised learning; Scope (computer science); Semi-supervised learning; Artificial neural network; Field (mathematics); Data science; Mathematics","score_opus":0.02000205411217099,"score_gpt":0.24997513849481656,"score_spread":0.22997308438264558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984353870","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003389189,0.24135578,0.7350987,0.00357839,0.0011458722,0.0002254745,0.0005357228,0.00085465674,0.013816174],"genre_scores_gemma":[0.12499387,0.3760209,0.47741178,0.0027662492,0.007048465,0.00086456,0.003907817,0.00073715934,0.00624919],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9876539,0.00520911,0.0012194761,0.0017254852,0.003974521,0.0002175346],"domain_scores_gemma":[0.9730954,0.019818002,0.00083993044,0.0017439931,0.0042310767,0.00027150582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008611789,0.0014184114,0.0024388433,0.0050126165,0.00093404995,0.0040000654,0.0035549437,0.0022569837,0.0033743859],"category_scores_gemma":[0.023191372,0.001001386,0.0018570563,0.008960475,0.0024517493,0.0066124853,0.0026464008,0.0028970798,0.0030519278],"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":[0.00009379097,0.00012647304,0.0020556257,0.006161568,0.0002298829,0.0001654902,0.00053782045,0.024793012,0.0009108266,0.083235614,0.032111928,0.84957796],"study_design_scores_gemma":[0.000050895283,0.00027009603,0.0031352,0.0050406954,0.00015215798,0.0014996888,0.00057332386,0.22540651,0.0039967033,0.3391644,0.4204899,0.00022039341],"about_ca_topic_score_codex":0.001470266,"about_ca_topic_score_gemma":0.0008576726,"teacher_disagreement_score":0.008611789,"about_ca_system_score_codex":0.0014928799,"about_ca_system_score_gemma":0.0025260274,"threshold_uncertainty_score":0.04554403},"labels":[],"label_agreement":null},{"id":"W2985780925","doi":"10.48550/arxiv.1911.07086","title":"Signed Input Regularization","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Regularization (linguistics); Robustness (evolution); Computer science; Artificial intelligence; Parameterized complexity; Inference; Pattern recognition (psychology); Algorithm; Mathematics; Machine learning","score_opus":0.060461186893986066,"score_gpt":0.17873327303073847,"score_spread":0.1182720861367524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985780925","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":[0.008377095,0.00026823656,0.9858759,0.0002982469,0.000096326294,0.000046129047,0.00021280602,0.0017373452,0.0030879304],"genre_scores_gemma":[0.55537283,0.00075198093,0.42021486,0.0011622388,0.00021325906,0.00037551371,0.0022567853,0.0011046327,0.01854797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991322,0.00024659917,0.00004312617,0.00024272138,0.0002514658,0.000083908584],"domain_scores_gemma":[0.99859816,0.00049521786,0.00011085362,0.00042128516,0.00030857322,0.00006591711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013476631,0.0017030371,0.0010889387,0.0007105586,0.0004959901,0.0014967538,0.0022919266,0.0024298711,0.007022175],"category_scores_gemma":[0.006194076,0.00045469264,0.0010353058,0.0008094808,0.001129592,0.0022710671,0.0021348393,0.0028400146,0.0020592168],"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":[0.00023813517,0.00015102862,0.0011916773,0.0003090554,0.0001276703,0.00015689763,0.00010132913,0.51243585,0.016389044,0.069049016,0.01735154,0.38249874],"study_design_scores_gemma":[0.000008893971,0.000025214067,0.00014558916,0.000015651933,0.0000083581335,0.000043019816,0.000009112712,0.9732055,0.0039088456,0.020270148,0.0023486267,0.000011006999],"about_ca_topic_score_codex":0.0025862935,"about_ca_topic_score_gemma":0.0037039272,"teacher_disagreement_score":0.007022175,"about_ca_system_score_codex":0.0010729189,"about_ca_system_score_gemma":0.0013786192,"threshold_uncertainty_score":0.023491502},"labels":[],"label_agreement":null},{"id":"W2987112586","doi":"10.1101/845537","title":"Unsupervised domain adaptation for the automated segmentation of neuroanatomy in MRI: a deep learning approach","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Segmentation; Artificial intelligence; Convolutional neural network; Generalizability theory; Domain adaptation; Domain (mathematical analysis); Pattern recognition (psychology); Neuroanatomy; Context (archaeology); Machine learning; Deep learning; Artificial neural network; Classifier (UML); Neuroscience","score_opus":0.017955826154096733,"score_gpt":0.23175975771524143,"score_spread":0.2138039315611447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987112586","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":[0.03316799,0.00023750182,0.9644733,0.00013884094,0.0000246197,0.000033939727,0.00007252478,0.0012935317,0.00055772776],"genre_scores_gemma":[0.5038878,0.0003257617,0.491571,0.00024633136,0.00005917079,0.00014467146,0.0006420476,0.00033493905,0.0027883267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969053,0.00009943921,0.00001340136,0.00010694002,0.000053606105,0.00003615773],"domain_scores_gemma":[0.9992563,0.00031067763,0.000077534714,0.00017404748,0.00013585838,0.000045421664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011035716,0.00074993406,0.0006211222,0.0007312929,0.00035050575,0.000602194,0.0010296421,0.0012237797,0.0008308962],"category_scores_gemma":[0.0020616846,0.00040661747,0.0007434414,0.0006206113,0.0007168466,0.00088848535,0.0013251593,0.0015502998,0.0004922579],"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":[0.00016765083,0.000158616,0.0014144385,0.000100009056,0.000113862334,0.00011992278,0.00013053883,0.6905383,0.04316655,0.005918674,0.0029076997,0.25526372],"study_design_scores_gemma":[0.0000029932285,0.000012089175,0.00019328667,0.000004344585,0.0000035507653,0.000018685016,0.000006276281,0.99235064,0.0043736747,0.0026634592,0.0003661998,0.0000047127205],"about_ca_topic_score_codex":0.0030474756,"about_ca_topic_score_gemma":0.004261973,"teacher_disagreement_score":0.0030474756,"about_ca_system_score_codex":0.0006823546,"about_ca_system_score_gemma":0.0008884146,"threshold_uncertainty_score":0.006059468},"labels":[],"label_agreement":null},{"id":"W2991178846","doi":"10.1007/s41095-023-0362-4","title":"Class-conditional domain adaptation for semantic segmentation","year":2024,"lang":"en","type":"article","venue":"Computational Visual Media","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"York University","keywords":"Segmentation; Adaptation (eye); Class (philosophy); Computer science; Domain adaptation; Artificial intelligence; Domain (mathematical analysis); Natural language processing; Mathematics; Psychology","score_opus":0.03321349796197684,"score_gpt":0.3203442916150301,"score_spread":0.2871307936530533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991178846","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":[0.016898595,0.0004301888,0.9772424,0.0001403693,0.00008031042,0.000062910556,0.00020343017,0.0028009596,0.002140865],"genre_scores_gemma":[0.5081956,0.0007233637,0.48007402,0.00066738727,0.00019506853,0.00023805664,0.0029639362,0.0008222057,0.006120311],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925596,0.00016639539,0.000027526508,0.00031142533,0.00016677921,0.00007194281],"domain_scores_gemma":[0.9987393,0.00047354944,0.00009497056,0.00037109002,0.00023589874,0.00008527089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012588504,0.0009866524,0.00087769947,0.0015324937,0.00050720223,0.00094812654,0.0016662253,0.0012934506,0.0023334015],"category_scores_gemma":[0.0031017573,0.00031326382,0.0009862041,0.0014129161,0.0010144665,0.0018543282,0.0016416126,0.0022514595,0.0013724923],"study_design_candidate":"bench_or_experimental","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":[0.00032360424,0.0003257059,0.0019159137,0.00021190674,0.00017127213,0.00017817366,0.00020633587,0.2548989,0.049851675,0.016373336,0.014571868,0.6609714],"study_design_scores_gemma":[0.000008051086,0.000026898348,0.0006781324,0.000011200053,0.000014949967,0.000086748725,0.000028663851,0.9717604,0.010260075,0.014015371,0.003091775,0.000017753342],"about_ca_topic_score_codex":0.0036812155,"about_ca_topic_score_gemma":0.0050510447,"teacher_disagreement_score":0.0036812155,"about_ca_system_score_codex":0.0009850853,"about_ca_system_score_gemma":0.0008718494,"threshold_uncertainty_score":0.007806003},"labels":[],"label_agreement":null},{"id":"W2991222810","doi":"10.48550/arxiv.1911.12247","title":"Contrastive Learning of Structured World Models","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction","keywords":"Computer science; Representation (politics); Object (grammar); Embedding; Set (abstract data type); Artificial intelligence; Process (computing); Graph; Class (philosophy); Theoretical computer science","score_opus":0.07245122322623201,"score_gpt":0.1855727580677684,"score_spread":0.11312153484153639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991222810","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":[0.07978122,0.0002041033,0.91743207,0.00036109184,0.00002910357,0.00004215052,0.00013446348,0.0005908567,0.0014249244],"genre_scores_gemma":[0.86947423,0.00012741922,0.12783648,0.00022836175,0.000036138386,0.000095583426,0.00046767193,0.000105825624,0.0016282765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994886,0.00020262323,0.00001906862,0.00017733184,0.00007070044,0.000041728737],"domain_scores_gemma":[0.9973586,0.0017995511,0.00020354513,0.0003474887,0.00018838157,0.000102434955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012325781,0.00084046,0.00087224494,0.0006000924,0.00033138477,0.0009682753,0.0018730599,0.001412257,0.0014388991],"category_scores_gemma":[0.0067877974,0.0006976361,0.00094580784,0.0003885764,0.001258335,0.002544512,0.0018218827,0.0022960762,0.0003369581],"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":[0.00010351295,0.00007418508,0.0011029495,0.000066565066,0.000068034715,0.000070648624,0.00011506502,0.9367086,0.0031212466,0.01837321,0.00086000207,0.039335907],"study_design_scores_gemma":[0.000002866206,0.000010649207,0.00004819452,0.0000020493053,0.0000017610372,0.000004584944,0.0000029297835,0.99339724,0.00032611677,0.0061439546,0.000057977442,0.0000017908263],"about_ca_topic_score_codex":0.002725409,"about_ca_topic_score_gemma":0.0041673305,"teacher_disagreement_score":0.002725409,"about_ca_system_score_codex":0.0010549397,"about_ca_system_score_gemma":0.0005775135,"threshold_uncertainty_score":0.00765419},"labels":[],"label_agreement":null},{"id":"W2993018413","doi":"10.1016/j.knosys.2019.105343","title":"Autoencoder based sample selection for self-taught learning","year":2019,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Research (Canada)","funders":"","keywords":"Computer science; Autoencoder; Transfer of learning; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Sample (material); Machine learning; Data mining; Deep learning","score_opus":0.01578446395385561,"score_gpt":0.25331321865341005,"score_spread":0.23752875469955445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993018413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023775134,0.00039341144,0.97405994,0.00009846577,0.00003832237,0.000039428,0.00004900655,0.0009802348,0.00056614674],"genre_scores_gemma":[0.6559335,0.0003375419,0.33623442,0.00023096144,0.00008482453,0.00021641518,0.00050364033,0.00024714,0.006211615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995987,0.000113744885,0.000024940058,0.00012932262,0.00008929905,0.00004411325],"domain_scores_gemma":[0.9984352,0.0010111093,0.00006218455,0.00013789102,0.00029924544,0.000054440276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001237879,0.00052712584,0.0011412689,0.00059724343,0.00035895556,0.0005217248,0.0012915381,0.0011384924,0.0021938381],"category_scores_gemma":[0.0031298522,0.00044729587,0.0006036717,0.0005181782,0.00051398954,0.0010718257,0.0010206603,0.0013367994,0.00063254463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031363074,0.00028934804,0.0010433879,0.0001375994,0.00012742526,0.000085709566,0.00009874957,0.32088968,0.014549144,0.005803566,0.002960804,0.653701],"study_design_scores_gemma":[0.000005078226,0.000021544838,0.00014587666,0.0000033940246,0.0000070880033,0.000010377151,0.0000036921629,0.99665475,0.0019049424,0.0010666556,0.00017369702,0.0000029412247],"about_ca_topic_score_codex":0.0050756335,"about_ca_topic_score_gemma":0.005530421,"teacher_disagreement_score":0.0050756335,"about_ca_system_score_codex":0.0006686877,"about_ca_system_score_gemma":0.0009145026,"threshold_uncertainty_score":0.010092199},"labels":[],"label_agreement":null},{"id":"W2994886105","doi":"","title":"Progressive Memory Banks for Incremental Domain Adaptation","year":2020,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":15,"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; University of Waterloo","funders":"","keywords":"Computer science; Recurrent neural network; Domain adaptation; Parameterized complexity; Domain (mathematical analysis); Artificial intelligence; Artificial neural network; Adaptation (eye); Machine learning; Algorithm","score_opus":0.09777329092838097,"score_gpt":0.3433704679106314,"score_spread":0.24559717698225045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994886105","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":[0.02886549,0.000967464,0.96485436,0.00023447264,0.00009692126,0.00009901079,0.00013195843,0.0027711568,0.001979181],"genre_scores_gemma":[0.7043431,0.0008431091,0.287635,0.00059826684,0.00012068408,0.00036641382,0.0006538995,0.00027366084,0.0051658493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999582,0.000092485,0.00002964179,0.00017387272,0.00007029054,0.000051666975],"domain_scores_gemma":[0.9988728,0.00053546886,0.0000742906,0.00031915976,0.00014650013,0.000051779432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094351225,0.001018469,0.0009135128,0.0007924926,0.0004594618,0.00074315525,0.0024081017,0.0011515701,0.0027722663],"category_scores_gemma":[0.004024582,0.0005767486,0.0008373325,0.0008388838,0.00080901745,0.0034201154,0.0017020273,0.0021789696,0.0012283787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031223492,0.00032626896,0.0016851892,0.00018061441,0.00012074377,0.00030968306,0.0003367018,0.34571445,0.014080541,0.019560155,0.006245875,0.6111276],"study_design_scores_gemma":[0.000015647776,0.000043566517,0.00016182216,0.000012183874,0.000023278857,0.000063780695,0.000020883179,0.9816913,0.0037773752,0.012285812,0.001891911,0.000012468298],"about_ca_topic_score_codex":0.0048602144,"about_ca_topic_score_gemma":0.005527752,"teacher_disagreement_score":0.0048602144,"about_ca_system_score_codex":0.0007234056,"about_ca_system_score_gemma":0.0009433383,"threshold_uncertainty_score":0.00966388},"labels":[],"label_agreement":null},{"id":"W2995627237","doi":"","title":"Contrastive Learning of Structured World Models","year":2020,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction","keywords":"Computer science; Representation (politics); Object (grammar); Set (abstract data type); Embedding; Artificial intelligence; Process (computing); Class (philosophy); Graph; Artificial neural network; Machine learning; Theoretical computer science","score_opus":0.03323477857556303,"score_gpt":0.23758291113033897,"score_spread":0.20434813255477594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995627237","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":[0.06934475,0.00020181612,0.9280812,0.00031927275,0.000029652332,0.00004158329,0.00012478301,0.00056433026,0.0012924579],"genre_scores_gemma":[0.8575967,0.00013264512,0.13971522,0.000240132,0.000035665555,0.00009694099,0.00046015694,0.00009982421,0.0016226273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995277,0.00017495907,0.000019509785,0.00016862563,0.00006863261,0.000040539293],"domain_scores_gemma":[0.99755496,0.0016796662,0.00018134841,0.00029798815,0.00019258249,0.00009350057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012103798,0.00083894935,0.0008534522,0.000568113,0.00032443865,0.00093938294,0.0018506849,0.0013242933,0.0013921699],"category_scores_gemma":[0.006104675,0.00066790334,0.0008964835,0.00037170335,0.0011603483,0.0024040549,0.0017388402,0.0021951224,0.0003187529],"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":[0.00010800575,0.000078573525,0.0010687966,0.00006214338,0.000065074295,0.00007206201,0.00011299427,0.9356217,0.0031314555,0.014651747,0.00082390895,0.044203587],"study_design_scores_gemma":[0.000002623453,0.000010899524,0.000044260978,0.0000019146553,0.0000017187361,0.000004562873,0.0000028453287,0.9951014,0.00033829905,0.0044322186,0.000057463385,0.0000017348384],"about_ca_topic_score_codex":0.003036165,"about_ca_topic_score_gemma":0.0047397623,"teacher_disagreement_score":0.003036165,"about_ca_system_score_codex":0.0010413919,"about_ca_system_score_gemma":0.0005974834,"threshold_uncertainty_score":0.007555902},"labels":[],"label_agreement":null},{"id":"W2995680717","doi":"10.48550/arxiv.1912.08936","title":"One-Shot Weakly Supervised Video Object Segmentation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of Alberta","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Computer vision; Object (grammar); Pascal (unit); Scale-space segmentation; Segmentation-based object categorization; Shot (pellet); Bounding overwatch; Image segmentation; Benchmark (surveying); Pattern recognition (psychology); Geography","score_opus":0.12959186461909553,"score_gpt":0.21711043951524778,"score_spread":0.08751857489615225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995680717","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105889164,0.00097014767,0.8733404,0.00023841424,0.00017774256,0.0003120717,0.001760914,0.012297915,0.005013101],"genre_scores_gemma":[0.5751081,0.0003732275,0.4012334,0.00041607203,0.00014774693,0.00031442725,0.011215999,0.0010910962,0.010099911],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887663,0.00013460388,0.000041085783,0.0006225124,0.00019170024,0.0001334931],"domain_scores_gemma":[0.99863106,0.00033265766,0.00012629898,0.00045067296,0.00034282348,0.000116440875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010345047,0.0014626426,0.0015360285,0.0011681964,0.0005665983,0.0011950749,0.0026052326,0.001931972,0.0029369814],"category_scores_gemma":[0.0031358255,0.00057078036,0.0011535464,0.0007967565,0.0010121409,0.0018843119,0.0017064625,0.0014802444,0.0023617349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014558122,0.0005453983,0.0049385745,0.0006638386,0.00023885451,0.00033880633,0.0003254778,0.10169704,0.12865329,0.0050594155,0.019606866,0.7364767],"study_design_scores_gemma":[0.000025857238,0.00020609911,0.003256946,0.0000397863,0.000037403905,0.0002361422,0.00010026523,0.94286114,0.041685283,0.0069088927,0.004608092,0.000034033754],"about_ca_topic_score_codex":0.0040255664,"about_ca_topic_score_gemma":0.008934479,"teacher_disagreement_score":0.0040255664,"about_ca_system_score_codex":0.00093139015,"about_ca_system_score_gemma":0.0010768884,"threshold_uncertainty_score":0.00982523},"labels":[],"label_agreement":null},{"id":"W2996170846","doi":"","title":"Training Recurrent Neural Networks Online by Learning Explicit State Variables","year":2020,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of Alberta","funders":"","keywords":"Recurrent neural network; Computer science; Truncation (statistics); Computation; Focus (optics); Machine learning; Artificial intelligence; Variety (cybernetics); Artificial neural network; Online learning; Algorithm; Mathematical optimization; Mathematics","score_opus":0.11405124960379914,"score_gpt":0.3407093050386591,"score_spread":0.22665805543485995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996170846","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":[0.027069893,0.0002176239,0.9696548,0.0001781394,0.000044561442,0.000033264365,0.000053702275,0.0017264783,0.0010213879],"genre_scores_gemma":[0.5829067,0.00026103578,0.41179726,0.00025871987,0.000070235896,0.00018791617,0.00052011607,0.0003651809,0.0036328586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993818,0.00015873788,0.0000381537,0.00017919778,0.0001694363,0.00007270253],"domain_scores_gemma":[0.9962478,0.0026526311,0.0002465245,0.00033025045,0.0004399752,0.000082725914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017837274,0.0013156496,0.0011310338,0.00048817674,0.00037942757,0.0008536011,0.0019050328,0.0014027735,0.0025239056],"category_scores_gemma":[0.010184659,0.0008495293,0.0005946621,0.00052418275,0.000802947,0.0026805536,0.0012263055,0.002705505,0.0008873123],"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":[0.00008892546,0.00008028286,0.00067354226,0.000060898117,0.000041183568,0.00006646409,0.000058176043,0.8773697,0.0030283094,0.006195743,0.0014809644,0.110855766],"study_design_scores_gemma":[0.000003028447,0.000008240955,0.000026181957,0.000003016879,0.0000020375765,0.00000465257,0.0000017222739,0.9980496,0.0004772932,0.0013437315,0.00007853775,0.0000019426398],"about_ca_topic_score_codex":0.0064484477,"about_ca_topic_score_gemma":0.010228405,"teacher_disagreement_score":0.0064484477,"about_ca_system_score_codex":0.00079521857,"about_ca_system_score_gemma":0.0013908056,"threshold_uncertainty_score":0.012821853},"labels":[],"label_agreement":null},{"id":"W2996255323","doi":"10.1007/978-3-030-58558-7_2","title":"Associative Alignment for Few-Shot Image Classification","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Université Laval","funders":"","keywords":"Centroid; Computer science; Artificial intelligence; Pattern recognition (psychology); Associative property; Metric (unit); Feature (linguistics); Set (abstract data type); Domain (mathematical analysis); Image (mathematics); Constructive; Machine learning; Mathematics","score_opus":0.06221222492084128,"score_gpt":0.3203891967185511,"score_spread":0.2581769717977098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996255323","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027231319,0.0009902017,0.96809363,0.00016199207,0.00010754865,0.000059280606,0.0002283581,0.0020671904,0.0010604614],"genre_scores_gemma":[0.6257348,0.0010764949,0.35982126,0.00043522747,0.00039244714,0.0002731596,0.0030117773,0.000534666,0.0087201875],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988669,0.00026500484,0.00006812809,0.0004318025,0.00024247532,0.00012577367],"domain_scores_gemma":[0.9975776,0.0011021249,0.00017435812,0.0006515042,0.00034395632,0.00015053649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014372227,0.0009214586,0.0021341608,0.0016724127,0.00074994436,0.0010859655,0.0023100928,0.0018357612,0.004065455],"category_scores_gemma":[0.0044447617,0.0005661333,0.0008694254,0.0022851273,0.0007932754,0.002995898,0.0023033242,0.002085057,0.0021993648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051203143,0.00049996225,0.0015361191,0.00031542138,0.00020455335,0.00013654574,0.000115404684,0.06524946,0.03141516,0.0105973175,0.0080947755,0.88132334],"study_design_scores_gemma":[0.000016825132,0.00011229036,0.0008441615,0.000016709457,0.000041974574,0.00012477557,0.000051096667,0.9599706,0.009828471,0.027511176,0.0014631994,0.00001872829],"about_ca_topic_score_codex":0.0023652113,"about_ca_topic_score_gemma":0.0036155458,"teacher_disagreement_score":0.004065455,"about_ca_system_score_codex":0.00052596774,"about_ca_system_score_gemma":0.00093479466,"threshold_uncertainty_score":0.01360029},"labels":[],"label_agreement":null},{"id":"W2996891787","doi":"10.1609/aaai.v34i04.6038","title":"Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"Beijing Advanced Innovation Center for Big Data and Brain Computing; National Natural Science Foundation of China","keywords":"Learning vector quantization; Competitive learning; Artificial intelligence; Artificial neural network; Computer science; Vector quantization; Information bottleneck method; Machine learning; Quantization (signal processing); Feature learning; Instance-based learning; Classifier (UML); Pattern recognition (psychology); Unsupervised learning; Algorithm; Cluster analysis","score_opus":0.11109664830582071,"score_gpt":0.28008444633561197,"score_spread":0.16898779802979125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996891787","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":[0.0036888977,0.00046049565,0.9948259,0.00016186778,0.000045484714,0.000028013874,0.00004106287,0.00013233557,0.00061590725],"genre_scores_gemma":[0.49419725,0.0013688118,0.49935353,0.00044245727,0.00045554037,0.0003355179,0.00045687388,0.00015008124,0.003239965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816847,0.0007324562,0.00011763338,0.00036067938,0.0004969308,0.00012375733],"domain_scores_gemma":[0.99722975,0.0014050535,0.0002506454,0.00042509177,0.0005719706,0.000117579475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003399865,0.0008315353,0.0019032013,0.0014405429,0.00052261,0.0018058539,0.0031060495,0.0014286968,0.0021703762],"category_scores_gemma":[0.008592124,0.00058327045,0.0008129366,0.0019351478,0.0014223122,0.0041507906,0.0027432786,0.0022956892,0.00043081393],"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":[0.00011385266,0.00012970973,0.0010998967,0.0002647906,0.00016104271,0.000088088076,0.00027239078,0.62331825,0.0031368092,0.14747117,0.0032003692,0.22074363],"study_design_scores_gemma":[0.0000051438687,0.000029812605,0.0000711394,0.000011129612,0.000009600156,0.000012276972,0.000010638886,0.93626463,0.0004587941,0.062499087,0.0006210978,0.0000067382175],"about_ca_topic_score_codex":0.0028362034,"about_ca_topic_score_gemma":0.0021322242,"teacher_disagreement_score":0.003399865,"about_ca_system_score_codex":0.0013064475,"about_ca_system_score_gemma":0.0009955519,"threshold_uncertainty_score":0.017980397},"labels":[],"label_agreement":null},{"id":"W2997420347","doi":"10.1609/aaai.v34i04.6115","title":"Dual Adversarial Co-Learning for Multi-Domain Text Classification","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Classifier (UML); Artificial intelligence; Labeled data; Adversarial system; Machine learning; Domain (mathematical analysis); Generalization; Dual (grammatical number); Pattern recognition (psychology); Mathematics","score_opus":0.20247617342960006,"score_gpt":0.34109983262040067,"score_spread":0.1386236591908006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997420347","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":[0.018039435,0.00039943852,0.97956026,0.00037551127,0.000052527965,0.0000358308,0.000057198184,0.0005086495,0.0009711685],"genre_scores_gemma":[0.8102205,0.00041626423,0.18142231,0.000523042,0.00021532747,0.00019593492,0.00066480367,0.00013638692,0.006205451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988073,0.0004504467,0.000049467977,0.0003297068,0.0002371616,0.00012590262],"domain_scores_gemma":[0.9967049,0.0020103643,0.00033943474,0.00045573513,0.00033023657,0.00015937597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023524167,0.0010191017,0.0011531059,0.00078203983,0.00050157786,0.0008056071,0.0017386308,0.0015346315,0.0016271741],"category_scores_gemma":[0.0050635547,0.00038581467,0.0007954486,0.0008945444,0.0013455029,0.0021501726,0.001931744,0.0031314462,0.0007202856],"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":[0.0003615049,0.00027544814,0.0018709699,0.00011472906,0.000096914984,0.00019301141,0.00014280525,0.81129026,0.006478543,0.020252425,0.005293539,0.15362988],"study_design_scores_gemma":[0.00000313258,0.0000144854275,0.000056281708,0.0000021321146,0.000002778498,0.000011204141,0.0000038874837,0.9953323,0.00066941086,0.0036692237,0.00023177835,0.0000032805153],"about_ca_topic_score_codex":0.0019964543,"about_ca_topic_score_gemma":0.0015493577,"teacher_disagreement_score":0.0023524167,"about_ca_system_score_codex":0.0010484912,"about_ca_system_score_gemma":0.0007255072,"threshold_uncertainty_score":0.01244092},"labels":[],"label_agreement":null},{"id":"W2997820257","doi":"10.1609/aaai.v34i07.6628","title":"Diversity Transfer Network for Few-Shot Learning","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Transfer of learning; Classifier (UML); Negative transfer; Machine learning; Generative grammar; Feature vector; Code (set theory); Feature (linguistics); Task (project management)","score_opus":0.16750594347475917,"score_gpt":0.29090375327538814,"score_spread":0.12339780980062898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997820257","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":[0.02877291,0.0010318016,0.9645139,0.0004390337,0.000101123645,0.000107947904,0.00022760831,0.0018288057,0.0029767975],"genre_scores_gemma":[0.76287115,0.0006586069,0.22462179,0.00080080057,0.00018015332,0.00046591696,0.0015014488,0.000298448,0.008601658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946886,0.00013137041,0.000021715525,0.00020831153,0.000104691586,0.000065069165],"domain_scores_gemma":[0.9990138,0.00052524987,0.00007130966,0.00015814207,0.00015723931,0.00007430554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012960094,0.001149004,0.0012718176,0.0007843557,0.00067080144,0.00078351196,0.0027909693,0.001900979,0.0031221523],"category_scores_gemma":[0.004285025,0.0005096185,0.00082161644,0.00075148325,0.0011823114,0.0022350557,0.0019473593,0.0026696955,0.0009595626],"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":[0.00023154281,0.00024677638,0.0014767153,0.00022294336,0.0001388566,0.00022455954,0.00021197702,0.6240854,0.01183586,0.019497192,0.007866574,0.33396164],"study_design_scores_gemma":[0.000007268047,0.00002583503,0.000096106036,0.000006386263,0.0000070278047,0.000028341563,0.000007804559,0.98809403,0.0012683353,0.009944808,0.00050739315,0.000006640492],"about_ca_topic_score_codex":0.0040518628,"about_ca_topic_score_gemma":0.0051071034,"teacher_disagreement_score":0.0040518628,"about_ca_system_score_codex":0.0013917151,"about_ca_system_score_gemma":0.0009028087,"threshold_uncertainty_score":0.010444641},"labels":[],"label_agreement":null},{"id":"W2998655644","doi":"10.1609/aaai.v34i04.5884","title":"Residual Continual Learning","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Residual; Forgetting; Computer science; Normalization (sociology); Artificial intelligence; Task (project management); Machine learning; Learning network; Convolutional neural network; Deep learning; Artificial neural network; Algorithm; Engineering","score_opus":0.10166282549906423,"score_gpt":0.2867960757735737,"score_spread":0.18513325027450944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998655644","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":[0.018829659,0.0004794831,0.97497034,0.00023553103,0.00011090528,0.000067342684,0.00007552286,0.0021081527,0.0031230466],"genre_scores_gemma":[0.6034182,0.00037560257,0.3836921,0.0005603565,0.00022578545,0.00023627566,0.0005750869,0.00041255887,0.010504105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910283,0.00017540784,0.00004209515,0.00029749968,0.0002754901,0.000106737396],"domain_scores_gemma":[0.99854255,0.0003543232,0.00015456231,0.00042078027,0.00039953276,0.00012832129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016786491,0.0009050179,0.0010983548,0.0008282512,0.0005863067,0.00088700553,0.0033531776,0.0012739301,0.00381993],"category_scores_gemma":[0.0038162414,0.0005185221,0.00067959673,0.0006980664,0.0012966988,0.0026430401,0.0023800624,0.001913791,0.0012812958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004235347,0.0003650777,0.001830264,0.00022033647,0.00012431107,0.00019793636,0.00022953577,0.2171605,0.01633111,0.027256666,0.008448487,0.7274122],"study_design_scores_gemma":[0.000021337306,0.00010229217,0.00017796026,0.000011359814,0.000011841935,0.00007088546,0.000014692268,0.98356646,0.0035481434,0.009777268,0.0026805163,0.000017248953],"about_ca_topic_score_codex":0.0022272798,"about_ca_topic_score_gemma":0.0025628058,"teacher_disagreement_score":0.00381993,"about_ca_system_score_codex":0.000657805,"about_ca_system_score_gemma":0.0011656737,"threshold_uncertainty_score":0.012778997},"labels":[],"label_agreement":null},{"id":"W2999918589","doi":"10.1109/tnnls.2019.2957229","title":"LogDet Metric-Based Domain Adaptation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Key Research and Development Program of China; Australian Research Council; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Metric (unit); Computer science; Domain adaptation; Curse of dimensionality; Domain (mathematical analysis); Norm (philosophy); Transformation (genetics); Adaptation (eye); Dimensionality reduction; Algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.028279432711695324,"score_gpt":0.22772003949308675,"score_spread":0.19944060678139142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999918589","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006121202,0.00033786197,0.9909774,0.00011135982,0.00007442589,0.000041709263,0.00007438217,0.00082169747,0.0014399273],"genre_scores_gemma":[0.43768793,0.0010685995,0.54664135,0.0006780173,0.00026653556,0.00036217977,0.0015753722,0.0008177675,0.010902268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858993,0.000419428,0.00008232181,0.00043074554,0.000382391,0.00009508711],"domain_scores_gemma":[0.9986523,0.00048359061,0.00010564006,0.00030032438,0.00038871422,0.00006954052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016264414,0.001176473,0.001184514,0.0010052617,0.00043004888,0.0011329838,0.001533149,0.0013249031,0.002547268],"category_scores_gemma":[0.006326703,0.00034988744,0.0008833665,0.0012427114,0.001044556,0.002491842,0.002393956,0.00196069,0.0019109424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026963017,0.00022545467,0.0017449273,0.00026177953,0.00011480119,0.0002005811,0.00018970689,0.30697238,0.022852441,0.03494892,0.010704027,0.6215154],"study_design_scores_gemma":[0.000012164465,0.00008502997,0.0005772247,0.000015316806,0.000015047478,0.00022865774,0.00003245742,0.9718076,0.0064379056,0.016029917,0.0047266115,0.000032146163],"about_ca_topic_score_codex":0.0019215934,"about_ca_topic_score_gemma":0.002344864,"teacher_disagreement_score":0.002547268,"about_ca_system_score_codex":0.00076303846,"about_ca_system_score_gemma":0.0012641654,"threshold_uncertainty_score":0.008601546},"labels":[],"label_agreement":null},{"id":"W3002047086","doi":"10.24963/ijcai.2020/120","title":"Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Pascal (unit); Embedding; Minimum bounding box; Computer vision; Shot (pellet); Object (grammar); Image segmentation; Scale-space segmentation; Segmentation-based object categorization; Bounding overwatch; Pixel; Pattern recognition (psychology); Image (mathematics)","score_opus":0.050282794963911095,"score_gpt":0.32127389630887215,"score_spread":0.27099110134496107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002047086","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":[0.04520619,0.0007288304,0.9453709,0.00019136757,0.00009655457,0.0001220635,0.00028723374,0.00617819,0.0018185555],"genre_scores_gemma":[0.57902104,0.0004522208,0.4065956,0.00059796043,0.00017780229,0.00024711902,0.00349534,0.0009737829,0.008439067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988809,0.00016422773,0.000041612628,0.00062484044,0.00015237069,0.00013608037],"domain_scores_gemma":[0.9986645,0.0004866122,0.0001414839,0.0003221678,0.0002599757,0.00012534326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011139038,0.0018278611,0.0019125546,0.001575329,0.000614327,0.0014356792,0.0032813824,0.002094549,0.002136764],"category_scores_gemma":[0.0024811565,0.00086032826,0.0017015411,0.0014785209,0.00120943,0.0032623673,0.0023516773,0.0023238147,0.0015228216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008083507,0.0007350763,0.003893169,0.00042956122,0.00035203953,0.00029664437,0.0004976944,0.17574625,0.07559598,0.0059940526,0.009675721,0.7259754],"study_design_scores_gemma":[0.000011971613,0.00008766939,0.00086336304,0.000014538702,0.00003106794,0.00007151902,0.000046686568,0.98028743,0.011502525,0.0058705467,0.0011942821,0.000018400055],"about_ca_topic_score_codex":0.007224318,"about_ca_topic_score_gemma":0.012344428,"teacher_disagreement_score":0.007224318,"about_ca_system_score_codex":0.001260724,"about_ca_system_score_gemma":0.0010455283,"threshold_uncertainty_score":0.014364541},"labels":[],"label_agreement":null},{"id":"W3006469715","doi":"10.1109/icpr48806.2021.9412902","title":"Few-Shot Few-Shot Learning and the role of Spatial Attention","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Shot (pellet); Computer science; One shot; Artificial intelligence; Engineering; Materials science","score_opus":0.01470476570635841,"score_gpt":0.24482175750278803,"score_spread":0.23011699179642964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006469715","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":[0.044292837,0.0019288318,0.94903505,0.00069980347,0.00013209638,0.000086101805,0.00016520459,0.00073702034,0.0029230954],"genre_scores_gemma":[0.84344345,0.0010284149,0.1470744,0.0007624124,0.00032743326,0.00016366175,0.0006713863,0.00020468551,0.0063240635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902976,0.00025273577,0.000035865425,0.0004378925,0.00014343229,0.00010035443],"domain_scores_gemma":[0.9956038,0.0027458305,0.00033419556,0.0006964218,0.00033241362,0.00028733208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002069421,0.0012879008,0.0016918781,0.0008792522,0.00067116617,0.0015492234,0.0031413618,0.0019981994,0.0021274965],"category_scores_gemma":[0.010709852,0.0007908707,0.0007389326,0.00090243976,0.0022049209,0.0041367584,0.002154724,0.0025711567,0.0005453034],"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":[0.0006278281,0.0006192267,0.0060445364,0.000746465,0.0004159449,0.00045854802,0.0006847983,0.56267184,0.023846889,0.056608416,0.0076154023,0.33966005],"study_design_scores_gemma":[0.000017070539,0.00013833765,0.0013868004,0.000035786114,0.000037164536,0.00013179365,0.000047469926,0.92889875,0.0044261883,0.06335197,0.001493704,0.00003502243],"about_ca_topic_score_codex":0.0047878996,"about_ca_topic_score_gemma":0.004658971,"teacher_disagreement_score":0.0047878996,"about_ca_system_score_codex":0.0012685717,"about_ca_system_score_gemma":0.0008282819,"threshold_uncertainty_score":0.010944307},"labels":[],"label_agreement":null},{"id":"W3007560286","doi":"10.1017/s0269888920000107","title":"Domain adaptation-based transfer learning using adversarial networks","year":2020,"lang":"en","type":"article","venue":"The Knowledge Engineering Review","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Generalization; Adversarial system; Task (project management); Reinforcement learning; Adaptation (eye); Artificial intelligence; Transfer of learning; Domain (mathematical analysis); Machine learning; Multi-task learning; Domain adaptation; Relation (database); Negative transfer; Data mining; Psychology; Mathematics","score_opus":0.035040056946704964,"score_gpt":0.24525326218233207,"score_spread":0.2102132052356271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007560286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01875881,0.0013922453,0.9766591,0.000289773,0.00009735641,0.00006751455,0.000039303257,0.0007238644,0.001972071],"genre_scores_gemma":[0.8700212,0.0012398468,0.12269847,0.00050530146,0.00016075862,0.00023295985,0.00026904908,0.000118660275,0.0047537386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991937,0.0003254378,0.0000379301,0.00022070746,0.0001542761,0.000067947905],"domain_scores_gemma":[0.9982622,0.0011120796,0.00014093038,0.00021819107,0.00019085527,0.00007576249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016720742,0.0011268128,0.0011559558,0.0007559173,0.0003382326,0.0006211163,0.0019107978,0.0012272607,0.0016297216],"category_scores_gemma":[0.0034361356,0.00043606872,0.00097603555,0.00069549686,0.001054987,0.0014213367,0.001753485,0.0021971788,0.000513151],"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":[0.000058455556,0.0001108917,0.0006096427,0.000092349495,0.00012063866,0.000068481866,0.000059528436,0.87753534,0.0027238308,0.005867981,0.0016560173,0.11109682],"study_design_scores_gemma":[0.0000035671058,0.000020189305,0.000079654936,0.000006364769,0.0000056847525,0.000010931262,0.000003586279,0.9958474,0.00050012016,0.0032554772,0.00026290506,0.00000405862],"about_ca_topic_score_codex":0.002670475,"about_ca_topic_score_gemma":0.0014997278,"teacher_disagreement_score":0.002670475,"about_ca_system_score_codex":0.0008215902,"about_ca_system_score_gemma":0.0005748697,"threshold_uncertainty_score":0.0088428855},"labels":[],"label_agreement":null},{"id":"W3011580887","doi":"10.1109/access.2020.2982034","title":"Multi-Adversarial Partial Transfer Learning With Object-Level Attention Mechanism for Unsupervised Remote Sensing Scene Classification","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; Key Research and Development Program of Ningxia; National Natural Science Foundation of China","keywords":"Computer science; Categorization; Artificial intelligence; Classifier (UML); Transfer of learning; Adversarial system; Machine learning; Object (grammar); Deep learning; Convolutional neural network; Object detection; Domain (mathematical analysis); Context (archaeology); Cognitive neuroscience of visual object recognition; Contextual image classification; Pattern recognition (psychology); Image (mathematics)","score_opus":0.14233981932754422,"score_gpt":0.3105778125128565,"score_spread":0.16823799318531227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011580887","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":[0.03557904,0.00048284553,0.96052384,0.00030874895,0.00005826926,0.00003560631,0.00005502814,0.00090703403,0.0020495334],"genre_scores_gemma":[0.91810644,0.00032338305,0.07404056,0.00039078895,0.00008218189,0.00010102501,0.00028822254,0.00010766946,0.0065597235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954104,0.000105317136,0.000017206376,0.00014685336,0.00011223263,0.000077453995],"domain_scores_gemma":[0.99945503,0.00023649615,0.00007852265,0.00008189364,0.000103612256,0.00004437834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094981375,0.00091981044,0.0007982266,0.00055361754,0.00032981706,0.0005301966,0.0016734224,0.00083486637,0.001694217],"category_scores_gemma":[0.0015934091,0.00032526307,0.00092043815,0.00048250536,0.0008985787,0.0013620277,0.0015590547,0.001598432,0.00034656157],"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":[0.00014431581,0.00010474706,0.0011463499,0.000058700192,0.00009035136,0.00012433185,0.0000954712,0.8652755,0.008566802,0.013579972,0.0025105046,0.10830288],"study_design_scores_gemma":[0.0000015352849,0.000011319958,0.0000717266,0.0000013958667,0.00000410958,0.000008158531,0.0000022936647,0.9968213,0.0006150163,0.002313058,0.00014757905,0.0000025506815],"about_ca_topic_score_codex":0.0039690956,"about_ca_topic_score_gemma":0.0026410436,"teacher_disagreement_score":0.0039690956,"about_ca_system_score_codex":0.0009708632,"about_ca_system_score_gemma":0.00067162816,"threshold_uncertainty_score":0.007892013},"labels":[],"label_agreement":null},{"id":"W3012872813","doi":"10.1145/3366423.3380034","title":"Anchored Model Transfer and Soft Instance Transfer for Cross-Task Cross-Domain Learning: A Study Through Aspect-Level Sentiment Classification","year":2020,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"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 Ottawa","funders":"","keywords":"Transfer of learning; Computer science; Benchmark (surveying); Artificial intelligence; Machine learning; Task (project management); Multi-task learning; Domain (mathematical analysis); Inductive transfer; Baseline (sea); Semi-supervised learning; Transfer (computing); Engineering","score_opus":0.10814300672687843,"score_gpt":0.3285644342266785,"score_spread":0.2204214274998001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012872813","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":[0.19115911,0.0065142736,0.78982365,0.0013611501,0.0004982326,0.00039259632,0.00042046278,0.003703601,0.006126896],"genre_scores_gemma":[0.8896702,0.0011781487,0.100981385,0.0005618572,0.0002661942,0.00028184595,0.001386905,0.00040182838,0.0052715633],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963831,0.0017662336,0.00014925817,0.000998249,0.0004303112,0.00027281098],"domain_scores_gemma":[0.9921244,0.004302649,0.0004976155,0.001902121,0.00081023294,0.00036305867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008836102,0.0021241372,0.0017521231,0.0015936353,0.00079722475,0.0021636619,0.0027038509,0.0028302504,0.002442422],"category_scores_gemma":[0.016980868,0.0005024562,0.0016565771,0.0017909443,0.0018119608,0.005117685,0.00429219,0.0045829546,0.0015188776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008514148,0.0011496616,0.008247569,0.0006276754,0.00058314047,0.00033418668,0.00061984616,0.22577348,0.009510198,0.01346106,0.011116712,0.72772515],"study_design_scores_gemma":[0.000033891654,0.00028117743,0.0012889402,0.000037453545,0.000049147035,0.00009134529,0.00013709127,0.9769035,0.003534132,0.015901713,0.0017139139,0.000027630449],"about_ca_topic_score_codex":0.001998089,"about_ca_topic_score_gemma":0.0014818849,"teacher_disagreement_score":0.008836102,"about_ca_system_score_codex":0.0012567664,"about_ca_system_score_gemma":0.001044484,"threshold_uncertainty_score":0.04673034},"labels":[],"label_agreement":null},{"id":"W3014073509","doi":"10.48550/arxiv.2003.12943","title":"Adaptive Object Detection with Dual Multi-Label Prediction","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Regularization (linguistics); Pattern recognition (psychology); Object detection; Benchmark (surveying); Object (grammar); Cognitive neuroscience of visual object recognition; Feature (linguistics); Machine learning; Computer vision","score_opus":0.11350526566837923,"score_gpt":0.19280386103611089,"score_spread":0.07929859536773165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014073509","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":[0.025367398,0.00047863988,0.9700024,0.00023986182,0.000099013036,0.000054837277,0.0001217036,0.0024651105,0.0011711379],"genre_scores_gemma":[0.65367013,0.0004204368,0.33248755,0.00094032666,0.00017297098,0.00017205132,0.0013213649,0.0002463753,0.010568747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990644,0.0001629902,0.000023547409,0.00043332897,0.00020934266,0.00010633238],"domain_scores_gemma":[0.99891293,0.00037195624,0.00010570422,0.00031584487,0.00021819757,0.00007533597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011009187,0.0011069915,0.0014315895,0.00066762266,0.000345014,0.0009396174,0.003411905,0.0016447345,0.0015117791],"category_scores_gemma":[0.002160183,0.00046506786,0.00092898874,0.00075047446,0.00088849594,0.0020512636,0.0018882181,0.0027793122,0.0013704859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004888358,0.0006199083,0.0038580615,0.00017167065,0.00021084661,0.0003142951,0.0001678616,0.2967323,0.035192218,0.008744843,0.008590486,0.64490867],"study_design_scores_gemma":[0.000006502181,0.000034910623,0.00032186502,0.0000045710613,0.000011192946,0.00004919653,0.000009397545,0.9906577,0.004330319,0.0038622313,0.00070310134,0.0000090723315],"about_ca_topic_score_codex":0.0027842924,"about_ca_topic_score_gemma":0.0033975646,"teacher_disagreement_score":0.003411905,"about_ca_system_score_codex":0.0006717088,"about_ca_system_score_gemma":0.00072647614,"threshold_uncertainty_score":0.0058222413},"labels":[],"label_agreement":null},{"id":"W3014285644","doi":"10.48550/arxiv.2004.01735","title":"Unsupervised Domain Adaptation with Progressive Domain Augmentation","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Domain (mathematical analysis); Computer science; Subspace topology; Exploit; Domain adaptation; Artificial intelligence; Adaptation (eye); Divergence (linguistics); Interpolation (computer graphics); Pattern recognition (psychology); Image (mathematics); Mathematics; Biology","score_opus":0.08486027176570617,"score_gpt":0.1941086992223512,"score_spread":0.10924842745664502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014285644","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022495963,0.00024951473,0.9741255,0.00007254607,0.000056972916,0.00006736034,0.00010849859,0.001462672,0.0013609753],"genre_scores_gemma":[0.46504942,0.00035234503,0.5274273,0.00034863452,0.00009292351,0.0002462626,0.0015545983,0.00030546798,0.0046230643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932563,0.00017961407,0.000023993327,0.00027162666,0.00013382109,0.00006536184],"domain_scores_gemma":[0.99888736,0.0003738837,0.000080401274,0.00037975778,0.00021504927,0.00006354134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007869742,0.0007929021,0.00084756216,0.00090912794,0.00048030043,0.0006739194,0.0012543226,0.00084809127,0.0013805624],"category_scores_gemma":[0.0026541536,0.00029705508,0.0009542956,0.0009535604,0.00092367554,0.0016847438,0.002038563,0.0017585584,0.0011349071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039727654,0.00045759956,0.0036084207,0.0002470222,0.00015766766,0.00029128295,0.00040834185,0.18082504,0.10778975,0.014099794,0.010339299,0.6813784],"study_design_scores_gemma":[0.00002503037,0.00010201415,0.00091494265,0.000017705943,0.00002189476,0.0002654113,0.00009043195,0.94887465,0.02607341,0.018035192,0.0055454243,0.000033908032],"about_ca_topic_score_codex":0.0013906413,"about_ca_topic_score_gemma":0.0021656395,"teacher_disagreement_score":0.0013906413,"about_ca_system_score_codex":0.00036665192,"about_ca_system_score_gemma":0.0007758319,"threshold_uncertainty_score":0.004618466},"labels":[],"label_agreement":null},{"id":"W3014831306","doi":"10.1007/978-3-030-60365-6_15","title":"Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Graph; Segmentation; Discriminator; Artificial intelligence; Domain adaptation; Pattern recognition (psychology); Adversarial system; Invariant (physics); Theoretical computer science; Mathematics","score_opus":0.03319126847841496,"score_gpt":0.28098095256651995,"score_spread":0.24778968408810498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014831306","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":[0.014263094,0.0003209834,0.9826446,0.00013091502,0.000049534643,0.00003541763,0.00015570596,0.0017582184,0.0006414531],"genre_scores_gemma":[0.46862763,0.0007409437,0.5187321,0.0004555741,0.00015230889,0.00017592478,0.002289814,0.0016223096,0.0072033987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999624,0.00009526132,0.000015041541,0.00014813803,0.000064447435,0.00005308162],"domain_scores_gemma":[0.99930024,0.00030511376,0.00005465054,0.00016177294,0.0001313441,0.00004677331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072793075,0.00087713765,0.0013446081,0.0012836496,0.00037937966,0.0007702954,0.0015207683,0.0015509371,0.002872084],"category_scores_gemma":[0.0022253257,0.0005579686,0.0012873214,0.0015078606,0.0007690515,0.0011671275,0.0014469072,0.0017557875,0.0015975222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039599446,0.00020501373,0.000895073,0.00026176768,0.000222976,0.00012258261,0.00011516055,0.3207836,0.061030038,0.009706734,0.008402682,0.59785837],"study_design_scores_gemma":[0.000010358185,0.000037646696,0.0004889276,0.0000070807387,0.000018396244,0.00006170435,0.000018896255,0.98243505,0.0058161397,0.010126565,0.0009680286,0.000011233649],"about_ca_topic_score_codex":0.0040228716,"about_ca_topic_score_gemma":0.0053526917,"teacher_disagreement_score":0.0040228716,"about_ca_system_score_codex":0.0005336895,"about_ca_system_score_gemma":0.0008758486,"threshold_uncertainty_score":0.00960809},"labels":[],"label_agreement":null},{"id":"W3021349224","doi":"10.5244/c.33.40","title":"Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects.","year":2019,"lang":"en","type":"article","venue":"ANU Open Research (Australian National University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Object (grammar); Point cloud; Quality (philosophy); Scale (ratio); Zero (linguistics); Cognitive neuroscience of visual object recognition; Shot (pellet); Machine learning; Pattern recognition (psychology); Computer vision","score_opus":0.1535141030712505,"score_gpt":0.369391187092629,"score_spread":0.21587708402137853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021349224","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057938132,0.0020383191,0.9292684,0.00043825567,0.00020126958,0.00022982286,0.0006158828,0.006792113,0.002477843],"genre_scores_gemma":[0.74827975,0.000814179,0.23328651,0.0013725894,0.00023829246,0.00025390496,0.006413741,0.000787329,0.008553763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738985,0.0005568935,0.00011367931,0.00095797126,0.00072443957,0.00025714518],"domain_scores_gemma":[0.9951624,0.0022092036,0.000300023,0.001392315,0.00065598765,0.00028001302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003581279,0.0019882482,0.0023846114,0.0016962006,0.0010451484,0.0017953602,0.0055849813,0.003332419,0.0026785517],"category_scores_gemma":[0.009804723,0.0010016345,0.0014918845,0.0012358356,0.0025340081,0.0046016043,0.0052714334,0.003681621,0.0015129636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088029733,0.0007051269,0.006930066,0.000755395,0.0004767545,0.00051207276,0.00058938656,0.24772158,0.01930611,0.01034094,0.01890119,0.69288117],"study_design_scores_gemma":[0.000026363645,0.00019719198,0.0010764982,0.00003970558,0.000040213712,0.00023650446,0.00008696184,0.9735068,0.008958268,0.013749766,0.0020551616,0.000026569278],"about_ca_topic_score_codex":0.00768756,"about_ca_topic_score_gemma":0.01238799,"teacher_disagreement_score":0.00768756,"about_ca_system_score_codex":0.001872592,"about_ca_system_score_gemma":0.0014573509,"threshold_uncertainty_score":0.018939853},"labels":[],"label_agreement":null},{"id":"W3021483918","doi":"10.1007/978-3-030-47358-7_54","title":"A Deeper Look at Bongard Problems","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Task (project management); Set (abstract data type); Machine learning; Deep learning; Programming language","score_opus":0.02206970720635547,"score_gpt":0.23107371084120287,"score_spread":0.2090040036348474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021483918","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":[0.019500643,0.02707218,0.67199254,0.042107243,0.0036751279,0.00006621719,0.00024046711,0.0006430967,0.2347025],"genre_scores_gemma":[0.5628141,0.017106036,0.2001554,0.009049275,0.004847463,0.00015772878,0.0006513899,0.001181593,0.204037],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984711,0.00071373256,0.00005379611,0.00027757927,0.00036640183,0.00011746744],"domain_scores_gemma":[0.99546474,0.003430688,0.0001058826,0.00046021296,0.0003562017,0.0001823144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002789336,0.0009891324,0.0013660333,0.00138751,0.0021538055,0.0032130491,0.0020678442,0.0026627837,0.020259956],"category_scores_gemma":[0.014803951,0.00047487146,0.00085187634,0.0017991763,0.0046087517,0.012992895,0.003484845,0.007972442,0.0024011505],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042093892,0.000036467598,0.0001279892,0.00010622312,0.0000135393,0.000046741447,0.00016355465,0.0043782145,0.00020358138,0.93614644,0.013930386,0.044804752],"study_design_scores_gemma":[0.000004447105,0.000009614866,0.00006374022,0.000044777822,0.0000030185035,0.00005410429,0.000069135596,0.009379898,0.00014840679,0.9792698,0.010942007,0.000011090542],"about_ca_topic_score_codex":0.0030739915,"about_ca_topic_score_gemma":0.003404681,"teacher_disagreement_score":0.020259956,"about_ca_system_score_codex":0.0018613118,"about_ca_system_score_gemma":0.0010162455,"threshold_uncertainty_score":0.06777626},"labels":[],"label_agreement":null},{"id":"W3021747502","doi":"10.1007/978-3-030-59710-8_48","title":"Source-Relaxed Domain Adaptation for Image Segmentation","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":62,"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":"Segmentation; Prior probability; Adaptation (eye); Pattern recognition (psychology); Entropy (arrow of time); Image segmentation; Minification; Adversarial system; Divergence (linguistics)","score_opus":0.02280201284094537,"score_gpt":0.25200006118630547,"score_spread":0.2291980483453601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021747502","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":[0.0037386327,0.00056651107,0.9932554,0.00008565367,0.000050657796,0.000026216585,0.00010922078,0.0011874932,0.0009803581],"genre_scores_gemma":[0.17846325,0.0014589312,0.8012187,0.00045228822,0.00020782561,0.00019802248,0.0022969749,0.0012889705,0.014415052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995426,0.00013060607,0.000019639407,0.00016449268,0.00008980645,0.00005279857],"domain_scores_gemma":[0.9992101,0.00039717898,0.000028098773,0.00020120307,0.00012894925,0.000034400426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010693244,0.0010173004,0.0016010734,0.0007498508,0.00035802546,0.00087744865,0.0020290618,0.0019871444,0.0045979475],"category_scores_gemma":[0.0023731107,0.00068679947,0.0012253555,0.0012598742,0.0007936505,0.0016142443,0.0020535863,0.0024511628,0.0024896257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003736116,0.00015940053,0.00029228075,0.00036522694,0.00018083252,0.00013674475,0.00013092838,0.2603691,0.04522644,0.016920857,0.012599299,0.66324526],"study_design_scores_gemma":[0.000009658072,0.000028195498,0.00020108822,0.000013245825,0.000017261302,0.00008806283,0.000015011333,0.97424465,0.007224045,0.015796637,0.0023490826,0.0000129743385],"about_ca_topic_score_codex":0.0031821039,"about_ca_topic_score_gemma":0.0034984061,"teacher_disagreement_score":0.0045979475,"about_ca_system_score_codex":0.00057769107,"about_ca_system_score_gemma":0.0007849789,"threshold_uncertainty_score":0.015381694},"labels":[],"label_agreement":null},{"id":"W3022336006","doi":"10.1007/978-3-030-47358-7_3","title":"Investigating Relational Recurrent Neural Networks with Variable Length Memory Pointer","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Computer science; Pointer (user interface); Encoder; ENCODE; Sentence; Auxiliary memory; Artificial intelligence; Theoretical computer science; Computer hardware; Operating system","score_opus":0.025206797256334935,"score_gpt":0.22924896301974043,"score_spread":0.2040421657634055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022336006","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38292167,0.0013876357,0.6071549,0.00059894146,0.00013104503,0.00003995215,0.00010992412,0.0008531632,0.006802672],"genre_scores_gemma":[0.95567125,0.00030026978,0.03831241,0.00007368125,0.00004349823,0.00002547614,0.0001663057,0.00009869226,0.0053084185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978906,0.00006762632,0.000009978851,0.00006451811,0.000035566423,0.000033342458],"domain_scores_gemma":[0.9975084,0.0019200044,0.00013875231,0.00016426943,0.00020051458,0.000068052956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010225718,0.00043265274,0.00058607175,0.0002737535,0.00026681676,0.0010608161,0.0015573706,0.0011866762,0.0030479464],"category_scores_gemma":[0.0059621623,0.00045234602,0.0004232125,0.0003942861,0.00047524984,0.0026272347,0.00077237585,0.0010722087,0.00030056477],"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":[0.00036581326,0.0001926809,0.0028286828,0.00021542494,0.00018224401,0.00043744675,0.00022567748,0.7829284,0.022790575,0.10032321,0.002472001,0.08703786],"study_design_scores_gemma":[0.0000021968408,0.000015645952,0.00008161969,0.0000024522235,0.00000765849,0.000009008593,0.000010838699,0.99396616,0.0007902532,0.005032992,0.00007899875,0.0000021522264],"about_ca_topic_score_codex":0.0031519546,"about_ca_topic_score_gemma":0.002969609,"teacher_disagreement_score":0.0031519546,"about_ca_system_score_codex":0.00071622146,"about_ca_system_score_gemma":0.00034303532,"threshold_uncertainty_score":0.010196388},"labels":[],"label_agreement":null},{"id":"W3023191393","doi":"10.1007/s13218-020-00659-6","title":"Interactive Transfer Learning in Relational Domains","year":2020,"lang":"en","type":"article","venue":"KI - Künstliche Intelligenz","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Air Force Office of Scientific Research","keywords":"Transfer of learning; Computer science; Task (project management); Knowledge transfer; Inductive transfer; Salient; Process (computing); Domain (mathematical analysis); Transfer (computing); Space (punctuation); Artificial intelligence; Interface (matter); Transfer problem; Human–computer interaction; Machine learning; Knowledge management; Engineering; Robot learning; Mathematics","score_opus":0.04656369151163966,"score_gpt":0.27329853121486974,"score_spread":0.22673483970323008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023191393","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":[0.05332302,0.0005514107,0.941927,0.00025452985,0.000033569555,0.000038522565,0.00006123766,0.0011805642,0.0026302035],"genre_scores_gemma":[0.8599424,0.00034404345,0.13242719,0.0001608414,0.00005930583,0.0000971471,0.0003142084,0.00019914976,0.006455785],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991742,0.0003794302,0.000034986842,0.00023371802,0.000109812965,0.00006786265],"domain_scores_gemma":[0.99647707,0.0027911246,0.00007042654,0.0004119405,0.00014929075,0.0001001256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018684122,0.0006127511,0.0009971268,0.00057354174,0.00050209736,0.00096098665,0.0016919305,0.0013774841,0.0033901345],"category_scores_gemma":[0.0072075455,0.00043587896,0.00058956834,0.00058457104,0.0010233514,0.0042003663,0.0033387328,0.0018968876,0.00081202143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047870388,0.00042618733,0.0014031527,0.00030323176,0.00018415875,0.00027808227,0.00066776515,0.45603007,0.015103384,0.051185455,0.0034381817,0.47050166],"study_design_scores_gemma":[0.000010591187,0.000037233825,0.0002509881,0.000007443534,0.000009680177,0.00002607302,0.00004829558,0.9612101,0.0022986496,0.03562386,0.0004686299,0.000008539329],"about_ca_topic_score_codex":0.0026438874,"about_ca_topic_score_gemma":0.0027899726,"teacher_disagreement_score":0.0033901345,"about_ca_system_score_codex":0.0006340575,"about_ca_system_score_gemma":0.00042483298,"threshold_uncertainty_score":0.011341155},"labels":[],"label_agreement":null},{"id":"W3023528699","doi":"10.18653/v1/2021.eacl-main.39","title":"AdapterFusion: Non-Destructive Task Composition for Transfer Learning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Deutsche Forschungsgemeinschaft; Samsung; DeepMind; Samsung Advanced Institute of Technology","keywords":"Computer science; Forgetting; Multi-task learning; Exploit; Classifier (UML); Task (project management); Artificial intelligence; Machine learning; Source code; Transfer of learning","score_opus":0.02071584240950941,"score_gpt":0.25615040593734023,"score_spread":0.23543456352783082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023528699","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066091074,0.00040074298,0.9531208,0.00018483722,0.00020095622,0.00019057587,0.00040928557,0.036259692,0.0026239557],"genre_scores_gemma":[0.20060277,0.00041057324,0.7719931,0.0005967101,0.0002809525,0.0014157842,0.004350884,0.0077853245,0.012563996],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764097,0.00085072924,0.00015591379,0.00065259467,0.00041554254,0.00028422606],"domain_scores_gemma":[0.9957474,0.0014018395,0.00009197351,0.0021400764,0.00039578843,0.00022293652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004070128,0.002199168,0.001946116,0.001417491,0.0011392469,0.0018039468,0.0060473066,0.003166313,0.020867733],"category_scores_gemma":[0.01274412,0.0012385342,0.0014146798,0.0014353079,0.0012422262,0.005679403,0.01140202,0.003971919,0.015146019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011733572,0.00061326916,0.0009789085,0.00036726613,0.00023579568,0.000295148,0.00047738868,0.030879572,0.016869629,0.020811668,0.056726165,0.8705718],"study_design_scores_gemma":[0.0002670587,0.00030876443,0.0005363995,0.00006872542,0.000096987365,0.00027614756,0.00023291542,0.77449656,0.031249512,0.17058982,0.0218096,0.00006758577],"about_ca_topic_score_codex":0.0019214894,"about_ca_topic_score_gemma":0.0029162725,"teacher_disagreement_score":0.020867733,"about_ca_system_score_codex":0.0007392886,"about_ca_system_score_gemma":0.0014611273,"threshold_uncertainty_score":0.069809556},"labels":[],"label_agreement":null},{"id":"W3030364939","doi":"10.1109/tpami.2021.3057446","title":"A continual learning survey: Defying forgetting in classification tasks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1593,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"Huawei Technologies; Fonds Wetenschappelijk Onderzoek; Departament d'Innovació, Universitats i Empresa, Generalitat de Catalunya; Generalitat de Catalunya","keywords":"Forgetting; Computer science; Artificial intelligence; Machine learning; Task (project management); Artificial neural network; Task analysis; Cognitive psychology","score_opus":0.04391655240961902,"score_gpt":0.29554405147445184,"score_spread":0.25162749906483284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030364939","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051950198,0.24828824,0.67475724,0.005044659,0.0011462215,0.0003562361,0.00034448615,0.005570966,0.012541633],"genre_scores_gemma":[0.56290007,0.10082522,0.3138021,0.0028515789,0.002393612,0.0005160731,0.0015956911,0.0012736865,0.013842004],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981622,0.00043761884,0.00016771798,0.00068482594,0.00042672543,0.00012085665],"domain_scores_gemma":[0.9885445,0.006914604,0.00048600815,0.0021132946,0.0016150614,0.0003265404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054517305,0.0017186694,0.0016726675,0.0015383258,0.00078464614,0.0023815143,0.0056180116,0.00216272,0.0025873885],"category_scores_gemma":[0.019314606,0.0009331173,0.0010998459,0.0018126571,0.0017259497,0.006567425,0.0023456116,0.0039054037,0.0017839387],"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":[0.00023955372,0.00023768631,0.0025282705,0.0014952844,0.00015060358,0.000036128382,0.00024142746,0.030528205,0.0020285076,0.009295843,0.0087750675,0.9444435],"study_design_scores_gemma":[0.00015945762,0.0019559986,0.0066964435,0.0016265344,0.00038123757,0.0010242304,0.0004528899,0.80041754,0.017342439,0.079457186,0.09026116,0.00022487664],"about_ca_topic_score_codex":0.004677658,"about_ca_topic_score_gemma":0.0041577243,"teacher_disagreement_score":0.0056180116,"about_ca_system_score_codex":0.0014756542,"about_ca_system_score_gemma":0.0019282933,"threshold_uncertainty_score":0.02883184},"labels":[],"label_agreement":null},{"id":"W3034007088","doi":"10.3389/fnins.2021.633674","title":"Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing Its Gradient Estimator Bias","year":2021,"lang":"en","type":"preprint","venue":"Frontiers in Neuroscience","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"Samsung; European Research Council; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Estimator; MNIST database; Computer science; Artificial neural network; Scaling; Backpropagation; Cross entropy; Deep learning; Algorithm; Artificial intelligence; Applied mathematics; Mathematics; Pattern recognition (psychology); Statistics","score_opus":0.032900518954110884,"score_gpt":0.2678406362161545,"score_spread":0.2349401172620436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034007088","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053088546,0.00048400636,0.9356649,0.00049756916,0.0002213902,0.00006728802,0.0000988932,0.0047455723,0.0051318626],"genre_scores_gemma":[0.6729566,0.00040876685,0.31884444,0.0005087055,0.000173928,0.0001598571,0.00037004912,0.001034369,0.00554326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942786,0.00011523512,0.000037736118,0.00013687603,0.000205178,0.0000770588],"domain_scores_gemma":[0.99860364,0.00054638623,0.00011414593,0.00027687606,0.00038598597,0.000073064395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012355455,0.0016524979,0.00058994244,0.0007768317,0.00041832565,0.00090669567,0.0015586347,0.0011713771,0.003140275],"category_scores_gemma":[0.0071904613,0.00042465117,0.0005176569,0.0004988449,0.0009449435,0.0020679121,0.0022755717,0.001974913,0.0014121494],"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":[0.0003600846,0.00027937003,0.0022559303,0.00022426808,0.000110944384,0.0003203969,0.00022362972,0.5071264,0.045979954,0.05698645,0.0090365345,0.37709603],"study_design_scores_gemma":[0.00001672685,0.000051661566,0.00015741131,0.000011078232,0.000010196299,0.000037863112,0.0000075370162,0.9779991,0.009475973,0.010584947,0.0016377307,0.00000989404],"about_ca_topic_score_codex":0.0043972833,"about_ca_topic_score_gemma":0.0054754224,"teacher_disagreement_score":0.0043972833,"about_ca_system_score_codex":0.0010274617,"about_ca_system_score_gemma":0.0011597533,"threshold_uncertainty_score":0.010505259},"labels":[],"label_agreement":null},{"id":"W3034183291","doi":"10.1109/cvpr42600.2020.00423","title":"Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data","year":2020,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Graph; Semi-supervised learning; Machine learning; Embedding; Cluster analysis; Exploit; Supervised learning; Labeled data; Metric (unit); Pattern recognition (psychology); Theoretical computer science; Artificial neural network","score_opus":0.13280993422194762,"score_gpt":0.26790040868971526,"score_spread":0.13509047446776765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034183291","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031434614,0.0005065382,0.95632386,0.00039186765,0.00008584227,0.00026941017,0.0006365907,0.008320642,0.0020306574],"genre_scores_gemma":[0.3878028,0.00034894462,0.599979,0.0006086038,0.00010587081,0.00041070205,0.0048377765,0.00061859254,0.0052877436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997678,0.00075917534,0.000113031005,0.00073737465,0.00052612973,0.00018633831],"domain_scores_gemma":[0.99415284,0.0021719488,0.00042243648,0.002014371,0.0009934319,0.00024497832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032285445,0.0018565597,0.001947845,0.002348415,0.0010123358,0.0012511511,0.005408177,0.00232973,0.0020878152],"category_scores_gemma":[0.007599099,0.0008052786,0.0012706122,0.001920015,0.0019579988,0.0042941654,0.0025704633,0.0032854164,0.0012691922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044739354,0.00056843075,0.0035123122,0.00037808347,0.00023803984,0.00022988768,0.00028930668,0.34893647,0.007959805,0.015750699,0.021245433,0.6004442],"study_design_scores_gemma":[0.000016463964,0.000040671985,0.00014199535,0.000008388826,0.0000071981453,0.000031071486,0.000024538956,0.9871525,0.0021807987,0.009418585,0.0009674453,0.000010374408],"about_ca_topic_score_codex":0.00873463,"about_ca_topic_score_gemma":0.016203921,"teacher_disagreement_score":0.00873463,"about_ca_system_score_codex":0.001460392,"about_ca_system_score_gemma":0.002273903,"threshold_uncertainty_score":0.017367601},"labels":[],"label_agreement":null},{"id":"W3034272105","doi":"10.1109/cvpr42600.2020.00903","title":"Phase Consistent Ecological Domain Adaptation","year":2020,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Artificial intelligence; Leverage (statistics); Segmentation; Domain adaptation; Classifier (UML); Machine learning; Pattern recognition (psychology); Transfer of learning; Image segmentation","score_opus":0.07326520512078818,"score_gpt":0.285387394862656,"score_spread":0.21212218974186783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034272105","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":[0.020563187,0.00026114256,0.9719225,0.00038166964,0.00009537311,0.00009541923,0.0002593375,0.0016792944,0.0047420096],"genre_scores_gemma":[0.5434158,0.0003240812,0.44060236,0.0012696921,0.00016675096,0.00048188632,0.0019680012,0.00095455727,0.010816789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990901,0.00021032519,0.000036286234,0.00033969263,0.00020567495,0.00011786175],"domain_scores_gemma":[0.99858594,0.00051948574,0.000090818794,0.00039785504,0.00029845914,0.00010751571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017390861,0.0011250817,0.0010336422,0.0007978331,0.0006804963,0.0014856509,0.0027056695,0.0020438966,0.0043503945],"category_scores_gemma":[0.0063422774,0.0005033315,0.001150796,0.00079141796,0.0014375097,0.002412881,0.0036782657,0.0028623315,0.0016395404],"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":[0.00026515138,0.00034168086,0.0025844006,0.0001953963,0.00014545802,0.00019262396,0.00022619506,0.69677484,0.022199715,0.040663194,0.01195327,0.2244581],"study_design_scores_gemma":[0.00001834667,0.00003731398,0.0003524829,0.000011376258,0.0000114558,0.00006556681,0.000028952109,0.9664511,0.0037724818,0.025890127,0.0033447337,0.000016123744],"about_ca_topic_score_codex":0.0035400367,"about_ca_topic_score_gemma":0.005007031,"teacher_disagreement_score":0.0043503945,"about_ca_system_score_codex":0.0010795749,"about_ca_system_score_gemma":0.0016382922,"threshold_uncertainty_score":0.014553547},"labels":[],"label_agreement":null},{"id":"W3034352268","doi":"10.48550/arxiv.1911.09704","title":"A Conceptual Framework for Lifelong Learning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Forgetting; Lifelong learning; Computer science; Variety (cybernetics); Transfer of learning; Perspective (graphical); Mechanism (biology); Task (project management); Inductive transfer; Cognitive science; Conceptual framework; Artificial intelligence; Transfer of training; Knowledge management; Human–computer interaction; Robot learning; Cognitive psychology; Epistemology; Psychology; Engineering","score_opus":0.10036883599678133,"score_gpt":0.21468489746236608,"score_spread":0.11431606146558475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034352268","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049857725,0.0019894184,0.9700707,0.0035817863,0.0001228801,0.00009831767,0.00019220737,0.0002306836,0.018728126],"genre_scores_gemma":[0.41435662,0.0029353707,0.5636883,0.001657795,0.00063469977,0.001177038,0.0006927779,0.00021144819,0.0146459285],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986737,0.00052824174,0.00009052117,0.0003981595,0.00020480313,0.00010473936],"domain_scores_gemma":[0.99686086,0.0013600556,0.00033190998,0.0005355185,0.00045241055,0.0004592649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030162998,0.00076896825,0.0004800956,0.0020200345,0.0013992471,0.003114832,0.0029121656,0.0020340811,0.005904432],"category_scores_gemma":[0.005497436,0.00038253362,0.0007827948,0.0014857705,0.006706061,0.008769686,0.0032095874,0.0028758133,0.0011214569],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005205435,0.000015258183,0.00018282453,0.00005079585,0.0000070035694,0.000035065837,0.0002991651,0.0029534788,0.00013510534,0.9852107,0.0009935694,0.010111784],"study_design_scores_gemma":[0.000007594312,0.000024468281,0.00014319853,0.000038447783,0.0000050606827,0.000075406075,0.00011785112,0.017531503,0.00012272985,0.95911944,0.022803301,0.000011000493],"about_ca_topic_score_codex":0.0025226525,"about_ca_topic_score_gemma":0.002119835,"teacher_disagreement_score":0.005904432,"about_ca_system_score_codex":0.0019504543,"about_ca_system_score_gemma":0.0015943083,"threshold_uncertainty_score":0.019752324},"labels":[],"label_agreement":null},{"id":"W3034408737","doi":"10.48550/arxiv.1911.08019","title":"Online Learned Continual Compression with Adaptive Quantization Modules","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":38,"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":"Computer science; Encoder; Quantization (signal processing); Data compression; USable; Artificial intelligence; Reinforcement learning; Machine learning; Algorithm; Multimedia","score_opus":0.06811625650054115,"score_gpt":0.18960398670101425,"score_spread":0.1214877302004731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034408737","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":[0.06911877,0.001103535,0.92365,0.00056754076,0.000103041566,0.000100532394,0.00026499035,0.002993432,0.0020981352],"genre_scores_gemma":[0.7992065,0.00030802173,0.19554876,0.00043432147,0.000111791014,0.00019001517,0.0006326531,0.00013750148,0.0034304732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949825,0.000111566835,0.000029877641,0.00017092637,0.00013295519,0.000056338828],"domain_scores_gemma":[0.9980563,0.0009401885,0.00015961732,0.00048242018,0.0002574154,0.00010395258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014664717,0.0007772923,0.0010576285,0.00059245666,0.00032222294,0.0007829529,0.002375835,0.0010944261,0.0019021734],"category_scores_gemma":[0.0061430563,0.00039742806,0.00037540353,0.0007752244,0.0012445013,0.0025793335,0.0019110278,0.0018294096,0.000556095],"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":[0.00037173214,0.0003496957,0.0027370183,0.0001645911,0.00008813962,0.00015350756,0.00016147953,0.5657422,0.00747925,0.013649515,0.0047873333,0.40431556],"study_design_scores_gemma":[0.000015809786,0.000051283638,0.00016917623,0.000008400431,0.0000058708647,0.00003397331,0.000008712196,0.99056983,0.0019843085,0.0067344955,0.0004107342,0.000007375761],"about_ca_topic_score_codex":0.002591921,"about_ca_topic_score_gemma":0.0028524282,"teacher_disagreement_score":0.002591921,"about_ca_system_score_codex":0.0007747248,"about_ca_system_score_gemma":0.0009966794,"threshold_uncertainty_score":0.007755518},"labels":[],"label_agreement":null},{"id":"W3034445880","doi":"10.18653/v1/2020.acl-main.102","title":"Dynamic Memory Induction Networks for Few-Shot Text Classification","year":2020,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Generalization; Artificial intelligence; Process (computing); Adaptive routing; Machine learning; Mechanism (biology); Dynamic random-access memory; Routing (electronic design automation); Routing protocol","score_opus":0.06928187749799837,"score_gpt":0.28392253478549784,"score_spread":0.21464065728749948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034445880","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":[0.0572226,0.0028327066,0.9335497,0.00047302677,0.00018898782,0.00012462404,0.0003929978,0.0024928772,0.002722524],"genre_scores_gemma":[0.8209732,0.0011807134,0.16483912,0.0006631981,0.00029281504,0.0002992805,0.0021841282,0.00018582342,0.009381652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994943,0.00010885307,0.000032459564,0.00019328509,0.00009907801,0.0000719481],"domain_scores_gemma":[0.99885964,0.0006067166,0.00011370513,0.00016790048,0.00019494988,0.000057054804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090790435,0.0010005134,0.0011921052,0.0012825527,0.0005895262,0.0008851248,0.0027274084,0.0013676904,0.0020641878],"category_scores_gemma":[0.0030398401,0.00040008745,0.00071118935,0.0012100956,0.0005982122,0.0030903749,0.0012176833,0.002180655,0.0011202332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004233332,0.00039096884,0.0027478612,0.00023571213,0.00012122003,0.00019007655,0.00022065802,0.2302283,0.010691812,0.010355372,0.00649201,0.73790264],"study_design_scores_gemma":[0.0000049975924,0.000056141587,0.00023435937,0.000013310122,0.000017460121,0.000035516685,0.000018717385,0.98809355,0.0024498007,0.008343672,0.0007234684,0.000009077085],"about_ca_topic_score_codex":0.0032934193,"about_ca_topic_score_gemma":0.004177405,"teacher_disagreement_score":0.0032934193,"about_ca_system_score_codex":0.00095228286,"about_ca_system_score_gemma":0.00061837526,"threshold_uncertainty_score":0.0069093704},"labels":[],"label_agreement":null},{"id":"W3034806614","doi":"","title":"ConQUR: Mitigating Delusional Bias in Deep Q-Learning","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Google (Canada); University of Alberta","funders":"","keywords":"Computer science; Class (philosophy); Variety (cybernetics); Scheme (mathematics); Delusion; Q-learning; Artificial intelligence; Simple (philosophy); Value (mathematics); Machine learning; Reinforcement learning; Mathematics; Psychology","score_opus":0.10880761885277714,"score_gpt":0.18882587792775857,"score_spread":0.08001825907498143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034806614","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018950574,0.00041219796,0.9780044,0.00051317795,0.000055920376,0.00007913883,0.00004058076,0.0006181618,0.0013258045],"genre_scores_gemma":[0.71360654,0.0003231246,0.28037515,0.0010368935,0.00012390755,0.00030767336,0.00019511154,0.00028475316,0.0037468367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99657035,0.0019400013,0.00013995283,0.00048511432,0.0006308514,0.0002337726],"domain_scores_gemma":[0.9845989,0.011036219,0.0008647517,0.0018816143,0.0011808161,0.00043777598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010116149,0.0011465352,0.0020061287,0.00062544714,0.0008624763,0.0013518127,0.0031132572,0.002128114,0.0040145693],"category_scores_gemma":[0.03199128,0.00082151307,0.00058288756,0.0007050843,0.0026629774,0.0036577014,0.0041036867,0.0042229304,0.0006889937],"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":[0.00062880124,0.00039421042,0.0036257487,0.00030075817,0.00015214088,0.0001996649,0.00044235555,0.6702828,0.0037313679,0.07837159,0.007152955,0.23471756],"study_design_scores_gemma":[0.000038593877,0.00006987802,0.00010716382,0.000017001748,0.000008061583,0.000024053994,0.000011868408,0.9742474,0.00083969143,0.024055636,0.0005720142,0.000008633784],"about_ca_topic_score_codex":0.00348725,"about_ca_topic_score_gemma":0.004097189,"teacher_disagreement_score":0.010116149,"about_ca_system_score_codex":0.0014243346,"about_ca_system_score_gemma":0.0026594691,"threshold_uncertainty_score":0.053499937},"labels":[],"label_agreement":null},{"id":"W3035153354","doi":"","title":"Domain Aggregation Networks for Multi-Source Domain Adaptation","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"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":"Computer science; Exploit; Domain (mathematical analysis); Generalization; Domain adaptation; Set (abstract data type); Artificial intelligence; Machine learning; Adaptation (eye); Data mining; Theoretical computer science; Mathematics","score_opus":0.10922560275916117,"score_gpt":0.1986025491779733,"score_spread":0.08937694641881212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035153354","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":[0.023756128,0.00072134257,0.97246516,0.0002644649,0.000065459164,0.00006232667,0.00011209903,0.0010383218,0.0015147268],"genre_scores_gemma":[0.68712115,0.00064208877,0.30507457,0.0006673678,0.00015357534,0.00031066893,0.001169832,0.00026715826,0.004593533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924976,0.0002689509,0.000030675652,0.0002728751,0.000114628674,0.000063063126],"domain_scores_gemma":[0.998161,0.0010275507,0.00011448031,0.000354008,0.00026087012,0.00008209973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021958004,0.0012104419,0.0010604337,0.0009563541,0.000586478,0.0008102429,0.0018092257,0.0013698485,0.0015938734],"category_scores_gemma":[0.0055643152,0.000538133,0.00080524216,0.0010262082,0.00091962155,0.002628312,0.0022176385,0.0028814343,0.0007316857],"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":[0.00024127567,0.0002877948,0.0024074577,0.00014875864,0.00018192461,0.00020399826,0.00023793463,0.63961154,0.008479029,0.016800273,0.00692932,0.32447064],"study_design_scores_gemma":[0.0000049352743,0.000017709192,0.00015971185,0.00000617004,0.0000078831545,0.00002576198,0.000016229835,0.9885105,0.0011332906,0.009353068,0.0007584927,0.000006179981],"about_ca_topic_score_codex":0.0035084581,"about_ca_topic_score_gemma":0.0043759397,"teacher_disagreement_score":0.0035084581,"about_ca_system_score_codex":0.0010636081,"about_ca_system_score_gemma":0.0007218991,"threshold_uncertainty_score":0.011612654},"labels":[],"label_agreement":null},{"id":"W3035272520","doi":"10.1109/cvpr42600.2020.00395","title":"Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data","year":2020,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Transfer of learning; Artificial intelligence; Domain (mathematical analysis); Machine learning; Segmentation; Data modeling; Search engine; Deep learning; Object (grammar); Data mining; Information retrieval; Database","score_opus":0.15098657240403532,"score_gpt":0.32107475403939345,"score_spread":0.17008818163535813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035272520","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02005654,0.0017042173,0.5038739,0.0007348684,0.00027796993,0.0008697747,0.018127633,0.44491422,0.0094408775],"genre_scores_gemma":[0.2911387,0.0010963564,0.5735332,0.0009701298,0.00018103758,0.0015846497,0.09494491,0.022076588,0.014474453],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819535,0.00029905915,0.000194477,0.0004535027,0.0007061371,0.0001515195],"domain_scores_gemma":[0.9964226,0.0013018573,0.00014818684,0.0012451356,0.00057062594,0.00031146503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027602937,0.0026153503,0.0018641842,0.0025555221,0.0006841436,0.0017396268,0.005931699,0.0019357225,0.02190104],"category_scores_gemma":[0.0117858825,0.0012636093,0.0011331216,0.0040692776,0.0008199106,0.0058061094,0.0047451165,0.0024248785,0.014814422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003030261,0.0010414233,0.004668778,0.0016007958,0.00071674335,0.0008833153,0.00032755247,0.047561623,0.022551918,0.018314933,0.44636166,0.45294097],"study_design_scores_gemma":[0.00063566584,0.00020191951,0.0012221474,0.00005413717,0.000058808848,0.00026968072,0.00012946075,0.90018797,0.0247299,0.025882956,0.046526857,0.000100444464],"about_ca_topic_score_codex":0.0068036835,"about_ca_topic_score_gemma":0.009706547,"teacher_disagreement_score":0.02190104,"about_ca_system_score_codex":0.0017419287,"about_ca_system_score_gemma":0.0018622999,"threshold_uncertainty_score":0.07326627},"labels":[],"label_agreement":null},{"id":"W3035302051","doi":"10.48550/arxiv.2006.15486","title":"Laplacian Regularized Few-Shot Learning","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Inference; Unary operation; Pairwise comparison; Artificial intelligence; Graph; Theoretical computer science; Machine learning; Mathematics","score_opus":0.10387841716670673,"score_gpt":0.17663104281949835,"score_spread":0.07275262565279161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035302051","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008287412,0.00019546786,0.9888342,0.0002174629,0.000037342892,0.00007069877,0.00012208584,0.0013818315,0.00085351255],"genre_scores_gemma":[0.5364701,0.00036396176,0.45073268,0.0012773136,0.0002855563,0.00046177724,0.002166958,0.0006827886,0.0075588883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983341,0.00040592052,0.00006260147,0.0006369713,0.0003987015,0.0001617658],"domain_scores_gemma":[0.99743325,0.0013085036,0.0001616152,0.00049163826,0.00043212046,0.0001729896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023272254,0.0015328944,0.0026977602,0.0014596914,0.000828747,0.0016640716,0.006261221,0.002407418,0.0033400329],"category_scores_gemma":[0.009325709,0.0009542575,0.0013466368,0.0014813951,0.0020077636,0.004557606,0.0031884462,0.0039281654,0.0013456156],"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":[0.0002865947,0.00054441905,0.0016289763,0.00032256436,0.0002047214,0.00014447987,0.00030560352,0.64451456,0.010220804,0.028101498,0.011101924,0.30262384],"study_design_scores_gemma":[0.000009579235,0.000028485758,0.000076708355,0.0000055287396,0.0000066282396,0.000015633965,0.000010155263,0.9851259,0.0008653239,0.013500742,0.00034659184,0.000008772311],"about_ca_topic_score_codex":0.0063953954,"about_ca_topic_score_gemma":0.0074407407,"teacher_disagreement_score":0.0063953954,"about_ca_system_score_codex":0.0016670934,"about_ca_system_score_gemma":0.0017219589,"threshold_uncertainty_score":0.012716353},"labels":[],"label_agreement":null},{"id":"W3036186419","doi":"10.1016/j.patcog.2021.107943","title":"Discriminative feature alignment: Improving transferability of unsupervised domain adaptation by Gaussian-guided latent alignment","year":2021,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Mitacs","keywords":"Computer science; Artificial intelligence; Feature vector; Feature (linguistics); Pattern recognition (psychology); Discriminative model; Classifier (UML); Inference; Gaussian; Domain (mathematical analysis); Latent variable; Machine learning; Mathematics","score_opus":0.03770863878710193,"score_gpt":0.2516976495158295,"score_spread":0.21398901072872759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036186419","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":[0.014067751,0.00022311066,0.98347175,0.00006326359,0.00003825902,0.00002354356,0.00006576284,0.0015086355,0.000537807],"genre_scores_gemma":[0.5304163,0.00036626877,0.46241942,0.00035064606,0.00011236495,0.00015181824,0.0011547207,0.00078968005,0.0042387755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989599,0.00032287248,0.000045249195,0.0003515592,0.00019829093,0.00012208769],"domain_scores_gemma":[0.9980465,0.0008250578,0.0001299335,0.00061937165,0.000257194,0.00012188106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012398125,0.001044287,0.0015936958,0.0008367622,0.0005002681,0.0006594559,0.0020810915,0.0012666169,0.0020369715],"category_scores_gemma":[0.0046773343,0.00047280642,0.0008643331,0.0014200481,0.0009029791,0.0021388738,0.0027950876,0.002189623,0.0013347161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007303603,0.00053908577,0.0018494785,0.0001740551,0.00021295743,0.00017139716,0.00025085744,0.23926193,0.054913566,0.015971264,0.008633315,0.6772917],"study_design_scores_gemma":[0.000017716926,0.000044284734,0.00028527566,0.000004439444,0.000012608036,0.000037464124,0.000016444308,0.98750407,0.005107385,0.0063912007,0.0005679089,0.000011259566],"about_ca_topic_score_codex":0.004614861,"about_ca_topic_score_gemma":0.0068639084,"teacher_disagreement_score":0.004614861,"about_ca_system_score_codex":0.00048145346,"about_ca_system_score_gemma":0.0010972082,"threshold_uncertainty_score":0.009176016},"labels":[],"label_agreement":null},{"id":"W3036461888","doi":"","title":"Improving Few-Shot Visual Classification with Unlabelled Examples","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Mahalanobis distance; Machine learning; Feature (linguistics); Cluster analysis; Shot (pellet); Test set; Training set; Set (abstract data type); One shot","score_opus":0.09894198425784734,"score_gpt":0.19913977007469602,"score_spread":0.10019778581684868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036461888","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":[0.13132372,0.0018225919,0.8506641,0.0004731834,0.00028513066,0.0002733451,0.0006375886,0.010359577,0.0041607833],"genre_scores_gemma":[0.6592948,0.00034160024,0.3297825,0.0006827156,0.00016149017,0.00022232422,0.003756537,0.0005357662,0.005222179],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975967,0.0005428247,0.000107645574,0.00097442116,0.00058824726,0.00019030595],"domain_scores_gemma":[0.9953257,0.0017441023,0.00030003933,0.0012690037,0.0011072997,0.0002538102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002529867,0.0019702644,0.0023182074,0.002098132,0.0007712976,0.0020106349,0.0050988332,0.0028431532,0.0020883686],"category_scores_gemma":[0.00938057,0.00055774243,0.0012809016,0.0012658794,0.0012329768,0.005106893,0.0026092352,0.0031641496,0.0019340741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006823106,0.0013209024,0.0058213337,0.00047791708,0.00027962818,0.00027094252,0.0003338748,0.11471246,0.039786123,0.0035404472,0.011752125,0.821022],"study_design_scores_gemma":[0.000024560472,0.00020618606,0.00086165965,0.000031056552,0.00003918125,0.00013941537,0.00009591743,0.9710161,0.018217728,0.007957051,0.001380315,0.000030728123],"about_ca_topic_score_codex":0.0032374677,"about_ca_topic_score_gemma":0.0051254574,"teacher_disagreement_score":0.0050988332,"about_ca_system_score_codex":0.001189349,"about_ca_system_score_gemma":0.0008349204,"threshold_uncertainty_score":0.013379395},"labels":[],"label_agreement":null},{"id":"W3037473509","doi":"10.48550/arxiv.2004.12209","title":"Convex Representation Learning for Generalized Invariance in\\n Semi-Inner-Product Space","year":2020,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Regular polygon; Representation (politics); Euclidean geometry; Generality; Invariant (physics); Mathematical optimization; Euclidean space; Kernel (algebra); Regularization (linguistics); Applied mathematics; Algebra over a field; Computer science; Artificial intelligence; Pure mathematics","score_opus":0.14835030635173452,"score_gpt":0.23191442394183984,"score_spread":0.08356411759010532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037473509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006583591,0.000111528556,0.9921346,0.00012079382,0.000017843819,0.00001217494,0.000029702753,0.00011752575,0.0008722893],"genre_scores_gemma":[0.60660684,0.0008995731,0.38305545,0.00035220195,0.0002680472,0.00023193703,0.00083729887,0.00036829826,0.0073802494],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99905473,0.00037383047,0.00004094742,0.00028343403,0.00017751926,0.00006948752],"domain_scores_gemma":[0.997877,0.0010995778,0.00020695328,0.0004692701,0.00022089978,0.00012622359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017077608,0.00093325507,0.00093965646,0.0005939218,0.00037123373,0.0011595273,0.0013325702,0.0008951017,0.0026064683],"category_scores_gemma":[0.0072589694,0.00036701828,0.000851005,0.00066740834,0.0022949174,0.0031766633,0.0024701003,0.002919659,0.00069494924],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009869321,0.00009701348,0.0007618194,0.00020115504,0.00007282846,0.00011882939,0.00019209202,0.30758312,0.0070390194,0.57751566,0.005632937,0.100686826],"study_design_scores_gemma":[0.000003218284,0.00002590507,0.00010616056,0.000005533595,0.0000033038414,0.000018963638,0.000008806279,0.88368744,0.00077640545,0.114676,0.00068146025,0.0000068832883],"about_ca_topic_score_codex":0.0015918101,"about_ca_topic_score_gemma":0.0013903381,"teacher_disagreement_score":0.0026064683,"about_ca_system_score_codex":0.0010393204,"about_ca_system_score_gemma":0.0007594335,"threshold_uncertainty_score":0.009031594},"labels":[],"label_agreement":null},{"id":"W3037660226","doi":"10.65109/yiph3635","title":"Maximizing Information Gain in Partially Observable Environments via Prediction Rewards","year":2020,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"European Commission","keywords":"MNIST database; Computer science; Reinforcement learning; Artificial intelligence; Action selection; Inference; Entropy (arrow of time); Function (biology); Realizability; Machine learning; Deep learning; Perception; Algorithm","score_opus":0.026207216369879573,"score_gpt":0.20586967049137025,"score_spread":0.17966245412149068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037660226","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":[0.06806193,0.0005912221,0.9247776,0.001419008,0.00005185226,0.00005175869,0.000113058275,0.00042958002,0.004503841],"genre_scores_gemma":[0.9368631,0.00037181503,0.059573706,0.0002285253,0.00006270007,0.00009709,0.00009755611,0.000090378286,0.0026151466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988865,0.0004331737,0.000050242874,0.00025995096,0.00021975447,0.00015045483],"domain_scores_gemma":[0.9916849,0.0068916637,0.0004823004,0.00035330266,0.00034044933,0.00024737924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021707243,0.001215337,0.0012550075,0.0005408334,0.00050358847,0.0016042052,0.001417998,0.0015806096,0.0017781588],"category_scores_gemma":[0.013628629,0.0007737362,0.00045705447,0.0004981302,0.0019836116,0.0037236542,0.0024361925,0.0022303637,0.00027900204],"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":[0.00013939143,0.00007910038,0.0010666664,0.00010636884,0.000052760042,0.000109222456,0.00014105969,0.90384346,0.0019212037,0.060814496,0.0011904035,0.030535894],"study_design_scores_gemma":[0.000009901646,0.000027190512,0.00015038176,0.000009461787,0.0000060572875,0.000012902941,0.000008430284,0.95268047,0.00045289937,0.04644094,0.00019374145,0.0000075191533],"about_ca_topic_score_codex":0.0030527667,"about_ca_topic_score_gemma":0.0035333768,"teacher_disagreement_score":0.0030527667,"about_ca_system_score_codex":0.0017926798,"about_ca_system_score_gemma":0.0013545023,"threshold_uncertainty_score":0.013006866},"labels":[],"label_agreement":null},{"id":"W3039645412","doi":"10.48550/arxiv.2007.01126","title":"A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning","year":2020,"lang":"en","type":"review","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Task (project management); Multi-task learning; Computer science; Generalization; Artificial intelligence; Machine learning; Deep learning; Engineering; Mathematics","score_opus":0.08042694558528264,"score_gpt":0.23422666252783214,"score_spread":0.1537997169425495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039645412","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":[0.00077719503,0.94094896,0.04703782,0.0018588307,0.0009736535,0.000041105202,0.00023523618,0.00026157487,0.007865652],"genre_scores_gemma":[0.010968637,0.9570088,0.022789136,0.0015177851,0.0018365184,0.00009866709,0.00076649996,0.000086944456,0.0049270215],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996965,0.00006159291,0.000033958597,0.000082041486,0.000099710735,0.000026212958],"domain_scores_gemma":[0.99898976,0.0006385431,0.000052560783,0.000054840642,0.00021445728,0.000049865113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010345845,0.0012092481,0.0010492872,0.0016348119,0.00027370418,0.0010275955,0.0014766249,0.0014290885,0.0051465305],"category_scores_gemma":[0.0026433393,0.00054228364,0.00057047093,0.003009708,0.00050445565,0.0024867603,0.0009237353,0.0019846892,0.0040674577],"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":[0.00004892209,0.000100081474,0.0003613702,0.007004446,0.000106466694,0.00008578042,0.00004969696,0.0050530657,0.0010097383,0.019495446,0.06339288,0.9032922],"study_design_scores_gemma":[0.000026598778,0.00018368725,0.0011993115,0.0030925681,0.0001702227,0.00087985,0.0000612596,0.015694464,0.0020497052,0.03824887,0.9383135,0.00007992806],"about_ca_topic_score_codex":0.0020881789,"about_ca_topic_score_gemma":0.0023268545,"teacher_disagreement_score":0.0051465305,"about_ca_system_score_codex":0.00077518483,"about_ca_system_score_gemma":0.0017990711,"threshold_uncertainty_score":0.017216861},"labels":[],"label_agreement":null},{"id":"W3041767058","doi":"10.48550/arxiv.2007.05683","title":"Batch-level Experience Replay with Review for Continual Learning","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","score_opus":0.18075097318939326,"score_gpt":0.22599199129761974,"score_spread":0.04524101810822648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041767058","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16791354,0.005916439,0.78898543,0.0019503183,0.0011594828,0.00082671066,0.0031672872,0.0211678,0.008913048],"genre_scores_gemma":[0.7739922,0.00071399607,0.20437287,0.0010831723,0.00039392736,0.0006517477,0.00586736,0.0010909959,0.01183369],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974069,0.000855642,0.0001240126,0.00095354323,0.0004888907,0.00017101454],"domain_scores_gemma":[0.993958,0.0023571537,0.0004282145,0.0020180666,0.0008074191,0.00043118157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042136568,0.0013250307,0.0013588229,0.00081859087,0.00071023946,0.001454808,0.0031011028,0.001946644,0.004958244],"category_scores_gemma":[0.020192496,0.000520007,0.0008150957,0.00069787516,0.0009920341,0.0034792207,0.0033832076,0.002995988,0.0029971802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002278921,0.0011877607,0.009022187,0.0012092206,0.00049179955,0.00035365814,0.0009152213,0.11562309,0.02569448,0.008554895,0.07628634,0.75838244],"study_design_scores_gemma":[0.00016754787,0.0011032622,0.0037368557,0.00011380478,0.000103103914,0.00034713084,0.00024653465,0.9335136,0.015379956,0.021863902,0.023303509,0.00012072637],"about_ca_topic_score_codex":0.0027579055,"about_ca_topic_score_gemma":0.005035624,"teacher_disagreement_score":0.004958244,"about_ca_system_score_codex":0.0009584274,"about_ca_system_score_gemma":0.0010763253,"threshold_uncertainty_score":0.02228421},"labels":[],"label_agreement":null},{"id":"W3042725540","doi":"10.48550/arxiv.2007.07011","title":"Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Forgetting; Lifelong learning; Computer science; Train; Task (project management); Reuse; Process (computing); Function (biology); Variety (cybernetics); Training (meteorology); Control (management); Artificial intelligence; Machine learning; Cognitive psychology; Economics; Political science; Engineering; Psychology; Management","score_opus":0.15636278028194522,"score_gpt":0.23472163230334553,"score_spread":0.0783588520214003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042725540","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":[0.016155086,0.00056543,0.9798995,0.0001712636,0.000067764144,0.00004722617,0.00003893594,0.0018459886,0.0012088037],"genre_scores_gemma":[0.6670573,0.00041895683,0.32759166,0.00038856498,0.00009358247,0.00020569019,0.00030933935,0.0004874329,0.0034475615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995561,0.00012321604,0.000028936036,0.00013852297,0.00009752575,0.000055729244],"domain_scores_gemma":[0.9982998,0.0009686201,0.0001342881,0.00029302784,0.00020808492,0.000096206524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014281778,0.0010804979,0.0012083132,0.00056872034,0.000437354,0.00075564644,0.0013025803,0.0012997722,0.0032568676],"category_scores_gemma":[0.008032189,0.0006180565,0.00048737793,0.00043342507,0.0010802689,0.0021106487,0.0012322483,0.0022196749,0.0010617257],"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":[0.00019771759,0.00019860771,0.0018220724,0.00016751853,0.000071804076,0.00011730691,0.00019125671,0.72148126,0.0053644055,0.021478357,0.004903978,0.24400578],"study_design_scores_gemma":[0.000011094715,0.00002783076,0.000071686205,0.00000984061,0.0000036868482,0.00001978796,0.0000057633547,0.99134666,0.00096454954,0.00691007,0.00062365714,0.0000053145372],"about_ca_topic_score_codex":0.004018379,"about_ca_topic_score_gemma":0.0045695524,"teacher_disagreement_score":0.004018379,"about_ca_system_score_codex":0.0007616364,"about_ca_system_score_gemma":0.0013405815,"threshold_uncertainty_score":0.010895252},"labels":[],"label_agreement":null},{"id":"W3043064029","doi":"10.1109/wacv48630.2021.00138","title":"Unsupervised Multi-Target Domain Adaptation Through Knowledge Distillation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Genetec (Canada); École de Technologie Supérieure","funders":"","keywords":"Domain (mathematical analysis); Computer science; Adaptation (eye); Artificial intelligence; Domain adaptation; Process (computing); Domain knowledge; Machine learning; Labeled data; Pattern recognition (psychology); Mathematics; Psychology","score_opus":0.07176694587146469,"score_gpt":0.3045953406013121,"score_spread":0.2328283947298474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043064029","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":[0.051814504,0.0012988097,0.9352011,0.0003816884,0.0001527615,0.00011407468,0.00042028213,0.006750843,0.0038659244],"genre_scores_gemma":[0.6677925,0.0006113248,0.31931224,0.00076245127,0.00009930795,0.00026162088,0.0027765625,0.00048527337,0.007898877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994192,0.00011610458,0.00002443652,0.00027665883,0.0000884982,0.00007514685],"domain_scores_gemma":[0.99886,0.000441706,0.000096443655,0.00031586186,0.00021464977,0.00007134963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088296254,0.0016155428,0.0010960022,0.0008457022,0.00051263254,0.0009181722,0.0023783408,0.001530412,0.0019912755],"category_scores_gemma":[0.00261796,0.0006195129,0.0014169397,0.0009448981,0.000964719,0.0021868683,0.0021214131,0.0030058345,0.0011902846],"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":[0.00019137809,0.00025942962,0.002514794,0.00020847308,0.00016326125,0.0002607599,0.00020855779,0.49189782,0.01869875,0.0051870495,0.008152786,0.472257],"study_design_scores_gemma":[0.000009600457,0.00004130978,0.00027447497,0.000011010149,0.000015995445,0.000058431116,0.000024059893,0.98922825,0.004652595,0.0038598576,0.0018122034,0.000012170747],"about_ca_topic_score_codex":0.0071527376,"about_ca_topic_score_gemma":0.011542433,"teacher_disagreement_score":0.0071527376,"about_ca_system_score_codex":0.0008700561,"about_ca_system_score_gemma":0.0013566783,"threshold_uncertainty_score":0.014222205},"labels":[],"label_agreement":null},{"id":"W3046037042","doi":"","title":"Beyond H-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence.","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Divergence (linguistics); Marginal distribution; Domain (mathematical analysis); USable; Matching (statistics); Computer science; Kullback–Leibler divergence; Perspective (graphical); Chain rule (probability); Mathematics; Information theory; Upper and lower bounds; Theoretical computer science; Mathematical optimization; Artificial intelligence; Statistics; Random variable; Regular conditional probability; Posterior probability; Bayesian probability; Mathematical analysis","score_opus":0.06600459547504262,"score_gpt":0.1885956435310878,"score_spread":0.12259104805604518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046037042","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054823114,0.0007407116,0.98711747,0.0010520143,0.00007046775,0.000037070244,0.00006398125,0.00011582004,0.0053200913],"genre_scores_gemma":[0.72064686,0.0021531521,0.26477736,0.0018895499,0.00060072885,0.00045905294,0.00038907165,0.0003618884,0.008722322],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99603695,0.0019916634,0.00014805337,0.00059980317,0.0010134758,0.00021011464],"domain_scores_gemma":[0.9831618,0.012679554,0.0008519813,0.0020392973,0.00080508646,0.00046237782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008749349,0.0012766856,0.0013316066,0.0014273816,0.0010676917,0.0031940797,0.0027961656,0.0027550547,0.0027703466],"category_scores_gemma":[0.033588376,0.00064425636,0.0011215835,0.0013384726,0.0059196632,0.0062114033,0.0064133853,0.0073206676,0.0006205193],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005411377,0.000059579786,0.0011729702,0.00015603066,0.00008539507,0.00012039305,0.00022439462,0.18230954,0.0013599343,0.7784467,0.003166221,0.03284481],"study_design_scores_gemma":[0.0000071323475,0.00003526429,0.0002997646,0.00003326951,0.000009848808,0.00006719709,0.000030079438,0.4694222,0.00071805797,0.5276812,0.0016783645,0.000017679282],"about_ca_topic_score_codex":0.0020019342,"about_ca_topic_score_gemma":0.0015031963,"teacher_disagreement_score":0.008749349,"about_ca_system_score_codex":0.0028434235,"about_ca_system_score_gemma":0.0020359678,"threshold_uncertainty_score":0.046271563},"labels":[],"label_agreement":null},{"id":"W3046220680","doi":"10.3758/s13421-020-01074-w","title":"Category similarity affects study choices in self-regulated learning","year":2020,"lang":"en","type":"article","venue":"Memory & Cognition","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Categorization; Psychology; Similarity (geometry); Set (abstract data type); Cognitive psychology; Concept learning; Interleaving; Social psychology; Artificial intelligence; Computer science","score_opus":0.029137998001383843,"score_gpt":0.25712138219247493,"score_spread":0.2279833841910911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046220680","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99876535,0.000026317457,0.00017528473,0.000030171215,0.0000041263174,0.000009867654,0.000018509687,0.00000901581,0.00096131954],"genre_scores_gemma":[0.99878305,0.000014797029,0.0003239101,0.00003509349,0.000003156523,0.00001413043,0.000054517757,0.000011643222,0.000759764],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980527,0.00074564636,0.00015699235,0.00045528924,0.0004317411,0.00015757403],"domain_scores_gemma":[0.967161,0.019607387,0.0053263367,0.0029663402,0.0013466935,0.0035922562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031439206,0.00019722432,0.00049092824,0.0007489498,0.0004521441,0.0021963886,0.0006388942,0.0012891128,0.0045734495],"category_scores_gemma":[0.028580027,0.00038010115,0.00028758193,0.00040413017,0.00066837994,0.0014326457,0.0011336829,0.0012627352,0.00054734363],"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":[0.01379252,0.010922817,0.85028756,0.00012417436,0.00048315656,0.0004971571,0.0033201212,0.0028518895,0.06912564,0.0028127418,0.0010205761,0.044761665],"study_design_scores_gemma":[0.0002256102,0.0018833647,0.9827808,0.000013206955,0.00007527985,0.00024915847,0.0006825855,0.0063811736,0.003213736,0.0038588245,0.0005864471,0.000049816488],"about_ca_topic_score_codex":0.0012229997,"about_ca_topic_score_gemma":0.001565742,"teacher_disagreement_score":0.0045734495,"about_ca_system_score_codex":0.000435902,"about_ca_system_score_gemma":0.00040499718,"threshold_uncertainty_score":0.016626835},"labels":[],"label_agreement":null},{"id":"W3046558900","doi":"10.5539/cis.v13n3p93","title":"Riemannian Proximal Policy Optimization","year":2020,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Mathematical optimization; Markov decision process; Optimization problem; Convergence (economics); Upper and lower bounds; Space (punctuation); Process (computing); Applied mathematics; Mathematics; Markov process; Statistics","score_opus":0.015522271976225802,"score_gpt":0.24237921245583754,"score_spread":0.22685694047961175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046558900","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":[0.0015192189,0.00013965735,0.99683064,0.00011719328,0.000031735373,0.00002272359,0.000015242706,0.00019062699,0.0011330282],"genre_scores_gemma":[0.35797647,0.00090360834,0.6280558,0.00071371696,0.00019555863,0.0004247006,0.00033693947,0.0005439136,0.010849294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987186,0.0005085002,0.000059008908,0.00027792202,0.00032848306,0.00010740824],"domain_scores_gemma":[0.9983582,0.0009181718,0.0001159372,0.00015912978,0.0003180388,0.00013059594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019085883,0.0015256425,0.0021543948,0.00086845586,0.00064834947,0.0010145719,0.0016555184,0.0019287667,0.004103673],"category_scores_gemma":[0.006495226,0.0007535406,0.0010649266,0.000734428,0.0017002376,0.0016956974,0.0026056888,0.0023634399,0.0014017004],"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":[0.000077682904,0.00006451265,0.00033644738,0.00016971504,0.00007603029,0.000099396246,0.000085304964,0.82742584,0.0018764955,0.10919106,0.0039198697,0.05667762],"study_design_scores_gemma":[0.00000802623,0.000024616049,0.00003454125,0.0000063600337,0.000004905807,0.00001828929,0.0000037482462,0.9780116,0.00032294315,0.020500239,0.0010578597,0.0000068452327],"about_ca_topic_score_codex":0.0036575764,"about_ca_topic_score_gemma":0.002390497,"teacher_disagreement_score":0.004103673,"about_ca_system_score_codex":0.0013702739,"about_ca_system_score_gemma":0.002496356,"threshold_uncertainty_score":0.013728142},"labels":[],"label_agreement":null},{"id":"W3049227448","doi":"10.3390/app10165631","title":"Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":27,"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":"National Research Foundation of Korea","keywords":"Hebbian theory; Transfer of learning; Leabra; Computer science; Artificial intelligence; Competitive learning; Machine learning; Discriminative model; Unsupervised learning; Multi-task learning; Learning rule; Convolutional neural network; Inductive transfer; Pattern recognition (psychology); Artificial neural network; Task (project management); Robot learning; Wake-sleep algorithm","score_opus":0.031925447075101324,"score_gpt":0.26086827127573653,"score_spread":0.22894282420063522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049227448","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.115193,0.00084688497,0.87480915,0.0002572845,0.00008303238,0.000109517176,0.00004506284,0.002049145,0.0066069295],"genre_scores_gemma":[0.89057434,0.00032924904,0.10495791,0.00018483278,0.000042141386,0.00011807286,0.00014258668,0.00017316402,0.0034776663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919385,0.00011259485,0.00004986831,0.00023731086,0.00026353833,0.00014271121],"domain_scores_gemma":[0.9983619,0.00052812044,0.0001206788,0.00050849357,0.00038082668,0.000099997495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015690147,0.0011628302,0.001280451,0.0009204223,0.0006609046,0.0011413036,0.0019194996,0.0011284293,0.0031630052],"category_scores_gemma":[0.003705353,0.00032066676,0.00069276115,0.00085057906,0.0009856274,0.004172514,0.0018503149,0.0014380031,0.0010138791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005559822,0.0006934272,0.0016172422,0.00019247794,0.00020356338,0.00030861693,0.0001798235,0.4494985,0.068957075,0.01673798,0.002848569,0.45820665],"study_design_scores_gemma":[0.000035639783,0.00013463084,0.00045752246,0.0000057832835,0.000027963824,0.00005862562,0.00002614993,0.96505815,0.020735854,0.012356895,0.0010868692,0.000015796655],"about_ca_topic_score_codex":0.002163772,"about_ca_topic_score_gemma":0.0015096649,"teacher_disagreement_score":0.0031630052,"about_ca_system_score_codex":0.00094494625,"about_ca_system_score_gemma":0.00093965954,"threshold_uncertainty_score":0.010581315},"labels":[],"label_agreement":null},{"id":"W3080269789","doi":"10.24963/kr.2020/87","title":"Ontology-guided Semantic Composition for Zero-shot Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"Thales (Canada)","funders":"Samsung; Norges Forskningsråd; Siemens; Engineering and Physical Sciences Research Council; University of Oxford","keywords":"Ontology; Computer science; Embedding; Semantics (computer science); Class (philosophy); OWL-S; Natural language processing; Information retrieval; Artificial intelligence; Shot (pellet); Ontology learning; Semantic Web; Web Ontology Language; Zero (linguistics); Upper ontology; Semantic Web Stack; Suggested Upper Merged Ontology; Programming language; Linguistics","score_opus":0.0803430304813903,"score_gpt":0.29873817487725923,"score_spread":0.21839514439586893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080269789","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012429423,0.00017089583,0.9852642,0.00011010109,0.000024378296,0.00006491704,0.00009192064,0.0011102882,0.00073394395],"genre_scores_gemma":[0.48219943,0.00035357586,0.5111238,0.00046281502,0.00008494305,0.00025692247,0.002034487,0.00035742926,0.0031266704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982529,0.00041867193,0.0001081749,0.000596956,0.00046681045,0.00015642658],"domain_scores_gemma":[0.9985312,0.0005952618,0.00009083833,0.00041592924,0.0002453276,0.00012136966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021330055,0.00087094185,0.001345788,0.001780043,0.0010641392,0.0011344666,0.002798592,0.0012887237,0.0026600978],"category_scores_gemma":[0.004596832,0.0004914639,0.0013656314,0.001292405,0.0017601395,0.005214723,0.0043487037,0.002241443,0.0007249242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039668282,0.00076202257,0.0028477635,0.0004697643,0.00017706642,0.00026030105,0.0009191601,0.09491799,0.026686024,0.091935836,0.006064184,0.7745633],"study_design_scores_gemma":[0.000021498625,0.00007721711,0.00040199823,0.00002361716,0.000037374277,0.000093119146,0.00014995903,0.8843849,0.008591872,0.10314256,0.003053175,0.000022763294],"about_ca_topic_score_codex":0.004326596,"about_ca_topic_score_gemma":0.0064788642,"teacher_disagreement_score":0.004326596,"about_ca_system_score_codex":0.0013488149,"about_ca_system_score_gemma":0.0017270731,"threshold_uncertainty_score":0.011280596},"labels":[],"label_agreement":null},{"id":"W3086607822","doi":"","title":"'Less Than One'-Shot Learning: Learning N Classes From M<N Samples","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":27,"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 Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Classifier (UML); Machine learning; Generalization; One shot; Shot (pellet); Artificial neural network; Robustness (evolution); Class (philosophy); Task (project management); Training set; Single shot; Pattern recognition (psychology); Mathematics; Engineering","score_opus":0.26163007942385463,"score_gpt":0.22222942578337831,"score_spread":0.03940065364047632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086607822","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08866384,0.00079987926,0.9042112,0.0014826655,0.00007814093,0.00023203145,0.00020305206,0.00085655734,0.0034726108],"genre_scores_gemma":[0.7507359,0.000358785,0.24114671,0.0009848168,0.00014162879,0.00034500365,0.0009179089,0.00018858307,0.005180709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774724,0.0006726262,0.00012870842,0.0008168813,0.0004050305,0.00022950447],"domain_scores_gemma":[0.99107295,0.0062019336,0.0004563685,0.0012477688,0.0004773817,0.00054351095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040845186,0.0012485167,0.0025768578,0.0008871023,0.0012982275,0.002221563,0.0043349895,0.0033183142,0.0032662367],"category_scores_gemma":[0.020117693,0.00079847954,0.0011480433,0.00073984225,0.002977141,0.0069724335,0.0045090113,0.003949288,0.0007052046],"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":[0.0013826983,0.0007644433,0.0062846355,0.0006492692,0.00030358427,0.0003459425,0.0006379477,0.5121882,0.009722418,0.087467216,0.007971427,0.37228215],"study_design_scores_gemma":[0.000026889313,0.00010772356,0.0005966105,0.000025740312,0.00001807268,0.00008108077,0.00006290084,0.92570233,0.0029904596,0.06977683,0.0005903121,0.00002102703],"about_ca_topic_score_codex":0.00511584,"about_ca_topic_score_gemma":0.0042217476,"teacher_disagreement_score":0.00511584,"about_ca_system_score_codex":0.002463954,"about_ca_system_score_gemma":0.0012858632,"threshold_uncertainty_score":0.0216012},"labels":[],"label_agreement":null},{"id":"W3087059466","doi":"10.1016/j.artint.2021.103635","title":"CVPR 2020 continual learning in computer vision competition: Approaches, results, current challenges and future directions","year":2021,"lang":"en","type":"preprint","venue":"Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"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; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Benchmarking; Computer science; Forgetting; Artificial intelligence; Benchmark (surveying); Task (project management); Field (mathematics); Machine learning; Deep learning; Set (abstract data type); Competition (biology); Engineering","score_opus":0.09943231038043623,"score_gpt":0.3070483565818663,"score_spread":0.20761604620143007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087059466","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14304656,0.08019651,0.45020428,0.04724785,0.04499657,0.0049183914,0.047421917,0.06014966,0.12181833],"genre_scores_gemma":[0.34948257,0.007277405,0.32416174,0.009204373,0.0066082017,0.0018857331,0.18444967,0.0065534036,0.110376865],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9890442,0.0044274908,0.00027143426,0.0020125376,0.0029185775,0.0013257514],"domain_scores_gemma":[0.99026555,0.0022147559,0.00015020062,0.0017852652,0.0033060624,0.0022781491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021368409,0.004923008,0.0060760593,0.0030778765,0.0030565972,0.0062183947,0.009340271,0.007448711,0.0164873],"category_scores_gemma":[0.018035015,0.0009643284,0.0015760829,0.002932596,0.0023802007,0.005159971,0.0068039154,0.009505277,0.011026126],"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":[0.0017137635,0.002175294,0.0010314594,0.0011272508,0.0007868871,0.00014275147,0.000077536424,0.025415422,0.0030976231,0.008367759,0.60966635,0.3463979],"study_design_scores_gemma":[0.0015698337,0.002655295,0.01030838,0.00045222376,0.0004202461,0.00073830294,0.0004612636,0.6651233,0.012996941,0.06524191,0.23971784,0.00031447675],"about_ca_topic_score_codex":0.035462797,"about_ca_topic_score_gemma":0.042547014,"teacher_disagreement_score":0.035462797,"about_ca_system_score_codex":0.0029505573,"about_ca_system_score_gemma":0.005051881,"threshold_uncertainty_score":0.11300832},"labels":[],"label_agreement":null},{"id":"W3087148478","doi":"","title":"Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data","year":2020,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Multi-task learning; Computer science; Overfitting; Forgetting; Artificial intelligence; Transfer of learning; Task (project management); Machine learning; Benchmark (surveying); Transformer; Artificial neural network","score_opus":0.07018382920870495,"score_gpt":0.2793119769554278,"score_spread":0.20912814774672286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087148478","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.089684,0.0009318038,0.8969762,0.0006014301,0.0001558805,0.00017742097,0.0002497526,0.007896235,0.0033272223],"genre_scores_gemma":[0.7855244,0.00042072957,0.20485325,0.0008194004,0.00013346705,0.00043574863,0.0012893917,0.00059219403,0.005931408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991786,0.00024378703,0.000042378142,0.00028013377,0.00014266698,0.000112341615],"domain_scores_gemma":[0.99761045,0.00108726,0.00014908414,0.000634253,0.0003596077,0.0001592411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024340684,0.0016203321,0.0010544066,0.00061882107,0.0005158984,0.0009038537,0.0035744146,0.0017917047,0.0034718863],"category_scores_gemma":[0.008784476,0.00057884987,0.0011318701,0.0009105548,0.001017658,0.004499687,0.0033984338,0.0037359544,0.001342581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005481652,0.00079373084,0.00238872,0.0002475418,0.00020887396,0.0002891654,0.00031009162,0.47800118,0.016351718,0.0064555313,0.007496583,0.4869087],"study_design_scores_gemma":[0.000020287778,0.00006799946,0.00020702375,0.0000067608803,0.000018215283,0.00002435685,0.000012873062,0.9916433,0.0022876256,0.0051506693,0.00055131805,0.000009666686],"about_ca_topic_score_codex":0.006600709,"about_ca_topic_score_gemma":0.0058704508,"teacher_disagreement_score":0.006600709,"about_ca_system_score_codex":0.0009943879,"about_ca_system_score_gemma":0.0015005593,"threshold_uncertainty_score":0.013124585},"labels":[],"label_agreement":null},{"id":"W3088471706","doi":"10.48550/arxiv.2009.12658","title":"Domain Generalization via Semi-supervised Meta Learning","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer science; Centroid; Generalization; Leverage (statistics); Semi-supervised learning; Machine learning; Pattern recognition (psychology); Domain (mathematical analysis); Benchmark (surveying); Supervised learning; Mathematics; Artificial neural network","score_opus":0.11109503485511314,"score_gpt":0.1965172022499819,"score_spread":0.08542216739486876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088471706","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018024636,0.00035013547,0.97935927,0.00015798993,0.000023997616,0.000055321918,0.00011170464,0.0012117836,0.000705037],"genre_scores_gemma":[0.63498735,0.0003850017,0.35973597,0.0004604243,0.00011089455,0.00032901057,0.0014769831,0.0002520058,0.0022623998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982551,0.00067418424,0.000086674976,0.0006368012,0.00025708196,0.00009019883],"domain_scores_gemma":[0.9958852,0.0017646652,0.0004209676,0.0013477298,0.0004280651,0.0001533033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027238268,0.0014177117,0.0016996808,0.0016868431,0.0005624402,0.0011231824,0.002896442,0.0014964539,0.0010475562],"category_scores_gemma":[0.0056728953,0.0007052675,0.0018827888,0.0013080761,0.001556276,0.00303611,0.0028293221,0.0024673643,0.00065049867],"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":[0.00021495947,0.00025276883,0.0032760466,0.00021934575,0.00032558048,0.0001925666,0.00027909604,0.6657363,0.007125972,0.010254174,0.0041804803,0.3079427],"study_design_scores_gemma":[0.0000089985715,0.000038801547,0.00016583115,0.000012662216,0.000014402445,0.00004153662,0.000020072313,0.9859912,0.0014187576,0.011793955,0.00048367912,0.000010109437],"about_ca_topic_score_codex":0.0015115385,"about_ca_topic_score_gemma":0.0024687648,"teacher_disagreement_score":0.002896442,"about_ca_system_score_codex":0.0010802725,"about_ca_system_score_gemma":0.0009609762,"threshold_uncertainty_score":0.014405131},"labels":[],"label_agreement":null},{"id":"W3089098255","doi":"10.1109/access.2020.3026684","title":"Semantic Segmentation Using a GAN and a Weakly Supervised Method Based on Deep Transfer Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":13,"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":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Upsampling; Artificial intelligence; Computer science; Segmentation; Transfer of learning; Generalization; Pattern recognition (psychology); Deep learning; Bilinear interpolation; Deconvolution; Image segmentation; Pooling; Computer vision; Image (mathematics); Algorithm; Mathematics","score_opus":0.06924967432766485,"score_gpt":0.33158491474376667,"score_spread":0.2623352404161018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089098255","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":[0.011221181,0.00009231506,0.98639107,0.00009856405,0.000027943686,0.00003604984,0.000039388582,0.0008692206,0.0012242998],"genre_scores_gemma":[0.63346505,0.00023590797,0.35955027,0.00030848425,0.0000800241,0.0001691929,0.00051739265,0.00029288186,0.0053808456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999619,0.000093603914,0.000015944419,0.00014748491,0.000078051475,0.000045875084],"domain_scores_gemma":[0.99971217,0.00008498057,0.000037122467,0.00008392191,0.000055131786,0.000026624011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064252625,0.0010724242,0.00079499016,0.0006082211,0.00032570402,0.0005487176,0.0012645628,0.00094262895,0.0015047044],"category_scores_gemma":[0.0011278308,0.00040676538,0.0010261015,0.00051404344,0.0008771757,0.0013388806,0.0010797493,0.0015020674,0.00049132475],"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":[0.00019515783,0.00015351942,0.0010770073,0.00009110118,0.00010336158,0.00017197376,0.0001462472,0.6836608,0.035677012,0.021893505,0.002870193,0.2539601],"study_design_scores_gemma":[0.0000023919008,0.00001863412,0.000077321696,0.0000024420187,0.0000044438525,0.000022616137,0.0000039920615,0.9935807,0.0023925318,0.0035605843,0.00033003162,0.0000043617842],"about_ca_topic_score_codex":0.0024516669,"about_ca_topic_score_gemma":0.0034328403,"teacher_disagreement_score":0.0024516669,"about_ca_system_score_codex":0.0008116563,"about_ca_system_score_gemma":0.0007687858,"threshold_uncertainty_score":0.0058889985},"labels":[],"label_agreement":null},{"id":"W3090492056","doi":"10.1109/ijcnn48605.2020.9206989","title":"Joint progressive knowledge distillation and unsupervised domain adaptation","year":2020,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Genetec (Canada); École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Domain adaptation; Domain (mathematical analysis); Divergence (linguistics); Machine learning; Pattern recognition (psychology); Data mining; Classifier (UML)","score_opus":0.03377469339052964,"score_gpt":0.2582732862549615,"score_spread":0.22449859286443186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090492056","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":[0.06024796,0.0011524955,0.9286476,0.00043087362,0.0000998759,0.00013964652,0.00033790644,0.005096917,0.0038468335],"genre_scores_gemma":[0.7236659,0.0005848939,0.26656887,0.00053236686,0.00011984796,0.0002627176,0.0015595154,0.00029218572,0.006413715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999453,0.00012998872,0.000034049906,0.00019793112,0.00011119279,0.00007383163],"domain_scores_gemma":[0.99871445,0.00048429848,0.00011253642,0.0004735207,0.00016418987,0.000051011666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093620655,0.0013010665,0.0010097572,0.0007616335,0.0003430471,0.0008019506,0.0017304841,0.0011287867,0.0020175905],"category_scores_gemma":[0.0033010289,0.00036260925,0.00097066845,0.00095781713,0.0011063811,0.0022770988,0.002086394,0.0021155626,0.00092034956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021608782,0.00026229394,0.0014673715,0.00018166179,0.000101967096,0.00015126057,0.00017176101,0.3967615,0.013556073,0.011042195,0.0058731115,0.5702147],"study_design_scores_gemma":[0.000014830726,0.000050806404,0.00024278244,0.00001057842,0.00001559378,0.0000615597,0.00002451127,0.9826142,0.0070077074,0.0078542065,0.0020895929,0.000013518218],"about_ca_topic_score_codex":0.00439139,"about_ca_topic_score_gemma":0.0062051723,"teacher_disagreement_score":0.00439139,"about_ca_system_score_codex":0.0006967753,"about_ca_system_score_gemma":0.0011756608,"threshold_uncertainty_score":0.008731663},"labels":[],"label_agreement":null},{"id":"W3091553978","doi":"10.1109/tgrs.2020.3022608","title":"Unifying Top–Down Views by Task-Specific Domain Adaptation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"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 British Columbia","funders":"Qatar National Research Fund","keywords":"Computer science; Task (project management); Adaptation (eye); Artificial intelligence; Domain (mathematical analysis); Domain adaptation; Representation (politics); Task analysis; Baseline (sea); Process (computing); Machine learning","score_opus":0.03867010356365063,"score_gpt":0.24706503444369304,"score_spread":0.2083949308800424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091553978","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":[0.015518184,0.00079471694,0.9795293,0.00015729236,0.000112665286,0.00009104825,0.00037317784,0.0018395692,0.001583975],"genre_scores_gemma":[0.3644053,0.0015125682,0.61946464,0.0007155861,0.00032487346,0.0002686859,0.0047912695,0.00065010367,0.007866941],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932814,0.00013691929,0.00002582642,0.0003262299,0.00010636856,0.00007645726],"domain_scores_gemma":[0.99919194,0.00022822527,0.000067303576,0.00030879967,0.0001391093,0.000064558706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084098754,0.0020901433,0.0014174343,0.0009011475,0.00039955802,0.0011607556,0.0015081656,0.0010150379,0.0024506126],"category_scores_gemma":[0.0022702613,0.0004862832,0.0017729679,0.0011616455,0.00070318364,0.0019972469,0.001721518,0.0026388178,0.0016965364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038989531,0.00040031254,0.002496014,0.00037882559,0.0003681823,0.0002608582,0.00023935002,0.119851604,0.074783444,0.007832171,0.016161887,0.77683735],"study_design_scores_gemma":[0.000023340295,0.00012060641,0.0016299818,0.000029900504,0.00009618626,0.00018192208,0.0001025373,0.95839804,0.017595958,0.014273558,0.0075031137,0.000044867014],"about_ca_topic_score_codex":0.004228784,"about_ca_topic_score_gemma":0.0049119345,"teacher_disagreement_score":0.004228784,"about_ca_system_score_codex":0.0003985735,"about_ca_system_score_gemma":0.00073420163,"threshold_uncertainty_score":0.008408308},"labels":[],"label_agreement":null},{"id":"W3091905774","doi":"","title":"Siamese Neural Networks for One-shot Image Recognition","year":2015,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3254,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Convolutional neural network; Machine learning; Deep learning; Class (philosophy); Process (computing); Pattern recognition (psychology); Artificial neural network; Similarity (geometry); Shot (pellet); Feature learning; Image (mathematics)","score_opus":0.16045069847547405,"score_gpt":0.31388452337812006,"score_spread":0.153433824902646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091905774","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011047082,0.0018725349,0.9818141,0.0004758487,0.000084317166,0.00005303504,0.00013891213,0.0012678509,0.0032463681],"genre_scores_gemma":[0.49850008,0.0025563731,0.48155394,0.00039935604,0.00023953764,0.000250137,0.0009816486,0.00020298085,0.015315906],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996768,0.000070319606,0.000017004837,0.0001125376,0.000094772215,0.000028594462],"domain_scores_gemma":[0.9993594,0.00025645693,0.000057916797,0.0001778337,0.00010846204,0.000040020237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078121416,0.0006083408,0.00066089164,0.000579712,0.00031616792,0.0008217009,0.0011828718,0.0011156083,0.0039800256],"category_scores_gemma":[0.0026396373,0.00035043276,0.00042952484,0.00083278224,0.00086240564,0.0019253176,0.0010583245,0.0021098063,0.0013768827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014555671,0.00015582469,0.00086689607,0.00020980126,0.00008325436,0.00012213197,0.00009287438,0.38759503,0.014942972,0.10123197,0.010846881,0.4837068],"study_design_scores_gemma":[0.0000043076056,0.000022714485,0.0001830708,0.000006221289,0.0000043547184,0.000026159252,0.0000056155422,0.95483583,0.0018336874,0.041046437,0.0020227127,0.000008940211],"about_ca_topic_score_codex":0.005079979,"about_ca_topic_score_gemma":0.0067284363,"teacher_disagreement_score":0.005079979,"about_ca_system_score_codex":0.000916477,"about_ca_system_score_gemma":0.0007591405,"threshold_uncertainty_score":0.013314486},"labels":[],"label_agreement":null},{"id":"W3092787839","doi":"","title":"Exchanging Lessons Between Algorithmic Fairness and Domain Generalization","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Generalization; Computer science; Disjoint sets; Task (project management); Machine learning; Artificial intelligence; Domain (mathematical analysis); Theoretical computer science; Mathematics","score_opus":0.0885948343235872,"score_gpt":0.20868386327263555,"score_spread":0.12008902894904835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092787839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033509027,0.00056374,0.9591329,0.0022878712,0.00013716856,0.000058666847,0.00006036789,0.00031926637,0.003930968],"genre_scores_gemma":[0.7678545,0.00056040526,0.22570197,0.0015640098,0.00045116653,0.00023037882,0.00019897318,0.00031648437,0.0031221188],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9892869,0.005811467,0.00039282435,0.0024049415,0.0015607936,0.0005429687],"domain_scores_gemma":[0.9462775,0.035253458,0.0020530168,0.013000151,0.0021458138,0.001270019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019964794,0.0013747435,0.001784552,0.0009812729,0.0016451104,0.0030298494,0.0026864738,0.0026340268,0.0022400536],"category_scores_gemma":[0.08440052,0.000608804,0.0008840717,0.00090205256,0.0058209724,0.007985112,0.006691714,0.0054743323,0.0006927978],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052156573,0.00026856485,0.008597118,0.00026061604,0.00022045049,0.00018960952,0.000846184,0.37621117,0.003919507,0.44124517,0.0055693192,0.16215064],"study_design_scores_gemma":[0.000040639457,0.00009975062,0.0006684514,0.000043770666,0.000020972055,0.00008892826,0.000081146354,0.47723097,0.0023203378,0.5175035,0.0018722848,0.000029142064],"about_ca_topic_score_codex":0.0015513151,"about_ca_topic_score_gemma":0.0018831106,"teacher_disagreement_score":0.019964794,"about_ca_system_score_codex":0.0018161754,"about_ca_system_score_gemma":0.002807757,"threshold_uncertainty_score":0.10558522},"labels":[],"label_agreement":null},{"id":"W3093671052","doi":"10.48550/arxiv.2010.11924","title":"In Search of Robust Measures of Generalization","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Université de Montréal; Université Laval","funders":"","keywords":"Generalization; Robustness (evolution); Computer science; Generalization error; VC dimension; Artificial intelligence; Artificial neural network; Machine learning; Econometrics; Mathematics","score_opus":0.2157159118589783,"score_gpt":0.21664967303283703,"score_spread":0.0009337611738587381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093671052","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03743448,0.0039411876,0.9488292,0.0026335747,0.00016124293,0.000102696446,0.000497877,0.000674425,0.0057253237],"genre_scores_gemma":[0.81514883,0.0030785445,0.17381641,0.0019290667,0.00080780033,0.000647658,0.0014362894,0.0006325172,0.0025028486],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9860248,0.005339613,0.001011122,0.003744873,0.003342861,0.00053681753],"domain_scores_gemma":[0.8942927,0.06981902,0.009522809,0.02126768,0.0038130253,0.0012848015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027394429,0.0022352715,0.0025058493,0.004974163,0.0011471743,0.0037526444,0.0040379376,0.003951883,0.0021611545],"category_scores_gemma":[0.14429651,0.0010219389,0.0020538892,0.002944425,0.008692928,0.013156667,0.006912412,0.00684288,0.0005896897],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023216598,0.0001227353,0.012302083,0.0008039619,0.0010065546,0.00016515887,0.0006000922,0.3896791,0.0023053628,0.50191474,0.0038180633,0.08705001],"study_design_scores_gemma":[0.000015721214,0.0001401366,0.0029488623,0.00014733804,0.000058115344,0.00010038607,0.00007843608,0.32712293,0.0012221691,0.6660938,0.0020151355,0.000056827386],"about_ca_topic_score_codex":0.0016969871,"about_ca_topic_score_gemma":0.0010036367,"teacher_disagreement_score":0.027394429,"about_ca_system_score_codex":0.0029839284,"about_ca_system_score_gemma":0.0015373168,"threshold_uncertainty_score":0.14487731},"labels":[],"label_agreement":null},{"id":"W3094116343","doi":"10.1007/s10994-023-06337-6","title":"The role of mutual information in variational classifiers","year":2023,"lang":"en","type":"article","venue":"Machine Learning","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"École de Technologie Supérieure","funders":"H2020 Marie Skłodowska-Curie Actions; Universidad de Buenos Aires; Consejo Nacional de Investigaciones Científicas y Técnicas","keywords":"Information bottleneck method; Mutual information; Overfitting; Artificial intelligence; Regularization (linguistics); Computer science; Mathematics; Generalization; Kullback–Leibler divergence; Early stopping; MNIST database; Entropy (arrow of time); Inference; Algorithm; Machine learning; Artificial neural network","score_opus":0.00828382254537237,"score_gpt":0.22553121530298656,"score_spread":0.21724739275761418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094116343","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018504823,0.0017510166,0.9760801,0.0015005404,0.00009501188,0.000031254505,0.000065569286,0.000102694525,0.0018688815],"genre_scores_gemma":[0.7888543,0.0031941438,0.1975719,0.0010971547,0.0010840779,0.00026501878,0.0003766396,0.00041752553,0.007139189],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99495924,0.0031858704,0.00018948613,0.0008537255,0.00061676954,0.00019489792],"domain_scores_gemma":[0.95357394,0.041277014,0.0012033839,0.0020291356,0.00128993,0.000626591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01132337,0.0009309164,0.0025873294,0.0016806106,0.0013585667,0.0028686544,0.0040133153,0.0039317454,0.0018917972],"category_scores_gemma":[0.048293676,0.0016850727,0.0013517534,0.001334216,0.005508015,0.009407007,0.004487767,0.004549993,0.0002634442],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018963209,0.00011383065,0.0019121885,0.00027982396,0.00026202743,0.00010647475,0.0003283166,0.2634072,0.0015113902,0.6791852,0.002379453,0.050324343],"study_design_scores_gemma":[0.000010940623,0.00002817006,0.00031816526,0.000025855355,0.000020031039,0.00003868367,0.00001831785,0.6813104,0.00031884792,0.3173572,0.0005271211,0.000026206199],"about_ca_topic_score_codex":0.003144103,"about_ca_topic_score_gemma":0.002804412,"teacher_disagreement_score":0.01132337,"about_ca_system_score_codex":0.0017061619,"about_ca_system_score_gemma":0.001247751,"threshold_uncertainty_score":0.05988449},"labels":[],"label_agreement":null},{"id":"W3097943992","doi":"10.1016/j.media.2021.102146","title":"Self-paced and self-consistent co-training for semi-supervised image segmentation","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"National Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and Informatization; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Segmentation; Robustness (evolution); Cross entropy; Image segmentation; Machine learning; Pattern recognition (psychology); Entropy (arrow of time); Training set; Deep neural networks; Artificial neural network; Deep learning","score_opus":0.02099580457286689,"score_gpt":0.2998354642452361,"score_spread":0.27883965967236923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097943992","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01843966,0.0004795835,0.97865045,0.00008893715,0.000046595713,0.000050250306,0.00006483329,0.0016970604,0.00048266086],"genre_scores_gemma":[0.5046578,0.00038470677,0.48824826,0.0003684708,0.00012441678,0.0002278156,0.00081327703,0.00072058954,0.004454725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902594,0.000264759,0.000055529545,0.00037868292,0.0001659375,0.000109272274],"domain_scores_gemma":[0.9970849,0.0016067328,0.00022591179,0.0004621604,0.00048096987,0.00013929361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020825595,0.000881591,0.001797628,0.00091042684,0.00053677656,0.0008724765,0.0024791474,0.0021424224,0.0014556798],"category_scores_gemma":[0.0050129257,0.0007501263,0.0009404339,0.0010599232,0.0009225488,0.0014283764,0.0019832756,0.0017168534,0.00088242133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078267447,0.00046337227,0.0014066312,0.0003571704,0.0002460135,0.0001916697,0.00029268005,0.31729004,0.05636334,0.0033495633,0.0065361112,0.6127208],"study_design_scores_gemma":[0.0000060297916,0.000029783423,0.00026681487,0.000005939474,0.000010735681,0.000048493886,0.00001015277,0.9931304,0.005061024,0.0010894138,0.00033448948,0.0000067617652],"about_ca_topic_score_codex":0.003413564,"about_ca_topic_score_gemma":0.005407036,"teacher_disagreement_score":0.003413564,"about_ca_system_score_codex":0.0004728328,"about_ca_system_score_gemma":0.0011647291,"threshold_uncertainty_score":0.011013746},"labels":[],"label_agreement":null},{"id":"W3098535516","doi":"10.1016/j.media.2021.102038","title":"SoftSeg: Advantages of soft versus binary training for image segmentation","year":2021,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Canada First Research Excellence Fund; Canada Research Chairs; Canada Foundation for Innovation; Nvidia","keywords":"Artificial intelligence; Segmentation; Computer science; Pattern recognition (psychology); Voxel; Preprocessor; Binary classification; Image segmentation; Binary number; Ground truth; Pixel; Mathematics; Support vector machine","score_opus":0.04295141484576424,"score_gpt":0.35170314741180464,"score_spread":0.3087517325660404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098535516","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":[0.02617947,0.000744801,0.96797156,0.00032533883,0.000074622614,0.00005892594,0.0001435445,0.0029435912,0.0015582306],"genre_scores_gemma":[0.34064066,0.00078286295,0.64999783,0.00042009228,0.00019109232,0.00010267685,0.0009008525,0.0013036348,0.005660386],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931765,0.00020552667,0.000035395828,0.00020119039,0.00017579728,0.00006441129],"domain_scores_gemma":[0.99779785,0.0012615033,0.00008821941,0.0004175062,0.00031906785,0.00011595094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020283659,0.0010102586,0.0012966724,0.0011926617,0.0004989454,0.0015941301,0.001538525,0.0024316253,0.003863213],"category_scores_gemma":[0.0063373474,0.0005129796,0.00067515176,0.0008191484,0.0009492456,0.0021187982,0.0021531181,0.0018465135,0.0013760594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010504717,0.00034537233,0.0011352848,0.00030951475,0.00015439157,0.00010254248,0.00013208104,0.14307395,0.05165881,0.011075491,0.0048621744,0.7860999],"study_design_scores_gemma":[0.000020310143,0.00010639846,0.0006860216,0.000023365465,0.000029482399,0.00015577266,0.000025413961,0.96408534,0.023349516,0.010224174,0.001276199,0.000018015045],"about_ca_topic_score_codex":0.0022224095,"about_ca_topic_score_gemma":0.003095512,"teacher_disagreement_score":0.003863213,"about_ca_system_score_codex":0.0004361795,"about_ca_system_score_gemma":0.0007760197,"threshold_uncertainty_score":0.0129237175},"labels":[],"label_agreement":null},{"id":"W3099351824","doi":"","title":"Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":50,"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":"Computer science; Adaptation (eye); Artificial intelligence; Knowledge management; Machine learning; Psychology","score_opus":0.09874274633483145,"score_gpt":0.30306125328846156,"score_spread":0.20431850695363013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099351824","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":[0.0052486616,0.0004581224,0.9929066,0.00017062032,0.000077870594,0.000028756232,0.00002347342,0.00022659916,0.0008593986],"genre_scores_gemma":[0.47345188,0.0012618301,0.5164025,0.00042251585,0.00053924567,0.00032211223,0.00022073452,0.000247381,0.0071317717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906045,0.00026594164,0.00006476603,0.00030557424,0.00022192468,0.000081416125],"domain_scores_gemma":[0.99645233,0.002229342,0.00021446448,0.0005169641,0.00040769044,0.00017927997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024066668,0.000879532,0.0017060757,0.0013538657,0.00072037516,0.0014470435,0.0035381436,0.0015780216,0.0025609268],"category_scores_gemma":[0.006996414,0.00073559245,0.0011272115,0.0015365259,0.0018393532,0.0039367178,0.00410822,0.0031784019,0.0004756167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026001557,0.0004049744,0.0014109003,0.00035884063,0.00033449524,0.00021890453,0.00035199066,0.38469085,0.0048710722,0.120579414,0.0040411116,0.4824773],"study_design_scores_gemma":[0.0000065410827,0.00003109365,0.0001187146,0.000010946587,0.0000152577795,0.000026270367,0.000010266962,0.9538743,0.00052825536,0.04472872,0.0006388621,0.000010809031],"about_ca_topic_score_codex":0.003422109,"about_ca_topic_score_gemma":0.003302556,"teacher_disagreement_score":0.0035381436,"about_ca_system_score_codex":0.0008119347,"about_ca_system_score_gemma":0.00116492,"threshold_uncertainty_score":0.012727857},"labels":[],"label_agreement":null},{"id":"W3102559011","doi":"10.48550/arxiv.2011.06485","title":"Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robustness (evolution); Computer science; Machine learning; Artificial intelligence; Empirical risk minimization; Invariant (physics); Minification; Training set; Support vector machine; Generalization; Mathematics","score_opus":0.16333570463343014,"score_gpt":0.2266039780746313,"score_spread":0.06326827344120117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3102559011","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5034315,0.0018456698,0.4778239,0.0073969495,0.00017492223,0.00038198932,0.00050010683,0.00070311804,0.007741745],"genre_scores_gemma":[0.94020206,0.00022630564,0.05700301,0.0005510827,0.00016896217,0.000082067985,0.00024174298,0.000079860125,0.0014447932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98817843,0.007787979,0.00039435897,0.0015804988,0.001657932,0.0004007507],"domain_scores_gemma":[0.9059794,0.07525017,0.0048020887,0.008935288,0.003592459,0.0014406557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028480876,0.00073198974,0.001700253,0.0019726201,0.0021841365,0.0024663166,0.0023779904,0.0034190998,0.0013766137],"category_scores_gemma":[0.09282031,0.0002285788,0.0011709249,0.0021802369,0.004519241,0.003290165,0.0023746295,0.003592008,0.00041808048],"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":[0.002107021,0.0013993764,0.09330608,0.00040125533,0.00034250168,0.001904468,0.0022879646,0.4637094,0.0051274984,0.16266921,0.009772353,0.25697282],"study_design_scores_gemma":[0.00012360085,0.00032190655,0.010304218,0.00007089837,0.000046474564,0.0005603596,0.00038871172,0.6835132,0.005956402,0.29424834,0.004384357,0.00008153445],"about_ca_topic_score_codex":0.0029453686,"about_ca_topic_score_gemma":0.0021623312,"teacher_disagreement_score":0.028480876,"about_ca_system_score_codex":0.002647874,"about_ca_system_score_gemma":0.0014868277,"threshold_uncertainty_score":0.15062302},"labels":[],"label_agreement":null},{"id":"W3102998533","doi":"","title":"Information Maximization for Few-Shot Learning","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":49,"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":"Computer science; Shot (pellet); Maximization; Artificial intelligence; Machine learning; Mathematics; Mathematical optimization","score_opus":0.040735737377868744,"score_gpt":0.2550705761810338,"score_spread":0.21433483880316506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3102998533","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":[0.004331227,0.00160028,0.9924718,0.00046709334,0.000051691153,0.000044446646,0.00011436806,0.00018816692,0.0007308981],"genre_scores_gemma":[0.49296528,0.0045196107,0.48353302,0.0012439389,0.0009664035,0.0008987321,0.0019432561,0.0005220859,0.013407588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763954,0.0011319984,0.00014402028,0.00060529046,0.00033271013,0.00014644292],"domain_scores_gemma":[0.9853888,0.012412699,0.0004381673,0.00072327023,0.00080001855,0.00023702715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059556887,0.0015887892,0.004133028,0.0018345916,0.00096847134,0.002197137,0.004339493,0.0038814757,0.0033159226],"category_scores_gemma":[0.024742996,0.0014534503,0.001447089,0.0022224826,0.0030187173,0.0055629145,0.0030709228,0.0039170594,0.0009312123],"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":[0.00033402836,0.00029507672,0.00090973364,0.000993126,0.0004274723,0.00015799381,0.00022470571,0.6676273,0.0029400624,0.15980758,0.009637832,0.15664512],"study_design_scores_gemma":[0.000010512166,0.000022507693,0.000131268,0.000020073203,0.000015321477,0.0000213972,0.000008542135,0.90499866,0.00038394728,0.09396555,0.00040492162,0.00001726655],"about_ca_topic_score_codex":0.005512504,"about_ca_topic_score_gemma":0.004524612,"teacher_disagreement_score":0.0059556887,"about_ca_system_score_codex":0.002445382,"about_ca_system_score_gemma":0.0018939856,"threshold_uncertainty_score":0.031497},"labels":[],"label_agreement":null},{"id":"W3103117332","doi":"","title":"Your \"Labrador\" is My \"Dog\": Fine-Grained, or Not.","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"Computer science; Leverage (statistics); Intuition; Granularity; Artificial intelligence; Classifier (UML); Single level; Machine learning; Psychology; Cognitive science","score_opus":0.1307720786565493,"score_gpt":0.20329912299094233,"score_spread":0.07252704433439303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103117332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5471721,0.0034057684,0.28696388,0.013520042,0.0009338184,0.00045855856,0.0029041788,0.0042212154,0.1404204],"genre_scores_gemma":[0.9293197,0.0004222987,0.05770934,0.00129994,0.000075332835,0.00007483531,0.0010792624,0.00026511465,0.009754141],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990766,0.00023286536,0.00003698173,0.0003694928,0.00015257244,0.00013140403],"domain_scores_gemma":[0.99777895,0.0009942988,0.0002740948,0.00033670408,0.0004128333,0.00020312451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013580505,0.0005875744,0.0003039565,0.0006528666,0.0010122138,0.0025756129,0.00077517156,0.0015330549,0.008230156],"category_scores_gemma":[0.009630277,0.00019600296,0.00043761404,0.00047391417,0.0015818984,0.0062597,0.0011487905,0.0013899297,0.0025498704],"study_design_candidate":"bench_or_experimental","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":[0.001406454,0.00019994348,0.1203808,0.001159709,0.00014662283,0.00095226266,0.019906547,0.005028185,0.048117705,0.09494025,0.09320796,0.61455363],"study_design_scores_gemma":[0.00009979971,0.0006247138,0.1446741,0.0012238432,0.00030904304,0.005164369,0.04506015,0.16858073,0.035509318,0.27415267,0.32417816,0.00042309173],"about_ca_topic_score_codex":0.0073126117,"about_ca_topic_score_gemma":0.010082726,"teacher_disagreement_score":0.008230156,"about_ca_system_score_codex":0.0010463265,"about_ca_system_score_gemma":0.00043252078,"threshold_uncertainty_score":0.027532578},"labels":[],"label_agreement":null},{"id":"W3104090274","doi":"","title":"Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Western University","funders":"","keywords":"Forgetting; Lifelong learning; Computer science; Train; Task (project management); Reuse; Process (computing); Function (biology); Artificial intelligence; Variety (cybernetics); Control (management); Machine learning; Cognitive psychology; Economics; Engineering; Management; Political science; Psychology","score_opus":0.1349434077588783,"score_gpt":0.21645684205782129,"score_spread":0.08151343429894298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104090274","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":[0.017473573,0.0006102809,0.97826904,0.00017124544,0.000072318515,0.000051116323,0.00003946691,0.0020072889,0.001305725],"genre_scores_gemma":[0.6771604,0.00041868715,0.31759417,0.00038617974,0.00008847161,0.0002011492,0.00030817505,0.00045886234,0.003383826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995734,0.00011544749,0.000028284287,0.0001324866,0.0000952258,0.00005520694],"domain_scores_gemma":[0.9983884,0.00091891625,0.00012417609,0.00027211828,0.00020610914,0.000090269226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00141514,0.0010606762,0.0012015622,0.000562737,0.00042640415,0.0007337438,0.0012708247,0.0012350966,0.0032626898],"category_scores_gemma":[0.0076332605,0.00059549045,0.00048085864,0.00042257796,0.0010222265,0.0020194857,0.0011753802,0.0021770056,0.0010507957],"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":[0.00019957374,0.00020845175,0.0018249671,0.0001629105,0.00006967639,0.000115662275,0.00019126528,0.7000263,0.0051901285,0.019461915,0.0048672585,0.26768196],"study_design_scores_gemma":[0.00001149973,0.000031553904,0.00007592146,0.0000102569165,0.0000039456017,0.000020812362,0.0000061474957,0.9918057,0.0009740773,0.006417664,0.0006370399,0.0000054567668],"about_ca_topic_score_codex":0.00426728,"about_ca_topic_score_gemma":0.004803377,"teacher_disagreement_score":0.00426728,"about_ca_system_score_codex":0.00075958984,"about_ca_system_score_gemma":0.0013572967,"threshold_uncertainty_score":0.010914803},"labels":[],"label_agreement":null},{"id":"W3105236818","doi":"","title":"LoCo: Local Contrastive Representation Learning","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"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 Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Feature learning; Block (permutation group theory); Infomax; Representation (politics); Pattern recognition (psychology); Unsupervised learning; Channel (broadcasting); Mathematics; Blind signal separation","score_opus":0.08625001382560506,"score_gpt":0.18631294572797366,"score_spread":0.1000629319023686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105236818","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":[0.012673519,0.00028655172,0.9715768,0.00020885984,0.00007881856,0.00007489184,0.00030691334,0.011148246,0.0036453763],"genre_scores_gemma":[0.44014198,0.00026701155,0.5400093,0.00089575705,0.00014802342,0.00046275125,0.0021887976,0.0020540673,0.01383236],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965847,0.0000674258,0.0000111142535,0.00013565124,0.00008397624,0.00004340585],"domain_scores_gemma":[0.9994717,0.00016916898,0.000046988673,0.00020034729,0.000067306624,0.00004456718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007320634,0.0009658774,0.00070222525,0.0004888789,0.00036850796,0.0010283509,0.0022755729,0.0011684302,0.007397193],"category_scores_gemma":[0.002653302,0.00033843616,0.00056619156,0.00046027492,0.0008189295,0.0022648273,0.002415967,0.0022210376,0.0031945866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053347053,0.00039090036,0.0015932953,0.00029996358,0.00014247869,0.00022812217,0.00015729047,0.1328879,0.043852974,0.05359761,0.032368805,0.7339473],"study_design_scores_gemma":[0.000051484254,0.00016835683,0.00029917603,0.000024709012,0.000019268005,0.00010143436,0.000021840466,0.93526596,0.022090932,0.035622228,0.0063122227,0.000022374736],"about_ca_topic_score_codex":0.0013338609,"about_ca_topic_score_gemma":0.0030874044,"teacher_disagreement_score":0.007397193,"about_ca_system_score_codex":0.00065365544,"about_ca_system_score_gemma":0.00067289063,"threshold_uncertainty_score":0.02474612},"labels":[],"label_agreement":null},{"id":"W3107090469","doi":"10.1007/978-3-030-58604-1_4","title":"Adaptive Object Detection with Dual Multi-label Prediction","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Regularization (linguistics); Pattern recognition (psychology); Benchmark (surveying); Object (grammar); Object detection; Cognitive neuroscience of visual object recognition; Feature (linguistics); Computer vision; Machine learning","score_opus":0.029416488666864145,"score_gpt":0.23852544253333371,"score_spread":0.20910895386646958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107090469","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0099971015,0.0004128902,0.98637515,0.00012234379,0.00013716941,0.000034392342,0.00008939781,0.0013541318,0.0014774186],"genre_scores_gemma":[0.2736187,0.0005563663,0.70730174,0.00046401986,0.00025617218,0.00011752253,0.00092690054,0.00036372035,0.01639482],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99898976,0.00015899903,0.00003548638,0.00041353988,0.00027386114,0.00012825358],"domain_scores_gemma":[0.9987129,0.00048155469,0.000056465648,0.00037316766,0.00029511348,0.000080800506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011850332,0.000941914,0.0017153305,0.0010289449,0.000466297,0.0012660851,0.002879832,0.001973531,0.0032027224],"category_scores_gemma":[0.0017390564,0.0006977092,0.0010457494,0.0011458615,0.0006631173,0.0016934437,0.0025822192,0.002267497,0.0027345587],"study_design_candidate":"bench_or_experimental","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":[0.00040147995,0.00033575375,0.0009545098,0.00013400143,0.00015047145,0.00011832804,0.000056812547,0.0675627,0.05529315,0.0049678884,0.0069546276,0.86307037],"study_design_scores_gemma":[0.0000071134623,0.000033854838,0.00038607197,0.0000061520445,0.000022677512,0.000075812044,0.000008026005,0.98386,0.009355158,0.005171573,0.0010607932,0.000012714867],"about_ca_topic_score_codex":0.0028365473,"about_ca_topic_score_gemma":0.0041317716,"teacher_disagreement_score":0.0032027224,"about_ca_system_score_codex":0.0005023944,"about_ca_system_score_gemma":0.0006345922,"threshold_uncertainty_score":0.010714233},"labels":[],"label_agreement":null},{"id":"W3108916577","doi":"","title":"ImCLR: Implicit Contrastive Learning for Image Classification","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Benchmark (surveying); Hyperparameter; Machine learning; Supervised learning; Network architecture; Pattern recognition (psychology); Artificial neural network","score_opus":0.07425106803374812,"score_gpt":0.20402697389321456,"score_spread":0.12977590585946644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108916577","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":[0.019768769,0.0008728957,0.96321017,0.00057327107,0.00020680887,0.00018446821,0.000521907,0.008118478,0.006543316],"genre_scores_gemma":[0.3940343,0.0006471507,0.5871978,0.0014297633,0.00028964746,0.00061298726,0.002735824,0.0013132908,0.011739259],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987644,0.00033691773,0.00004749908,0.00039214935,0.00033662032,0.00012244142],"domain_scores_gemma":[0.99797076,0.0006622811,0.00021118415,0.0007562813,0.0002920292,0.00010741085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020415734,0.0023312687,0.0010732558,0.0011738027,0.00055091083,0.0013699365,0.0049061505,0.0020220089,0.0053512906],"category_scores_gemma":[0.008039627,0.00064588955,0.0012870363,0.0009976722,0.001611794,0.0037438548,0.003699921,0.005211145,0.00347251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051396096,0.0005119267,0.0026702166,0.0004918801,0.00021878105,0.0002649491,0.00022015847,0.17081872,0.04276921,0.040418655,0.030461617,0.71064],"study_design_scores_gemma":[0.00006291206,0.0002633092,0.00044811677,0.00005726166,0.000030828724,0.00018443314,0.00002489114,0.9395943,0.021317204,0.030897548,0.0070840735,0.000035051988],"about_ca_topic_score_codex":0.001788517,"about_ca_topic_score_gemma":0.003195475,"teacher_disagreement_score":0.0053512906,"about_ca_system_score_codex":0.0010068278,"about_ca_system_score_gemma":0.0009423932,"threshold_uncertainty_score":0.017901897},"labels":[],"label_agreement":null},{"id":"W3110297414","doi":"","title":"Contextual Interference Reduction by Selective Fine-Tuning of Neural Networks.","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Interpretability; Robustness (evolution); Artificial intelligence; MNIST database; Machine learning; Feature learning; Artificial neural network; Pattern recognition (psychology)","score_opus":0.0754647525756441,"score_gpt":0.19732864085127608,"score_spread":0.12186388827563198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110297414","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":[0.09219984,0.0008819903,0.9003598,0.00018407931,0.00010461081,0.00008784021,0.0001335568,0.002780444,0.0032679536],"genre_scores_gemma":[0.8092007,0.00029711192,0.18714543,0.00028298688,0.000064927524,0.0001407416,0.0003758258,0.00025586734,0.0022364308],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997695,0.00004760151,0.000009581921,0.00009140008,0.00004522648,0.000036797843],"domain_scores_gemma":[0.99956256,0.00016252637,0.000051237996,0.00011864189,0.00006741302,0.000037594426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042800215,0.0009967516,0.0005247831,0.00036145476,0.00026241815,0.00043886187,0.0013726419,0.00070494623,0.0012123106],"category_scores_gemma":[0.0024607177,0.0002901318,0.00053196127,0.00038301895,0.000489969,0.0009861698,0.0011582429,0.0012820306,0.00043612145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035134106,0.0003976571,0.0017367839,0.0003030831,0.00020687908,0.00019292289,0.00018901144,0.37875685,0.17140841,0.00826816,0.0041407924,0.43404806],"study_design_scores_gemma":[0.000015345024,0.00007710987,0.0008059652,0.000013291338,0.000034963752,0.000055843313,0.00002192935,0.9648981,0.025164178,0.007267924,0.0016351027,0.000010220118],"about_ca_topic_score_codex":0.0021035462,"about_ca_topic_score_gemma":0.0048658377,"teacher_disagreement_score":0.0021035462,"about_ca_system_score_codex":0.00058354065,"about_ca_system_score_gemma":0.00049831613,"threshold_uncertainty_score":0.0042339563},"labels":[],"label_agreement":null},{"id":"W3114171568","doi":"10.1109/bibe50027.2020.00049","title":"Cluster-Boosted Multi-Task Learning Framework for Survival Analysis","year":2020,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Cluster (spacecraft); Task (project management); Artificial intelligence; Engineering; Operating system; Systems engineering","score_opus":0.06256261830342982,"score_gpt":0.3078791878275283,"score_spread":0.2453165695240985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114171568","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":[0.017822439,0.00063342863,0.9788835,0.0004348147,0.00008406324,0.00006876921,0.00033904993,0.0011590914,0.00057468005],"genre_scores_gemma":[0.6526344,0.00052726,0.3391822,0.00047568692,0.00028207476,0.00041907342,0.0021129758,0.0002558035,0.004110503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906427,0.0003161121,0.000041721287,0.00029324304,0.00015588658,0.00012876725],"domain_scores_gemma":[0.9983595,0.0007276249,0.0001199045,0.00018428003,0.00045600568,0.00015271538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028713837,0.0010941067,0.0018525822,0.0011293052,0.0007499087,0.0007266724,0.0027995955,0.001452192,0.0022867206],"category_scores_gemma":[0.0046748547,0.00037049007,0.0014550969,0.001408808,0.0007401758,0.001052399,0.001269075,0.0021544225,0.0009344416],"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":[0.00051834714,0.00036644575,0.004249285,0.00019857186,0.00019327083,0.00015652161,0.00015761418,0.77621615,0.0034940985,0.009991645,0.0111741265,0.19328402],"study_design_scores_gemma":[0.000009195666,0.000025361227,0.00019581264,0.0000025369995,0.00000964355,0.000011248666,0.0000057233374,0.9928491,0.0003343512,0.006174483,0.00037563485,0.000006771503],"about_ca_topic_score_codex":0.0090878485,"about_ca_topic_score_gemma":0.008162639,"teacher_disagreement_score":0.0090878485,"about_ca_system_score_codex":0.001173051,"about_ca_system_score_gemma":0.0018103411,"threshold_uncertainty_score":0.018069923},"labels":[],"label_agreement":null},{"id":"W3115284685","doi":"10.1109/isbi48211.2021.9434103","title":"On Self-Supervised Multimodal Representation Learning: An Application To Alzheimer’s Disease","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Avid Radiopharmaceuticals","keywords":"Artificial intelligence; Computer science; Machine learning; Supervised learning; Modalities; Regularization (linguistics); Pattern recognition (psychology); Artificial neural network","score_opus":0.03969260774938757,"score_gpt":0.3182848777692139,"score_spread":0.27859227001982634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115284685","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":[0.12122801,0.0005759717,0.8753276,0.00058547617,0.000037071895,0.00003857552,0.00004951825,0.00048394993,0.0016738665],"genre_scores_gemma":[0.82045245,0.0002716098,0.17643903,0.00012460061,0.00006129382,0.000051007064,0.00009916771,0.00008520491,0.0024154587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982846,0.000089474146,0.000005163816,0.00003142498,0.000031007046,0.000014474435],"domain_scores_gemma":[0.9993655,0.00040517683,0.000044425175,0.00007171422,0.000080440215,0.00003271982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011593844,0.00036714182,0.00040711765,0.00031507923,0.00019986839,0.00035924139,0.000512897,0.0008482279,0.0008153415],"category_scores_gemma":[0.0029038233,0.00013892184,0.0004141104,0.0002786886,0.0006145208,0.0005256735,0.0009314384,0.0008437222,0.00012412058],"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":[0.0001680222,0.0001555179,0.0014983715,0.000113523114,0.000101290134,0.00022874631,0.00017003913,0.79790664,0.013684702,0.026767664,0.0019321322,0.15727325],"study_design_scores_gemma":[0.000003464447,0.000019199162,0.0001397036,0.0000021620863,0.0000025102797,0.0000212329,0.0000037173268,0.99348164,0.0008251731,0.0053758854,0.00012289661,0.0000024884278],"about_ca_topic_score_codex":0.0013825775,"about_ca_topic_score_gemma":0.0013108617,"teacher_disagreement_score":0.0013825775,"about_ca_system_score_codex":0.00035280656,"about_ca_system_score_gemma":0.00026557568,"threshold_uncertainty_score":0.00613147},"labels":[],"label_agreement":null},{"id":"W3116512656","doi":"10.1109/ictai50040.2020.00046","title":"Why Layer-Wise Learning is Hard to Scale-up and a Possible Solution via Accelerated Downsampling","year":2020,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Nvidia; National Aeronautics and Space Administration","keywords":"Upsampling; Computer science; Layer (electronics); Feature (linguistics); Artificial intelligence; Convolution (computer science); Feature learning; Deep learning; Scale (ratio); Backpropagation; Machine learning; Artificial neural network; Image (mathematics)","score_opus":0.07194476852431984,"score_gpt":0.2797950632922878,"score_spread":0.20785029476796796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116512656","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":[0.055454623,0.0030877024,0.9212022,0.010523237,0.00065208756,0.000116589195,0.0002214661,0.003610038,0.0051319725],"genre_scores_gemma":[0.6212638,0.0023855239,0.36092955,0.0024957675,0.00060745777,0.00032734347,0.00047947574,0.00086006144,0.0106511],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886453,0.000264068,0.00006678767,0.00035849953,0.0003072341,0.00013889922],"domain_scores_gemma":[0.99625045,0.0012705618,0.0002926596,0.0013500783,0.0006931769,0.00014312386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022704892,0.0011060601,0.00073872134,0.000400146,0.0007426642,0.0013295917,0.0015756425,0.001827615,0.0042512044],"category_scores_gemma":[0.013802593,0.00079103047,0.0005081868,0.0006762665,0.0014869884,0.003746159,0.0013509343,0.0032730913,0.0022769992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005562369,0.00021971385,0.005985074,0.0007243421,0.00027266855,0.0007267736,0.000564713,0.23092398,0.05985534,0.075106524,0.04755897,0.57750565],"study_design_scores_gemma":[0.00008129826,0.00015309668,0.0027208754,0.0000964745,0.000066110275,0.00080399594,0.0002474809,0.82290024,0.028378414,0.124094106,0.020382518,0.00007541144],"about_ca_topic_score_codex":0.0043431427,"about_ca_topic_score_gemma":0.00495715,"teacher_disagreement_score":0.0043431427,"about_ca_system_score_codex":0.0007099295,"about_ca_system_score_gemma":0.0011138608,"threshold_uncertainty_score":0.014221728},"labels":[],"label_agreement":null},{"id":"W3118144431","doi":"10.1109/cvpr46437.2021.01089","title":"IIRC: Incremental Implicitly-Refined Classification","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Benchmark (surveying); Computer science; Task (project management); Class (philosophy); Granularity; Artificial intelligence; Machine learning; Lifelong learning; Programming language; Engineering","score_opus":0.038633882211308784,"score_gpt":0.27379217352500174,"score_spread":0.23515829131369295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118144431","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":[0.08301182,0.0034377743,0.8658305,0.0021883373,0.0008004457,0.0007371112,0.004605715,0.029938111,0.009450111],"genre_scores_gemma":[0.50308794,0.0006056046,0.45930493,0.002254033,0.00043135398,0.0007324008,0.020588024,0.0015411403,0.011454661],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99630666,0.0009078341,0.00016198352,0.001309978,0.00086901383,0.0004444649],"domain_scores_gemma":[0.9922748,0.002440979,0.00037248412,0.002987972,0.0015262916,0.00039744677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036569021,0.00288914,0.0021327173,0.0015127253,0.0010131622,0.00199038,0.009098974,0.0036834648,0.004896587],"category_scores_gemma":[0.013201691,0.0008761215,0.0019165293,0.0014617904,0.0014112943,0.006729469,0.003778444,0.0061986092,0.0027484808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014170536,0.0010279022,0.010713071,0.0006033129,0.00034542347,0.0004620871,0.00051097554,0.2556749,0.009884645,0.017388439,0.08599401,0.6159782],"study_design_scores_gemma":[0.0000662459,0.00017816508,0.0007339193,0.000048321017,0.000042707903,0.00014375684,0.00004896121,0.9751821,0.0040434217,0.012406132,0.0070633427,0.000042975968],"about_ca_topic_score_codex":0.016969377,"about_ca_topic_score_gemma":0.020740915,"teacher_disagreement_score":0.016969377,"about_ca_system_score_codex":0.002297866,"about_ca_system_score_gemma":0.003172642,"threshold_uncertainty_score":0.033741236},"labels":[],"label_agreement":null},{"id":"W3118334164","doi":"10.1109/wacv48630.2021.00332","title":"Towards Contextual Learning in Few-shot Object Classification","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Leverage (statistics); Computer science; Artificial intelligence; Isolation (microbiology); Object (grammar); Context (archaeology); Visual Objects; Machine learning; Perception; Psychology","score_opus":0.0804871400187697,"score_gpt":0.31154265881401966,"score_spread":0.23105551879524994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118334164","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":[0.01968076,0.0008087227,0.9766896,0.00025296817,0.000044006218,0.0000610798,0.000110334804,0.0016575471,0.00069492025],"genre_scores_gemma":[0.49177387,0.0007243435,0.50226647,0.0008035089,0.00033109344,0.00023346442,0.0012372058,0.00033932563,0.0022907767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986118,0.00035068896,0.000053819294,0.0006011579,0.00024116454,0.00014132622],"domain_scores_gemma":[0.99751294,0.0013814223,0.00016516905,0.0004839611,0.00027364673,0.00018289206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020901293,0.0014424894,0.0021119486,0.0019963868,0.000790911,0.0015462347,0.0030000028,0.002362807,0.002081742],"category_scores_gemma":[0.0068419464,0.00081496744,0.0012769105,0.0013763746,0.0016786011,0.0033262703,0.002992479,0.002794935,0.0008677374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046228655,0.00053415704,0.0046982113,0.00054321886,0.00025941164,0.00025763724,0.0004977966,0.27230212,0.01948868,0.024946429,0.0068432447,0.6691668],"study_design_scores_gemma":[0.000018411893,0.000067295005,0.0005900884,0.000025290945,0.000023995735,0.0000558354,0.000049806735,0.96373236,0.0040286253,0.030091003,0.0013006489,0.000016499027],"about_ca_topic_score_codex":0.0042442265,"about_ca_topic_score_gemma":0.006102951,"teacher_disagreement_score":0.0042442265,"about_ca_system_score_codex":0.0011460251,"about_ca_system_score_gemma":0.0009416267,"threshold_uncertainty_score":0.011053801},"labels":[],"label_agreement":null},{"id":"W3118731078","doi":"10.3390/geomatics1010004","title":"Train Fast While Reducing False Positives: Improving Animal Classification Performance Using Convolutional Neural Networks","year":2021,"lang":"en","type":"article","venue":"Geomatics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Computer Research Institute of Montréal; Université de Sherbrooke","funders":"Mitacs","keywords":"Computer science; False positive paradox; Artificial intelligence; Classifier (UML); False positives and false negatives; Machine learning; Convolutional neural network; Pattern recognition (psychology); Binary classification; Data mining; Support vector machine","score_opus":0.04118448344027419,"score_gpt":0.253090894192409,"score_spread":0.2119064107521348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118731078","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.549132,0.0007647597,0.43893138,0.0007773476,0.00016647074,0.00010451346,0.00041163512,0.006902923,0.002809044],"genre_scores_gemma":[0.875584,0.00015027299,0.12065823,0.00037165798,0.00004786433,0.00004697832,0.0010406272,0.00015895713,0.0019414019],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920374,0.00017194281,0.00004718013,0.00022439325,0.00020712726,0.00014558209],"domain_scores_gemma":[0.997212,0.0014294892,0.0002533225,0.0004972954,0.00051786867,0.00009003269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024222247,0.0012308934,0.00056543545,0.00085655507,0.00035109904,0.00074521254,0.0013404231,0.0010533243,0.00068790757],"category_scores_gemma":[0.007469702,0.0004015955,0.00040535192,0.0005223871,0.00055879063,0.001803878,0.0008470535,0.0011463882,0.00049915473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048803765,0.00047045122,0.020941388,0.00013010806,0.00020670057,0.0002831309,0.00018277568,0.28992206,0.04612516,0.0014685879,0.007043373,0.63273823],"study_design_scores_gemma":[0.000010152899,0.00007132943,0.0024945033,0.000009044423,0.000020875395,0.000051673505,0.000020396992,0.9823301,0.013356724,0.0009839885,0.0006406359,0.000010647842],"about_ca_topic_score_codex":0.0075267856,"about_ca_topic_score_gemma":0.011134349,"teacher_disagreement_score":0.0075267856,"about_ca_system_score_codex":0.00070156774,"about_ca_system_score_gemma":0.0007506748,"threshold_uncertainty_score":0.014965951},"labels":[],"label_agreement":null},{"id":"W3118797298","doi":"","title":"Exploring representation learning for flexible few-shot tasks","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Flexibility (engineering); Representation (politics); Task (project management); Benchmark (surveying); Class (philosophy); Set (abstract data type); Machine learning; Feature learning; Context (archaeology); Feature (linguistics); Shot (pellet); Mathematics","score_opus":0.30819809113878577,"score_gpt":0.34669765633298,"score_spread":0.03849956519419423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118797298","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":[0.12878637,0.0015483462,0.8656036,0.0006550739,0.000058000584,0.00017091728,0.00043775607,0.00106221,0.0016776689],"genre_scores_gemma":[0.8545748,0.00046079856,0.14003968,0.00041783927,0.000106702,0.00028044643,0.0019748108,0.00017470136,0.0019702145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985567,0.0005722818,0.000056650762,0.00051743444,0.00017237243,0.00012459798],"domain_scores_gemma":[0.9960989,0.0027104865,0.00023112637,0.0005205504,0.00023741725,0.00020157201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028916465,0.0014402604,0.0015016962,0.00107162,0.0006411016,0.0013623344,0.0027788419,0.0020790128,0.0014877475],"category_scores_gemma":[0.012478824,0.00054708857,0.0009833304,0.0008877732,0.0015468001,0.003830307,0.0025652167,0.0027613358,0.00038950416],"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":[0.00043545585,0.0007689393,0.005218585,0.00043838465,0.00021754578,0.00024248488,0.00042098414,0.7201861,0.007734603,0.019789735,0.005198314,0.23934895],"study_design_scores_gemma":[0.000017656164,0.000110170484,0.000407319,0.000012972154,0.000009626737,0.000035263816,0.000040848114,0.9723812,0.0010593685,0.025502928,0.00041113992,0.00001151214],"about_ca_topic_score_codex":0.0036263962,"about_ca_topic_score_gemma":0.0039754086,"teacher_disagreement_score":0.0036263962,"about_ca_system_score_codex":0.0013816324,"about_ca_system_score_gemma":0.00084998395,"threshold_uncertainty_score":0.0152926445},"labels":[],"label_agreement":null},{"id":"W3118952246","doi":"","title":"A Universal Representation Transformer Layer for Few-Shot Image Classification","year":2021,"lang":"en","type":"article","venue":"UTS ePRESS (University of Technology Sydney)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":18,"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":"Computer science; Leverage (statistics); Artificial intelligence; Weighting; Contextual image classification; Transformer; Feature extraction; Pattern recognition (psychology); Machine learning; Visualization; Representation (politics); Feature (linguistics); Data mining; Image (mathematics)","score_opus":0.04486204740380463,"score_gpt":0.2692368662175408,"score_spread":0.22437481881373617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118952246","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":[0.043103613,0.0012845364,0.931675,0.0005035972,0.00028534516,0.00020958406,0.0009983033,0.017054321,0.0048855976],"genre_scores_gemma":[0.573757,0.0009065193,0.40214285,0.001057589,0.00019399785,0.00037382796,0.0060088993,0.00068662997,0.014872623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949133,0.000063340834,0.000022185668,0.00023388765,0.00010062309,0.00008857467],"domain_scores_gemma":[0.99940336,0.00015312327,0.00004097433,0.00021467931,0.00012894308,0.00005900917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010812831,0.0014101879,0.0010865389,0.001057356,0.00049307547,0.0013067643,0.0025700831,0.0014489996,0.0053691473],"category_scores_gemma":[0.002807175,0.000409809,0.0013610999,0.0010276071,0.00076279964,0.0034270608,0.00250052,0.0031154305,0.0030730504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032828658,0.0005305586,0.002979559,0.0002497256,0.00021252714,0.00015436263,0.0001922894,0.056254502,0.034042712,0.010170925,0.024579167,0.87030536],"study_design_scores_gemma":[0.000019392508,0.00011453627,0.00069854455,0.000028849236,0.0000535993,0.00013079616,0.00005238944,0.96270216,0.020899355,0.011034545,0.004239108,0.000026781188],"about_ca_topic_score_codex":0.0060199853,"about_ca_topic_score_gemma":0.008844742,"teacher_disagreement_score":0.0060199853,"about_ca_system_score_codex":0.0011514785,"about_ca_system_score_gemma":0.0011336332,"threshold_uncertainty_score":0.017961621},"labels":[],"label_agreement":null},{"id":"W3122263332","doi":"","title":"f-Domain-Adversarial Learning: Theory and Algorithms for Unsupervised Domain Adaptation with Neural Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Adversarial system; Computer science; Domain (mathematical analysis); Divergence (linguistics); Generalization; Artificial intelligence; Machine learning; Domain theory; Artificial neural network; Algorithm; Variety (cybernetics); Mathematics; Discrete mathematics","score_opus":0.01922757717792776,"score_gpt":0.239559009938009,"score_spread":0.22033143276008124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122263332","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013753336,0.00033509522,0.9970433,0.00018253282,0.000020855117,0.000026383057,0.000020610143,0.000099532066,0.0008964159],"genre_scores_gemma":[0.37045997,0.0025203447,0.6179626,0.00075837993,0.00033185334,0.00073338585,0.00040275813,0.00038797216,0.006442797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810445,0.0008270689,0.000091011985,0.00042216614,0.00040920635,0.00014608132],"domain_scores_gemma":[0.9933808,0.0050862515,0.0003852133,0.0005481296,0.0004033903,0.00019632187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005266917,0.0019278013,0.0017732108,0.0013810786,0.000839884,0.0018072091,0.0027716102,0.002769593,0.002637415],"category_scores_gemma":[0.01384445,0.0008360783,0.0013126701,0.0012676694,0.0034037186,0.0037839473,0.004763882,0.006496445,0.0007928046],"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":[0.00004700775,0.00004881576,0.0006530555,0.00011682449,0.00005717388,0.000052879765,0.00011233189,0.7721041,0.0007739613,0.1545409,0.0025993655,0.06889365],"study_design_scores_gemma":[0.0000051528496,0.0000144878995,0.000058198722,0.000021183958,0.000004299622,0.000020634587,0.000007686427,0.9323176,0.00031362774,0.066449195,0.00077814504,0.000009798096],"about_ca_topic_score_codex":0.0034554491,"about_ca_topic_score_gemma":0.0023399857,"teacher_disagreement_score":0.005266917,"about_ca_system_score_codex":0.0023767904,"about_ca_system_score_gemma":0.0014980243,"threshold_uncertainty_score":0.027854443},"labels":[],"label_agreement":null},{"id":"W3122996481","doi":"10.48550/arxiv.2101.09825","title":"Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext Tasks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Université de Montréal","funders":"","keywords":"Computer science; Leverage (statistics); Pretext; Exploit; Machine learning; Complementarity (molecular biology); Artificial intelligence; Supervised learning; Task (project management); Training set; Semi-supervised learning; Class (philosophy); Labeled data","score_opus":0.05132379531189979,"score_gpt":0.17754794645715363,"score_spread":0.12622415114525384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122996481","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037829876,0.0008639745,0.9533273,0.00044475077,0.00013442144,0.0001393779,0.00035462307,0.0045694616,0.0023363177],"genre_scores_gemma":[0.6262697,0.00047456677,0.35530755,0.0011785575,0.00039710116,0.0003979451,0.0044915895,0.0011374216,0.010345612],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99797994,0.00078050344,0.000083747495,0.000676517,0.00032078905,0.00015862213],"domain_scores_gemma":[0.9924259,0.0042739566,0.00035538877,0.0018413755,0.00071718654,0.0003862229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032938656,0.0018795996,0.0022736823,0.0016100968,0.001086053,0.001619611,0.0036721323,0.0028728612,0.003564817],"category_scores_gemma":[0.014851959,0.00076176564,0.0014778705,0.0013391759,0.0018905271,0.005351128,0.003848647,0.0042865975,0.0021251887],"study_design_candidate":"bench_or_experimental","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":[0.00061698846,0.0012852162,0.0031688127,0.0006803787,0.00033015612,0.00026098712,0.00048724905,0.39699528,0.01672655,0.021911822,0.02394424,0.53359234],"study_design_scores_gemma":[0.000023552477,0.00006368788,0.00023336284,0.00001411004,0.000013126415,0.000038646634,0.000024533127,0.979745,0.0032691348,0.015809799,0.00075090636,0.000014154137],"about_ca_topic_score_codex":0.0042932276,"about_ca_topic_score_gemma":0.0066328514,"teacher_disagreement_score":0.0042932276,"about_ca_system_score_codex":0.0013150107,"about_ca_system_score_gemma":0.0014226063,"threshold_uncertainty_score":0.017419875},"labels":[],"label_agreement":null},{"id":"W3125085017","doi":"","title":"Leaky Tiling Activations: A Simple Approach to Learning Sparse Representations Online","year":2021,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Reinforcement learning; Regularization (linguistics); Simple (philosophy); Artificial neural network; Online algorithm; Artificial intelligence; Algorithm; Deep learning; Function (biology); Pattern recognition (psychology)","score_opus":0.1131943599475023,"score_gpt":0.3721155278946628,"score_spread":0.25892116794716047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125085017","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055700378,0.000058212274,0.9930274,0.00012684257,0.000025652722,0.000023991506,0.000020368241,0.0004240944,0.0007234572],"genre_scores_gemma":[0.51077163,0.00025147767,0.48215538,0.00035763343,0.000110518835,0.0002780272,0.00014973147,0.00031190264,0.005613683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961853,0.00011981528,0.000020937357,0.00010541714,0.00008838147,0.000046865956],"domain_scores_gemma":[0.9992017,0.00037408233,0.00008136919,0.00018589835,0.00008774837,0.000069125854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008766413,0.0007663793,0.00081566133,0.00039290925,0.0003234362,0.0007026865,0.0015686875,0.0011852342,0.004043927],"category_scores_gemma":[0.003923325,0.0004475871,0.0004886341,0.00054033415,0.0012595496,0.0021183568,0.0018184157,0.002219791,0.00076198345],"study_design_candidate":"bench_or_experimental","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":[0.00024692382,0.00015819483,0.00073653954,0.00013635935,0.00007350417,0.00014643096,0.0002087696,0.651787,0.023048379,0.0902773,0.0030785191,0.23010206],"study_design_scores_gemma":[0.00000989263,0.00003975867,0.000043692395,0.0000054210864,0.0000044970766,0.000024793862,0.000006579413,0.97256225,0.0026093903,0.023822093,0.0008654499,0.0000061153296],"about_ca_topic_score_codex":0.0010036267,"about_ca_topic_score_gemma":0.0011685888,"teacher_disagreement_score":0.004043927,"about_ca_system_score_codex":0.0004859519,"about_ca_system_score_gemma":0.00069604395,"threshold_uncertainty_score":0.013528287},"labels":[],"label_agreement":null},{"id":"W3127129507","doi":"","title":"Incremental few-shot learning via vector quantization in deep embedded space","year":2021,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Forgetting; Overfitting; Learning vector quantization; Computer science; Artificial intelligence; Regularization (linguistics); Machine learning; Quantization (signal processing); Incremental learning; Support vector machine; Deep learning; Reproducing kernel Hilbert space; Vector quantization; Kernel (algebra); Pattern recognition (psychology); Artificial neural network; Algorithm; Mathematics; Hilbert space","score_opus":0.05821540092947535,"score_gpt":0.34313833311950254,"score_spread":0.2849229321900272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127129507","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":[0.02441413,0.00072982896,0.97295797,0.00014018516,0.00007408906,0.000055543725,0.00009673068,0.0010630612,0.0004684974],"genre_scores_gemma":[0.7403956,0.00069440913,0.25409728,0.00031566338,0.00010325274,0.000193772,0.00088670076,0.0001598001,0.0031534776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992711,0.00015702339,0.000049497132,0.00025259465,0.00019866318,0.00007104113],"domain_scores_gemma":[0.9988961,0.00044651772,0.00010516308,0.00026123732,0.00021650047,0.00007449376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001072807,0.0008815868,0.0014402515,0.0005369193,0.0003608342,0.00081130874,0.0022344794,0.0009061279,0.0012583499],"category_scores_gemma":[0.0038511923,0.0004898931,0.0006238614,0.0007715303,0.0007881862,0.0030151142,0.0015056711,0.002285868,0.000444524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026191305,0.0002556368,0.0019125865,0.00023154891,0.00010397377,0.00012409379,0.00027394303,0.3360319,0.0112807695,0.011533493,0.004496409,0.6334937],"study_design_scores_gemma":[0.000008873083,0.000046554767,0.00017616095,0.0000068262293,0.000008240751,0.000024679632,0.000014701128,0.99152,0.0019985547,0.0057703,0.00041510732,0.000010030233],"about_ca_topic_score_codex":0.005951458,"about_ca_topic_score_gemma":0.0067097526,"teacher_disagreement_score":0.005951458,"about_ca_system_score_codex":0.0008207439,"about_ca_system_score_gemma":0.0010132369,"threshold_uncertainty_score":0.011833668},"labels":[],"label_agreement":null},{"id":"W3127215335","doi":"10.1609/aaai.v35i9.16969","title":"Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Distillation; Computer science; Feature (linguistics); Matching (statistics); Artificial intelligence; Machine learning; Selection (genetic algorithm); Control (management); Knowledge transfer; Mathematics; Knowledge management; Statistics","score_opus":0.04860094291411718,"score_gpt":0.2885622224527176,"score_spread":0.23996127953860044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127215335","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":[0.035491988,0.00052693917,0.95174587,0.00036119248,0.00009236396,0.00012389025,0.00038032886,0.008487231,0.002790229],"genre_scores_gemma":[0.4952637,0.00032904002,0.49273708,0.0006515748,0.000085827734,0.00022474153,0.0017549177,0.0008628013,0.008090291],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946326,0.000118302916,0.000025084393,0.00020734927,0.000118261174,0.00006765273],"domain_scores_gemma":[0.9991441,0.0003755638,0.00006320071,0.0002696593,0.00008819676,0.00005928641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008660458,0.0011631055,0.0009957151,0.001376051,0.00075288524,0.001002372,0.0027884752,0.0017022914,0.004592468],"category_scores_gemma":[0.0035650115,0.0005859807,0.0009447217,0.0012136931,0.0010621808,0.004673275,0.0027743513,0.0020744326,0.0014017345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039527952,0.00033976414,0.0010662191,0.000237819,0.00013081449,0.0002602775,0.00030253996,0.12134302,0.026207749,0.019064238,0.011914214,0.8187381],"study_design_scores_gemma":[0.00003393022,0.000089277164,0.0002836657,0.000014251036,0.000028137647,0.00009109097,0.000044660406,0.9521351,0.016421486,0.027126368,0.003706516,0.000025582116],"about_ca_topic_score_codex":0.0049229404,"about_ca_topic_score_gemma":0.006928398,"teacher_disagreement_score":0.0049229404,"about_ca_system_score_codex":0.0007201168,"about_ca_system_score_gemma":0.0010045441,"threshold_uncertainty_score":0.015363395},"labels":[],"label_agreement":null},{"id":"W3129082890","doi":"10.1007/s12559-020-09815-4","title":"GSNet: Group Sequential Learning for Image Recognition","year":2021,"lang":"en","type":"article","venue":"Cognitive Computation","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"York University","funders":"","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Convolutional neural network; Deep learning; Block (permutation group theory); Residual; Convolution (computer science); Machine learning; Computation; Code (set theory); Contextual image classification; Pattern recognition (psychology); Generalization; Image (mathematics); Artificial neural network; Set (abstract data type); Algorithm","score_opus":0.05009542573905999,"score_gpt":0.3057887952740145,"score_spread":0.2556933695349545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129082890","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059984517,0.00023273799,0.98170024,0.00013253982,0.000105082756,0.000064622596,0.0003587873,0.010552956,0.0008545743],"genre_scores_gemma":[0.14868985,0.0002997397,0.842337,0.00026567446,0.00012008542,0.00029963453,0.001945208,0.00096221705,0.0050805304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997392,0.000051166866,0.000012959508,0.00010353601,0.00006518393,0.00002788798],"domain_scores_gemma":[0.9994611,0.00019573235,0.000027771872,0.00016613079,0.00010002142,0.000049255064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072106754,0.0010709413,0.0011631193,0.00076047884,0.00038517767,0.00081651594,0.002076896,0.0011233793,0.007681644],"category_scores_gemma":[0.002053427,0.0004710125,0.0006343715,0.0009899131,0.00061432616,0.0016494108,0.001280859,0.0017148561,0.0024905684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005346608,0.00028278038,0.0009630055,0.0002561165,0.00017734751,0.00011861939,0.00008990723,0.11562709,0.013099367,0.032637957,0.041283976,0.79492915],"study_design_scores_gemma":[0.000029531879,0.00003841793,0.00019511797,0.000007722653,0.000013746153,0.000027020906,0.000008765663,0.96121037,0.0040854453,0.031089982,0.0032855554,0.0000083261],"about_ca_topic_score_codex":0.007843003,"about_ca_topic_score_gemma":0.013799005,"teacher_disagreement_score":0.007843003,"about_ca_system_score_codex":0.0006206852,"about_ca_system_score_gemma":0.0011072778,"threshold_uncertainty_score":0.025697649},"labels":[],"label_agreement":null},{"id":"W3129833897","doi":"10.1007/978-3-030-68790-8_56","title":"Deep Image Clustering Using Self-learning Optimization in a Variational Auto-Encoder","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"Polytechnique Montréal","funders":"","keywords":"Cluster analysis; Computer science; Autoencoder; Artificial intelligence; Benchmark (surveying); Deep learning; Pattern recognition (psychology); Correlation clustering; Regularization (linguistics); Machine learning; Data mining","score_opus":0.01613476539706432,"score_gpt":0.24639422648682693,"score_spread":0.2302594610897626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129833897","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":[0.0037001679,0.0001374254,0.99466246,0.00007241258,0.000030792402,0.000022810806,0.000040113016,0.00052641146,0.0008074618],"genre_scores_gemma":[0.16929998,0.0002547409,0.820116,0.00021183191,0.00007649104,0.000142044,0.00040430852,0.00065010623,0.0088445265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995883,0.00009620612,0.000020510402,0.00014404491,0.00010230389,0.000048591126],"domain_scores_gemma":[0.99925715,0.00030948647,0.00005216436,0.00016224831,0.00015944433,0.000059458067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00098565,0.0007043537,0.0017324598,0.0007648235,0.00055707694,0.0011114458,0.002468664,0.0020704435,0.0038730998],"category_scores_gemma":[0.0017845834,0.0010283662,0.001309216,0.0011231209,0.0010249184,0.001614853,0.0018925105,0.0020104102,0.0016834635],"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":[0.00008685167,0.000092200076,0.00033567933,0.000102728685,0.00009350325,0.000055853096,0.000064957094,0.77976406,0.010253337,0.036212347,0.0047262744,0.16821226],"study_design_scores_gemma":[0.0000017981108,0.0000072920025,0.000019184075,0.000002434211,0.0000028072236,0.000011231486,0.0000023424072,0.9958252,0.00069784326,0.0031820466,0.00024487308,0.0000030536094],"about_ca_topic_score_codex":0.0080386195,"about_ca_topic_score_gemma":0.01056818,"teacher_disagreement_score":0.0080386195,"about_ca_system_score_codex":0.0014878382,"about_ca_system_score_gemma":0.0013453139,"threshold_uncertainty_score":0.015983641},"labels":[],"label_agreement":null},{"id":"W3131265523","doi":"10.48550/arxiv.2102.06253","title":"Continuum: Simple Management of Complex Continual Learning Scenarios","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Loader; Field (mathematics); Machine learning; Key (lock); Simple (philosophy); Data pre-processing; Preprocessor; Artificial intelligence; Set (abstract data type); Data mining; Test data; Software engineering","score_opus":0.08511764575923818,"score_gpt":0.20451728600394542,"score_spread":0.11939964024470724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131265523","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015449011,0.00016611436,0.97093654,0.00070980575,0.00008893869,0.00023271126,0.0003889587,0.009024448,0.0030034953],"genre_scores_gemma":[0.34438106,0.00022082296,0.64687854,0.0005501876,0.00014134945,0.00078716234,0.0014954851,0.0012004154,0.0043450287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99584997,0.0014452413,0.00034233936,0.00107879,0.00096160406,0.00032207672],"domain_scores_gemma":[0.98892564,0.0034336955,0.00056978024,0.0049833325,0.0011690751,0.00091855053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007776367,0.0011322108,0.0014338755,0.0015692366,0.0014451685,0.0033966058,0.0066212555,0.002663504,0.008928372],"category_scores_gemma":[0.024808187,0.0009943581,0.0012996651,0.0010072835,0.002380351,0.009788275,0.009125038,0.0038893505,0.0030286973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012878884,0.0010032324,0.0106964875,0.00049502193,0.00022279534,0.001039194,0.0009953363,0.33727884,0.00960974,0.24515541,0.03691758,0.35529846],"study_design_scores_gemma":[0.000048406662,0.00012531174,0.0003551248,0.000037421112,0.000014708685,0.00024408869,0.00011348602,0.8740249,0.003033865,0.11392126,0.008039364,0.000041945244],"about_ca_topic_score_codex":0.0019484987,"about_ca_topic_score_gemma":0.002499683,"teacher_disagreement_score":0.008928372,"about_ca_system_score_codex":0.0014538171,"about_ca_system_score_gemma":0.0020843481,"threshold_uncertainty_score":0.041125834},"labels":[],"label_agreement":null},{"id":"W3132510842","doi":"10.48550/arxiv.2012.05942","title":"Convex Potential Flows: Universal Probability Distributions with Optimal\\n Transport and Convex Optimization","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Université de Montréal","funders":"","keywords":"Hessian matrix; Mathematics; Mathematical optimization; Convex optimization; Applied mathematics; Estimator; Convex analysis; Convex function; Conjugate gradient method; Jacobian matrix and determinant; Proper convex function; Regular polygon; Geometry","score_opus":0.04312048683777586,"score_gpt":0.16733277053012355,"score_spread":0.12421228369234769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132510842","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005730602,0.00020421665,0.9922116,0.0002604136,0.000021520542,0.000029811936,0.00009196807,0.0001994755,0.001250356],"genre_scores_gemma":[0.52448654,0.0013544419,0.46467894,0.000544243,0.00022138782,0.00041024265,0.000933581,0.0005842979,0.0067864335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987783,0.0005850499,0.000056275054,0.00025403756,0.00022003682,0.00010633154],"domain_scores_gemma":[0.99557793,0.0029872602,0.00035392327,0.00055937545,0.0003347556,0.00018677945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030253502,0.0013505387,0.0012830892,0.0013840372,0.0007580936,0.0019565374,0.001961951,0.0021215656,0.0036971045],"category_scores_gemma":[0.016571887,0.00090690225,0.001244449,0.001348646,0.0030419743,0.0056617656,0.0037129493,0.0029455086,0.00076491205],"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":[0.00007027521,0.00004272443,0.00078006944,0.0001372229,0.0000390209,0.00008258488,0.000110741195,0.530696,0.0011053866,0.41640803,0.0031148712,0.047413096],"study_design_scores_gemma":[0.0000035796622,0.000010417535,0.0000494093,0.0000117358495,0.000003416402,0.000025702213,0.0000075248527,0.8826679,0.0003887661,0.115949936,0.0008740102,0.0000075756443],"about_ca_topic_score_codex":0.0037715074,"about_ca_topic_score_gemma":0.002723287,"teacher_disagreement_score":0.0037715074,"about_ca_system_score_codex":0.0020738423,"about_ca_system_score_gemma":0.0017975303,"threshold_uncertainty_score":0.015999794},"labels":[],"label_agreement":null},{"id":"W3133289359","doi":"10.1016/j.neucom.2020.09.091","title":"Domain generalization via optimal transport with metric similarity learning","year":2021,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Western University; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Leverage (statistics); Artificial intelligence; Computer science; Similarity (geometry); Generalization; Invariant (physics); Machine learning; Domain (mathematical analysis); Pattern recognition (psychology); Metric (unit); Boundary (topology); Mathematics; Image (mathematics)","score_opus":0.011663182864334331,"score_gpt":0.2247388399529125,"score_spread":0.21307565708857817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133289359","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012768934,0.00026819066,0.9857119,0.00018386492,0.00004244651,0.000023500528,0.000039434424,0.0002655831,0.0006961539],"genre_scores_gemma":[0.6116963,0.00069661014,0.37903586,0.00033170698,0.00014833736,0.00019604896,0.00046623455,0.0003045276,0.0071243267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995499,0.00015718542,0.000026964983,0.00014217228,0.00008456929,0.000039116287],"domain_scores_gemma":[0.9988593,0.0005524076,0.00009163931,0.00024587085,0.00017453321,0.00007619063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012893964,0.000627169,0.001606791,0.0010028654,0.0005738561,0.0008906955,0.0016333137,0.0018576832,0.0016171719],"category_scores_gemma":[0.00405428,0.0005118118,0.0012587832,0.0009476822,0.0012249249,0.002589171,0.002846321,0.0019151535,0.00042092096],"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":[0.00016726647,0.00015573206,0.0009956773,0.00018806166,0.00015374653,0.00011006951,0.00014420536,0.7159479,0.0092756795,0.0851738,0.004485469,0.1832023],"study_design_scores_gemma":[0.0000035353507,0.000015123636,0.000059843558,0.000003739741,0.000004491757,0.000016799819,0.0000062312047,0.9775285,0.0005265183,0.021552134,0.00027718183,0.000005887659],"about_ca_topic_score_codex":0.0048808805,"about_ca_topic_score_gemma":0.003437467,"teacher_disagreement_score":0.0048808805,"about_ca_system_score_codex":0.0009769989,"about_ca_system_score_gemma":0.00097568415,"threshold_uncertainty_score":0.0097049475},"labels":[],"label_agreement":null},{"id":"W3133533153","doi":"10.1002/acp.3816","title":"With support, children can accurately sequence within‐event components","year":2021,"lang":"en","type":"article","venue":"Applied Cognitive Psychology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Psychology; Event (particle physics); Recall; Context (archaeology); Sequence (biology); Cognition; Variety (cybernetics); Cognitive psychology; Developmental psychology; Computer science; Artificial intelligence; Neuroscience; Genetics; History","score_opus":0.06746747305202515,"score_gpt":0.33563681939877077,"score_spread":0.2681693463467456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133533153","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96012783,0.00026554,0.026205897,0.00032234646,0.000034628174,0.00008909052,0.0005206806,0.0006027778,0.011831254],"genre_scores_gemma":[0.971704,0.00022587209,0.025450667,0.00006480367,0.000007742958,0.000059129918,0.00042526264,0.000051810362,0.0020107192],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99946576,0.000118129035,0.000052674215,0.00017775921,0.00013070754,0.000054991135],"domain_scores_gemma":[0.9947396,0.0026762863,0.00081281347,0.0011821811,0.0004118926,0.00017733824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013700932,0.00046685385,0.00029060713,0.0003953262,0.00015789554,0.0007692548,0.0006352854,0.00046366753,0.007105422],"category_scores_gemma":[0.010283374,0.00020871367,0.00025200765,0.00020236163,0.00050812383,0.001684904,0.00082935253,0.000754684,0.0014397386],"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":[0.00084100844,0.0007534916,0.2190839,0.0010206955,0.00008895608,0.0030145312,0.011441751,0.0072786273,0.14758654,0.012532753,0.00869425,0.5876634],"study_design_scores_gemma":[0.00029298506,0.003233983,0.60738593,0.00082072587,0.0002919489,0.011480036,0.010982802,0.037261985,0.16183893,0.05809005,0.108058296,0.00026245735],"about_ca_topic_score_codex":0.0009568781,"about_ca_topic_score_gemma":0.0016500367,"teacher_disagreement_score":0.007105422,"about_ca_system_score_codex":0.0001728324,"about_ca_system_score_gemma":0.00031681455,"threshold_uncertainty_score":0.023769975},"labels":[],"label_agreement":null},{"id":"W3137531678","doi":"10.1109/tmi.2021.3067688","title":"Constrained Domain Adaptation for Image Segmentation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Image segmentation; Computer vision; Artificial intelligence; Computer science; Image (mathematics); Adaptation (eye); Scale-space segmentation; Domain adaptation; Segmentation; Domain (mathematical analysis); Pattern recognition (psychology); Mathematics; Physics; Optics","score_opus":0.018226612705300668,"score_gpt":0.28463121135728553,"score_spread":0.26640459865198485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137531678","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":[0.00547181,0.0003869773,0.9909932,0.00016578341,0.000048929684,0.000034015844,0.00011061474,0.0011593603,0.0016293549],"genre_scores_gemma":[0.40110433,0.001533227,0.58154273,0.0008932204,0.00021264116,0.00039198855,0.0016160364,0.0014014696,0.011304345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999521,0.00012844215,0.00001830151,0.00016782875,0.00011247242,0.000051852676],"domain_scores_gemma":[0.99929476,0.00033921003,0.00007302425,0.00016647024,0.00008802661,0.000038512128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009575475,0.001189609,0.0009177427,0.0008112526,0.0003829282,0.00085668196,0.001616603,0.0015760874,0.0038290496],"category_scores_gemma":[0.0027663196,0.000639309,0.0010936534,0.0011164375,0.0012468493,0.0015182884,0.001996687,0.0025489794,0.0015242747],"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":[0.00007563668,0.000049765702,0.00035151737,0.00012008509,0.000066167944,0.00009425677,0.00008200471,0.8436322,0.011746644,0.015625004,0.005301757,0.12285502],"study_design_scores_gemma":[0.0000043516643,0.000010827642,0.000091019465,0.000009072581,0.0000051006705,0.00003375273,0.000007183425,0.98233056,0.002318343,0.013209098,0.001972959,0.000007628375],"about_ca_topic_score_codex":0.0041398713,"about_ca_topic_score_gemma":0.004367098,"teacher_disagreement_score":0.0041398713,"about_ca_system_score_codex":0.0011590862,"about_ca_system_score_gemma":0.001002033,"threshold_uncertainty_score":0.012809515},"labels":[],"label_agreement":null},{"id":"W3151722529","doi":"10.15607/rss.2021.xvii.012","title":"Learning Generalizable Robotic Reward Functions from “In-The-Wild” Human Videos","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office of Naval Research; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Artificial intelligence; Generalization; Reinforcement learning; Task (project management); Robot; Function (biology); Discriminator; Machine learning; Robotics; Human–computer interaction","score_opus":0.04143465606680853,"score_gpt":0.2716297009467223,"score_spread":0.23019504487991377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151722529","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":[0.24189803,0.0010041682,0.7488403,0.0012746798,0.00012109069,0.00018456785,0.0011926561,0.0026387884,0.0028458068],"genre_scores_gemma":[0.9191018,0.0003186976,0.07485108,0.00057421887,0.00007875833,0.00015561852,0.0017638691,0.00012038899,0.0030355111],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995003,0.00012133068,0.000016557995,0.00024201759,0.00005715154,0.000062614075],"domain_scores_gemma":[0.998743,0.0006492925,0.00018110992,0.0001823004,0.000117733645,0.00012657794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010880393,0.0013805032,0.0008637428,0.00049188756,0.00023269039,0.00061804446,0.0013614829,0.0014959346,0.0012779239],"category_scores_gemma":[0.005071912,0.0004330018,0.0006814766,0.00034291748,0.0011118625,0.0016655673,0.0010521696,0.0019467525,0.00036367812],"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":[0.000498554,0.0004508068,0.009167006,0.00021958012,0.00015890585,0.00030164048,0.00015776264,0.77104735,0.013234719,0.0073321573,0.006144728,0.19128686],"study_design_scores_gemma":[0.000020293863,0.000102335194,0.0012894314,0.000013341369,0.000008435757,0.000047425823,0.000017071327,0.98842794,0.0018812051,0.007684625,0.0004944477,0.0000134141155],"about_ca_topic_score_codex":0.0042248536,"about_ca_topic_score_gemma":0.0056832065,"teacher_disagreement_score":0.0042248536,"about_ca_system_score_codex":0.0010591969,"about_ca_system_score_gemma":0.0006863284,"threshold_uncertainty_score":0.008400559},"labels":[],"label_agreement":null},{"id":"W3152621714","doi":"","title":"Reducing Representation Drift in Online Continual Learning.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; McGill University","funders":"","keywords":"Forgetting; Computer science; Representation (politics); Focus (optics); Machine learning; Concept drift; Metric (unit); Streaming data; Entropy (arrow of time); Limiting; Artificial intelligence; Data stream mining; Data mining","score_opus":0.08157118274970762,"score_gpt":0.22179446655253468,"score_spread":0.14022328380282706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152621714","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":[0.10660989,0.0013477587,0.88575226,0.0006580025,0.00013052893,0.00015460097,0.00021899796,0.0030631202,0.0020647647],"genre_scores_gemma":[0.8619316,0.0002783219,0.13366356,0.00050388125,0.0001029209,0.00018036705,0.00058414025,0.00019513564,0.002560061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825567,0.000577402,0.000098969955,0.0005202345,0.0003969266,0.00015078456],"domain_scores_gemma":[0.9929559,0.00362613,0.0005990708,0.0017727053,0.0006550399,0.0003911092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046878103,0.0011759256,0.0014341739,0.00087388215,0.00070109084,0.0011891378,0.0035454268,0.0017890732,0.0018869881],"category_scores_gemma":[0.018926738,0.0006408808,0.00071414956,0.00079896057,0.0020707648,0.004553822,0.0039131152,0.0030782863,0.0007570793],"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":[0.0009926213,0.0008732527,0.00563988,0.00030670266,0.00023144642,0.00026122935,0.00050593517,0.56261367,0.007983318,0.014036523,0.005125968,0.4014294],"study_design_scores_gemma":[0.000024874753,0.00016444163,0.00039759392,0.000015622481,0.00001389393,0.000072781775,0.00003475416,0.97922164,0.0022913457,0.017080395,0.0006669494,0.000015815758],"about_ca_topic_score_codex":0.0027847385,"about_ca_topic_score_gemma":0.0025937771,"teacher_disagreement_score":0.0046878103,"about_ca_system_score_codex":0.0012371624,"about_ca_system_score_gemma":0.0010962115,"threshold_uncertainty_score":0.024791837},"labels":[],"label_agreement":null},{"id":"W3155346767","doi":"10.2139/ssrn.3808667","title":"Selecting Cover Images for Restaurant Reviews: AI vs. Wisdom of the Crowd","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"HEC Montréal; McGill University","funders":"","keywords":"Cover (algebra); Advertising; Business; Engineering","score_opus":0.012586770418017137,"score_gpt":0.2684149971293496,"score_spread":0.25582822671133243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155346767","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":[0.3829469,0.015351215,0.5707343,0.006132113,0.0013886213,0.00082104525,0.0020766375,0.0040000738,0.016549105],"genre_scores_gemma":[0.89593995,0.0014234933,0.09249515,0.0010695477,0.0013868463,0.00013887456,0.0020216615,0.00027258575,0.005251751],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795794,0.00074764935,0.00011809061,0.00064953306,0.00038613076,0.00014056491],"domain_scores_gemma":[0.9917025,0.0058424906,0.0004018998,0.00068334525,0.0009771122,0.0003926046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038378027,0.0014508284,0.002440087,0.0035052602,0.00091205264,0.0020555789,0.002070082,0.002462515,0.0026469221],"category_scores_gemma":[0.015313686,0.0005915153,0.00092446303,0.0017284071,0.00094588625,0.0042197295,0.0014210301,0.0020673866,0.0017279447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021237286,0.0013693038,0.02378997,0.0016191064,0.0009362223,0.0005660884,0.0009214725,0.08762679,0.017980859,0.007029273,0.063679725,0.7923575],"study_design_scores_gemma":[0.00008275316,0.000291127,0.0074950755,0.000090238726,0.00014614903,0.00043859537,0.00039023435,0.96718377,0.004227118,0.015202917,0.0043891985,0.00006283376],"about_ca_topic_score_codex":0.0054321806,"about_ca_topic_score_gemma":0.0073530897,"teacher_disagreement_score":0.0054321806,"about_ca_system_score_codex":0.00079099013,"about_ca_system_score_gemma":0.0007865922,"threshold_uncertainty_score":0.020296514},"labels":[],"label_agreement":null},{"id":"W3155471926","doi":"10.1145/3404835.3462837","title":"Privacy Protection in Deep Multi-modal Retrieval","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"China Scholarship Council; Arctic Institute of North America","keywords":"Computer science; Modal; Hash function; Information privacy; Deep learning; Information retrieval; Modality (human–computer interaction); Information sensitivity; Obfuscation; Artificial intelligence; Data mining; Machine learning; Computer security","score_opus":0.04161706387522086,"score_gpt":0.27444222665299595,"score_spread":0.2328251627777751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155471926","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02158156,0.0003324035,0.97526574,0.00050439104,0.000020636873,0.000047504454,0.00009038081,0.00044490557,0.0017125302],"genre_scores_gemma":[0.88736945,0.00033287506,0.10802392,0.00043050107,0.000064710286,0.00012541248,0.00021469557,0.00011906245,0.0033191964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963329,0.0014794681,0.00018029008,0.0006922129,0.0009857817,0.00032927195],"domain_scores_gemma":[0.9927383,0.0031470584,0.0005765884,0.0029003595,0.0004517419,0.00018596026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043830713,0.0006658524,0.0010890113,0.0005347497,0.0009317917,0.0018794028,0.0019236645,0.0018312015,0.001704118],"category_scores_gemma":[0.014134701,0.0005020213,0.00086864905,0.00067448517,0.002443325,0.0057087103,0.004596243,0.0028328637,0.0005856605],"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":[0.00077522366,0.00022942376,0.0025169651,0.00035135276,0.00017479958,0.00046977203,0.0008224416,0.5287427,0.028110413,0.19046256,0.0062091067,0.24113522],"study_design_scores_gemma":[0.000023076227,0.00007552418,0.00024701367,0.000018014993,0.000020066189,0.00018474481,0.00007618922,0.9069699,0.008243493,0.08234677,0.0017721943,0.000023043329],"about_ca_topic_score_codex":0.001145193,"about_ca_topic_score_gemma":0.0010310767,"teacher_disagreement_score":0.0043830713,"about_ca_system_score_codex":0.0012426985,"about_ca_system_score_gemma":0.0013059269,"threshold_uncertainty_score":0.023180187},"labels":[],"label_agreement":null},{"id":"W3155713909","doi":"10.1186/s40537-021-00455-5","title":"Domain randomization for neural network classification","year":2021,"lang":"en","type":"article","venue":"Journal Of Big Data","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Classifier (UML); Convolutional neural network; Artificial intelligence; Artificial neural network; Task (project management); Domain (mathematical analysis); Machine learning; Pattern recognition (psychology); Randomization; Transfer of learning; Contextual image classification; Data mining; Image (mathematics); Clinical trial; Mathematics","score_opus":0.1690613487970136,"score_gpt":0.3181836121059016,"score_spread":0.14912226330888803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155713909","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":[0.04178901,0.0013293845,0.9510252,0.0006740045,0.00016915044,0.00013055834,0.0005122534,0.002135514,0.0022349537],"genre_scores_gemma":[0.65922904,0.0008071466,0.33284378,0.00060101174,0.00019432108,0.00060355745,0.0021153314,0.00034732049,0.0032584784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988701,0.0005213528,0.000060334627,0.00031264988,0.00016847356,0.000067009554],"domain_scores_gemma":[0.9967043,0.0017842018,0.00024132377,0.0009003299,0.00027632783,0.00009349998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027524685,0.0007010297,0.0008125714,0.0005964385,0.00045456804,0.0008933873,0.001345253,0.0011444085,0.0021942202],"category_scores_gemma":[0.011886481,0.00039772558,0.0007602952,0.00069410104,0.0011059574,0.0018049671,0.0013432052,0.0024441457,0.00093182013],"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":[0.00042146337,0.0002286327,0.0034967728,0.00027615542,0.00013626645,0.00014659883,0.00009652542,0.7257523,0.009950066,0.043512296,0.009505554,0.20647742],"study_design_scores_gemma":[0.000012720586,0.000041149622,0.00033140084,0.0000149588395,0.0000057164953,0.000036512876,0.000011709168,0.96647936,0.0023719333,0.029086115,0.0015976911,0.000010605221],"about_ca_topic_score_codex":0.001582684,"about_ca_topic_score_gemma":0.0014848956,"teacher_disagreement_score":0.0027524685,"about_ca_system_score_codex":0.0010105044,"about_ca_system_score_gemma":0.0008148433,"threshold_uncertainty_score":0.014556587},"labels":[],"label_agreement":null},{"id":"W3157199058","doi":"","title":"Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data","year":2021,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Autoencoder; Imputation (statistics); Computer science; Missing data; Data modeling; Data mining; Machine learning; Artificial intelligence; Deep learning; Pattern recognition (psychology)","score_opus":0.25313555036735474,"score_gpt":0.3503186380782304,"score_spread":0.09718308771087564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157199058","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012257755,0.000294383,0.9865507,0.00014606395,0.00002629601,0.000023276674,0.00007441614,0.00029177024,0.0003352786],"genre_scores_gemma":[0.62218624,0.0008817654,0.3711603,0.00053262064,0.00013642487,0.0002026384,0.0014135174,0.00019593262,0.0032906176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999405,0.0002299849,0.000029949651,0.00017143102,0.00010968864,0.000053984073],"domain_scores_gemma":[0.99875593,0.00079660676,0.00008778454,0.00017223213,0.00014192825,0.0000455926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016558353,0.0008796913,0.0010910217,0.00043115387,0.0002554118,0.0006545497,0.0017386168,0.0010019963,0.0007675454],"category_scores_gemma":[0.0040437505,0.00062682026,0.00089485216,0.00069572456,0.0008545191,0.0014521913,0.0012752496,0.0019098832,0.00026650022],"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":[0.00010161193,0.000079898025,0.0015996606,0.0001053079,0.00018758864,0.00010473011,0.000106041465,0.857723,0.004251192,0.013689585,0.002305555,0.11974588],"study_design_scores_gemma":[0.0000032911073,0.000008826178,0.000081791695,0.0000042090887,0.000004972088,0.000009758281,0.0000038902926,0.9960019,0.00040834994,0.0032718598,0.00019819876,0.0000029226637],"about_ca_topic_score_codex":0.0060199224,"about_ca_topic_score_gemma":0.007026808,"teacher_disagreement_score":0.0060199224,"about_ca_system_score_codex":0.0005514798,"about_ca_system_score_gemma":0.0011454413,"threshold_uncertainty_score":0.011969745},"labels":[],"label_agreement":null},{"id":"W3157424867","doi":"","title":"A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix","year":2021,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University","funders":"","keywords":"Forgetting; Computer science; Gradient descent; Principal component analysis; Similarity (geometry); Task (project management); Matrix (chemical analysis); Stochastic gradient descent; Field (mathematics); Measure (data warehouse); Artificial intelligence; Algorithm; Data mining; Mathematics; Image (mathematics); Chemistry; Engineering; Psychology","score_opus":0.08335431458207082,"score_gpt":0.3645647301400853,"score_spread":0.28121041555801446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157424867","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":[0.03441267,0.00061410136,0.9618943,0.0005666404,0.000053398977,0.000053530624,0.000068548776,0.00026666382,0.0020700826],"genre_scores_gemma":[0.8323093,0.0008874481,0.15985624,0.00038921495,0.00022162632,0.00033370915,0.00027760098,0.00016306766,0.0055618268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99797493,0.00052070833,0.000120899684,0.00046986254,0.00066692784,0.00024674193],"domain_scores_gemma":[0.9865341,0.008244654,0.0014894194,0.0015054321,0.001467317,0.00075896154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039696405,0.0009864081,0.0013538154,0.0012446791,0.0011526286,0.0014970432,0.002809747,0.0016232034,0.0028871368],"category_scores_gemma":[0.025072731,0.0006269054,0.0007627215,0.0011888705,0.0035679177,0.0045863516,0.0030951207,0.0027852233,0.00048055482],"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":[0.0003250063,0.00023180591,0.0075741434,0.00038978946,0.00015946745,0.0005348237,0.0007453031,0.52273387,0.005233311,0.33652157,0.0039141374,0.12163685],"study_design_scores_gemma":[0.000013419535,0.00009280528,0.0008319917,0.000026219657,0.000014976257,0.00013699509,0.000038530514,0.9017296,0.0008994603,0.095322534,0.0008687185,0.000024836301],"about_ca_topic_score_codex":0.00383996,"about_ca_topic_score_gemma":0.0035431213,"teacher_disagreement_score":0.0039696405,"about_ca_system_score_codex":0.0016242347,"about_ca_system_score_gemma":0.0017879902,"threshold_uncertainty_score":0.02099371},"labels":[],"label_agreement":null},{"id":"W3157573123","doi":"10.48550/arxiv.2105.00157","title":"A Deep Learning Framework for Lifelong Machine Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Western University","funders":"","keywords":"Lifelong learning; Artificial intelligence; Computer science; Deep learning; Psychology; Machine learning; Cognitive science; Pedagogy","score_opus":0.06755863258608626,"score_gpt":0.20625510398506655,"score_spread":0.13869647139898028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157573123","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015621262,0.0005856088,0.99581146,0.00041934717,0.00003458606,0.000022361597,0.0001086354,0.00029403594,0.0011618726],"genre_scores_gemma":[0.2610769,0.002260815,0.7243707,0.00079603604,0.00027996025,0.00059535424,0.0010332731,0.00024997734,0.009337026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995005,0.00018303188,0.000027318043,0.00012091775,0.00011918042,0.000049038776],"domain_scores_gemma":[0.9992605,0.00030971837,0.000060594673,0.00017016966,0.000117982854,0.00008100487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014175329,0.000816295,0.0007429364,0.0008614931,0.0005050867,0.0013392441,0.0022051423,0.0012971516,0.0026395305],"category_scores_gemma":[0.0030537276,0.00047232624,0.00061963015,0.0009901555,0.0015343577,0.0028200706,0.0024596446,0.0030549166,0.00090713083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061448045,0.000109158806,0.0009333184,0.00023510266,0.00008782741,0.00011978475,0.00019187057,0.2799651,0.0023960515,0.5510281,0.01108431,0.15378797],"study_design_scores_gemma":[0.0000090378635,0.000032416097,0.0001277468,0.000027757857,0.000007155664,0.00003228162,0.000010605484,0.70348287,0.00052132766,0.2876276,0.008109483,0.000011737431],"about_ca_topic_score_codex":0.0040548234,"about_ca_topic_score_gemma":0.006079674,"teacher_disagreement_score":0.0040548234,"about_ca_system_score_codex":0.0014684385,"about_ca_system_score_gemma":0.001467205,"threshold_uncertainty_score":0.010654271},"labels":[],"label_agreement":null},{"id":"W3157787540","doi":"10.1007/978-981-16-2336-3_7","title":"Episodic Training for Domain Generalization Using Latent Domains","year":2021,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Artificial intelligence; Domain (mathematical analysis); Generalization; Classifier (UML); Machine learning; Feature (linguistics); Extractor; Cluster analysis; Pattern recognition (psychology); Mathematics","score_opus":0.11452061898146186,"score_gpt":0.3243889848396134,"score_spread":0.20986836585815155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157787540","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":[0.008144715,0.0008483435,0.98804784,0.00015703644,0.00006823705,0.00002767735,0.00010960978,0.0010436068,0.0015529251],"genre_scores_gemma":[0.4439552,0.0015491176,0.53490126,0.00056293095,0.00022461987,0.00025569365,0.0020290094,0.00052202336,0.01600011],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995733,0.00013682898,0.000024431525,0.00016123008,0.000059456463,0.000044761477],"domain_scores_gemma":[0.99862564,0.00085064763,0.000036745787,0.00032093082,0.00011797234,0.00004807697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010998076,0.0007280202,0.0010969249,0.00058130216,0.0004171358,0.0006443322,0.0018617158,0.0012085745,0.005089797],"category_scores_gemma":[0.0030409966,0.00053600734,0.0008465887,0.00077541464,0.0007308905,0.0022690843,0.0019244683,0.0024504706,0.0013444544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018643333,0.00018614922,0.00086398894,0.00019076712,0.00013569846,0.00012046847,0.00016771727,0.23590846,0.00751101,0.026397524,0.014763334,0.71356845],"study_design_scores_gemma":[0.000009076263,0.000030300727,0.00016031225,0.000017024762,0.00001621629,0.00003934057,0.000019933426,0.9735373,0.0019211592,0.02280714,0.0014355292,0.0000066178736],"about_ca_topic_score_codex":0.0033444245,"about_ca_topic_score_gemma":0.0046401895,"teacher_disagreement_score":0.005089797,"about_ca_system_score_codex":0.00057174877,"about_ca_system_score_gemma":0.00057136494,"threshold_uncertainty_score":0.01702702},"labels":[],"label_agreement":null},{"id":"W3160314846","doi":"10.1109/cvpr52688.2022.01434","title":"When Does Contrastive Visual Representation Learning Work?","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology; McGill University; California Institute of Technology; Danmarks Grundforskningsfond; National Science Foundation","keywords":"Computer science; Artificial intelligence; Supervised learning; Machine learning; Granularity; Task (project management); Representation (politics); Semi-supervised learning; Feature learning; Field (mathematics); Unsupervised learning; Labeled data; Domain (mathematical analysis); Natural language processing; Pattern recognition (psychology); Artificial neural network; Mathematics","score_opus":0.0420139713652861,"score_gpt":0.29057369130751903,"score_spread":0.24855971994223294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160314846","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":[0.13108814,0.032868553,0.7117782,0.07114044,0.0021241554,0.0005734005,0.0011779438,0.0053341705,0.043915004],"genre_scores_gemma":[0.8253009,0.004478908,0.1547265,0.005927712,0.0013095373,0.00034085807,0.00076431385,0.0011651806,0.0059860526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99445665,0.0024462556,0.00018597275,0.0018713425,0.0007193185,0.00032034717],"domain_scores_gemma":[0.969088,0.022138637,0.001246869,0.004300954,0.0023852224,0.00084031845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014056997,0.0013848506,0.0017364392,0.0012920303,0.0010591662,0.005878685,0.002391218,0.0054045157,0.0067146043],"category_scores_gemma":[0.10256736,0.00076310756,0.00065274996,0.0010545423,0.004145598,0.017162427,0.0029465926,0.0049625956,0.0045165434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012683553,0.00060236617,0.016180277,0.0012169435,0.00042711876,0.00023920447,0.0012114728,0.029402029,0.009461677,0.102994926,0.04220835,0.7947872],"study_design_scores_gemma":[0.00021297408,0.0008494912,0.006817783,0.0007366125,0.000120248784,0.0007190045,0.0009431017,0.31806383,0.01344633,0.63884497,0.019114524,0.00013113333],"about_ca_topic_score_codex":0.0017365474,"about_ca_topic_score_gemma":0.0014594884,"teacher_disagreement_score":0.014056997,"about_ca_system_score_codex":0.001213804,"about_ca_system_score_gemma":0.0010370101,"threshold_uncertainty_score":0.07434136},"labels":[],"label_agreement":null},{"id":"W3160681497","doi":"10.36227/techrxiv.14565078.v1","title":"Methods for maintenance of neural networks in continual learning scenarios","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"MNIST database; Computer science; Artificial neural network; Artificial intelligence; Novelty detection; Machine learning; Novelty; Forgetting; Representation (politics); Perspective (graphical); Correctness; Data mining; Algorithm","score_opus":0.03403171184187791,"score_gpt":0.3440928230028309,"score_spread":0.310061111160953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160681497","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00565976,0.00032311722,0.9925211,0.000120254685,0.000027337623,0.000040219704,0.000045506837,0.00072996295,0.00053263444],"genre_scores_gemma":[0.37546888,0.0008134773,0.61704206,0.00019174215,0.0002319997,0.00042642126,0.0004556076,0.0005111637,0.0048586214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803656,0.00044254138,0.00015340785,0.0006230165,0.0005799067,0.00016466249],"domain_scores_gemma":[0.98772615,0.0052562845,0.0015794033,0.0031005368,0.0019591567,0.00037844668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049409103,0.0011063789,0.0011109107,0.0024083767,0.00076749787,0.001693488,0.00498716,0.0017129302,0.0030540368],"category_scores_gemma":[0.022212537,0.0008545691,0.0012526225,0.001268263,0.0019226529,0.0047678356,0.0034307498,0.0030097824,0.00081516715],"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":[0.00017312467,0.00012192603,0.004236532,0.0002593811,0.00015334925,0.00018613764,0.00031581052,0.5416363,0.00428503,0.079823814,0.0033793994,0.36542922],"study_design_scores_gemma":[0.000006036338,0.00003345649,0.00027350395,0.0000141794235,0.000012327018,0.00004287925,0.000018179431,0.9767369,0.000792468,0.02116989,0.000890177,0.000010046321],"about_ca_topic_score_codex":0.0038677962,"about_ca_topic_score_gemma":0.0039572883,"teacher_disagreement_score":0.00498716,"about_ca_system_score_codex":0.0019448114,"about_ca_system_score_gemma":0.0013300311,"threshold_uncertainty_score":0.026130378},"labels":[],"label_agreement":null},{"id":"W3161607120","doi":"10.1109/icpr48806.2021.9412906","title":"Foreground-focused domain adaption for object detection","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Pipeline (software); Domain (mathematical analysis); Cityscape; Object detection; Benchmark (surveying); Object (grammar); Computer vision; Backpropagation; Inference; Domain adaptation; Pattern recognition (psychology); Adaptation (eye); Deep learning; Artificial neural network; Mathematics","score_opus":0.026221798236609133,"score_gpt":0.25253804065966373,"score_spread":0.2263162424230546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161607120","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":[0.03259088,0.0010387548,0.9578759,0.00024236302,0.0001316558,0.00009254613,0.00030449705,0.004957104,0.0027661957],"genre_scores_gemma":[0.6008044,0.000942652,0.3848508,0.000812116,0.00018190796,0.00014835443,0.0022489503,0.00056847563,0.009442464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994312,0.00009122984,0.000019577941,0.00024579588,0.0001377896,0.00007436127],"domain_scores_gemma":[0.9992908,0.00022083629,0.000054991997,0.00025144697,0.00014470483,0.000037139216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009096053,0.0012366723,0.00096732186,0.00086808763,0.00041617046,0.00094184704,0.0017773034,0.0010968596,0.0018499468],"category_scores_gemma":[0.0023631025,0.0004106565,0.0009521786,0.00083252526,0.00081001554,0.0015040705,0.0013906042,0.0020752016,0.0020994602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027728794,0.0002549744,0.004460736,0.00019372851,0.00022174596,0.000239417,0.00014636482,0.22063872,0.047354776,0.0074636666,0.016336221,0.70241225],"study_design_scores_gemma":[0.000010944206,0.000055825967,0.001112498,0.000015932172,0.000023001128,0.00022430552,0.00003256542,0.9636173,0.021955457,0.008568779,0.00436502,0.000018256433],"about_ca_topic_score_codex":0.0031417697,"about_ca_topic_score_gemma":0.004383768,"teacher_disagreement_score":0.0031417697,"about_ca_system_score_codex":0.00074617617,"about_ca_system_score_gemma":0.0008301914,"threshold_uncertainty_score":0.0062469244},"labels":[],"label_agreement":null},{"id":"W3163104826","doi":"10.32473/flairs.v34i1.128490","title":"Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pairwise comparison; Mixture model; Robustness (evolution); Cluster analysis; Class (philosophy); Computer science; Artificial intelligence; Synthetic data; Machine learning; Pattern recognition (psychology); Data mining","score_opus":0.16453687586872798,"score_gpt":0.3525063937222555,"score_spread":0.18796951785352753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163104826","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":[0.0027836186,0.00008241201,0.9965125,0.000069728376,0.000009567097,0.000024080437,0.000033423163,0.0002727577,0.00021204319],"genre_scores_gemma":[0.28111562,0.0003211341,0.71053314,0.00054510235,0.00018110333,0.0006156776,0.001086232,0.00043839612,0.005163702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995439,0.0019226519,0.00021811081,0.0010905032,0.0010719739,0.0002577725],"domain_scores_gemma":[0.9933802,0.0032930188,0.00067108456,0.0011911531,0.001165018,0.0002994797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004055657,0.0015064727,0.0031142563,0.002004428,0.000866248,0.0025040242,0.0057828175,0.0023880769,0.0019880391],"category_scores_gemma":[0.009794896,0.0014039436,0.0020709105,0.002040708,0.0021217382,0.0042169695,0.0042364467,0.0038644008,0.0013201721],"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":[0.0004964379,0.00032220528,0.003402407,0.0003377122,0.00042955708,0.00019385858,0.0005257257,0.65097636,0.009372731,0.049300406,0.0050605433,0.27958196],"study_design_scores_gemma":[0.0000070999877,0.000021329346,0.000084952524,0.000005211591,0.000008545375,0.0000143975985,0.000006350232,0.98868704,0.000659233,0.010142533,0.00035455654,0.00000878072],"about_ca_topic_score_codex":0.0031084097,"about_ca_topic_score_gemma":0.0038558599,"teacher_disagreement_score":0.0057828175,"about_ca_system_score_codex":0.0013927467,"about_ca_system_score_gemma":0.0017400582,"threshold_uncertainty_score":0.021448672},"labels":[],"label_agreement":null},{"id":"W3163495678","doi":"10.48550/arxiv.2104.09393","title":"Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Microsoft (Canada)","funders":"","keywords":"Computer science; Inference; Transformer; Artificial intelligence; Machine learning; Engineering","score_opus":0.07997889935552076,"score_gpt":0.2020436194366011,"score_spread":0.12206472008108034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163495678","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16765524,0.0018772655,0.8137768,0.00060874404,0.0001855737,0.00013932922,0.00053406577,0.008981532,0.006241555],"genre_scores_gemma":[0.8789931,0.0005496845,0.103789054,0.0003820353,0.000115704075,0.00009154317,0.0015240769,0.00041529755,0.014139609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993463,0.0001763854,0.000041260864,0.0001935549,0.00013870499,0.00010379353],"domain_scores_gemma":[0.99878734,0.0004356528,0.00009281113,0.00028217968,0.00033483986,0.00006710561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017111951,0.0008359234,0.0012410365,0.0009471772,0.00037033486,0.0012562549,0.0017169587,0.0011672715,0.0025550663],"category_scores_gemma":[0.0035749888,0.00034398914,0.00087973283,0.0009317021,0.0005435657,0.0033925779,0.0009790979,0.001738746,0.0021698033],"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":[0.00073336816,0.0006033279,0.0029387816,0.00022364802,0.0001823316,0.00015698806,0.0001454591,0.4938926,0.020125559,0.011940195,0.013448171,0.4556096],"study_design_scores_gemma":[0.000012510177,0.000050988612,0.00014873648,0.000002877626,0.0000134435195,0.000024619638,0.0000061878386,0.99563134,0.0017753132,0.0019281211,0.00039705995,0.000008902512],"about_ca_topic_score_codex":0.011782977,"about_ca_topic_score_gemma":0.014363531,"teacher_disagreement_score":0.011782977,"about_ca_system_score_codex":0.0010422419,"about_ca_system_score_gemma":0.0014208022,"threshold_uncertainty_score":0.023428798},"labels":[],"label_agreement":null},{"id":"W3165550512","doi":"10.3389/fninf.2022.805117","title":"Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian VIGOUR Centre; University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Neuroimaging; Matching (statistics); Deep learning; Domain (mathematical analysis); Domain adaptation; Adaptation (eye); Adversarial system; Pattern recognition (psychology); Psychology","score_opus":0.02774686720607144,"score_gpt":0.27564578081540303,"score_spread":0.24789891360933158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165550512","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":[0.36931944,0.032152295,0.58347124,0.0015595278,0.0008067762,0.0003240112,0.00046357943,0.0025643501,0.009338823],"genre_scores_gemma":[0.8349912,0.0076112226,0.15143295,0.00039006674,0.00029153307,0.00012222157,0.001114135,0.000321083,0.0037256496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850583,0.0006657799,0.00009267434,0.00030080255,0.00033902,0.000095871255],"domain_scores_gemma":[0.9938599,0.004031369,0.00022831571,0.0008980025,0.0008183422,0.00016409495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055498774,0.0011675317,0.0009328239,0.0009661043,0.00031693117,0.0009321825,0.0010037965,0.0011739507,0.0012135122],"category_scores_gemma":[0.01047249,0.00028992858,0.0011420002,0.0007266264,0.00070123887,0.0017708889,0.0010276311,0.0015721776,0.00047383556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021478536,0.0009639277,0.0076785414,0.00093226373,0.0010969264,0.00028617258,0.00024328298,0.37618142,0.010352971,0.004414728,0.0065592667,0.5891426],"study_design_scores_gemma":[0.00006378407,0.000668054,0.0037636766,0.00009178382,0.00019477888,0.00024328678,0.00010139429,0.9814642,0.007975511,0.0022605374,0.003129261,0.00004366149],"about_ca_topic_score_codex":0.0036469623,"about_ca_topic_score_gemma":0.002552222,"teacher_disagreement_score":0.0055498774,"about_ca_system_score_codex":0.00056438363,"about_ca_system_score_gemma":0.00065117294,"threshold_uncertainty_score":0.029350936},"labels":[],"label_agreement":null},{"id":"W3166318900","doi":"10.1109/syscon48628.2021.9447110","title":"Transfer Learning on the Edge Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transfer of learning; Server; Enhanced Data Rates for GSM Evolution; Latency (audio); Domain (mathematical analysis); Task (project management); Distributed computing; Artificial intelligence; Transfer (computing); Edge device; Machine learning; Human–computer interaction; Computer network; Operating system; Telecommunications; Engineering","score_opus":0.02464537511911352,"score_gpt":0.22754853578425566,"score_spread":0.20290316066514213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166318900","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":[0.06187784,0.00025027062,0.9345183,0.00026709627,0.000035855264,0.00007559086,0.000068121924,0.0006478878,0.002259196],"genre_scores_gemma":[0.8390843,0.00028029713,0.15353371,0.0003364047,0.000060686933,0.00019049762,0.00030342684,0.000086633816,0.0061240504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993876,0.00016959927,0.00002601539,0.00022200606,0.000104468956,0.000090244255],"domain_scores_gemma":[0.9981628,0.0010397731,0.0001057821,0.0002667286,0.00034281507,0.00008214428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014252749,0.00074963825,0.0007078052,0.0007108614,0.0005757155,0.0007864037,0.0014592606,0.0012611853,0.0016647221],"category_scores_gemma":[0.0056954045,0.00033633836,0.00055743067,0.00065704156,0.0010458346,0.002783751,0.0016699568,0.0018920259,0.0004897425],"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":[0.00019275241,0.00013195314,0.001700336,0.0000541133,0.000044160766,0.00015552745,0.00010037164,0.8258584,0.0035980002,0.010173689,0.0013736668,0.15661709],"study_design_scores_gemma":[0.0000029054563,0.00002143235,0.00014338078,0.0000020240245,0.0000034674479,0.0000094013785,0.000008576845,0.9933564,0.0009226066,0.005336596,0.0001900978,0.0000030132317],"about_ca_topic_score_codex":0.0064769555,"about_ca_topic_score_gemma":0.0039548418,"teacher_disagreement_score":0.0064769555,"about_ca_system_score_codex":0.0010594988,"about_ca_system_score_gemma":0.0006570886,"threshold_uncertainty_score":0.012878537},"labels":[],"label_agreement":null},{"id":"W3167810990","doi":"","title":"Efficient and robust multi-task learning in the brain with modular task primitives.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Modular design; Task (project management); Leverage (statistics); Artificial intelligence; Modularity (biology); Multi-task learning; Artificial neural network; Machine learning; Human–computer interaction","score_opus":0.058560388132925655,"score_gpt":0.182534238062437,"score_spread":0.12397384992951133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167810990","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":[0.18332574,0.0003896329,0.8128502,0.0005362222,0.000040593364,0.00008804306,0.00009104009,0.00050136703,0.0021771363],"genre_scores_gemma":[0.88543886,0.00011433703,0.11271443,0.00012339505,0.000018633351,0.00009504952,0.0001339493,0.00005048018,0.001310885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967456,0.00010550774,0.000013922764,0.000119206976,0.00004736962,0.000039508846],"domain_scores_gemma":[0.99838173,0.00078151055,0.00022673777,0.0003403304,0.000108690634,0.00016104127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001262342,0.0006641722,0.00055144384,0.00032255947,0.0003076888,0.00062009715,0.0011058484,0.001075489,0.0008991687],"category_scores_gemma":[0.005817182,0.00036650305,0.00062426645,0.00028177176,0.0013079285,0.0024381739,0.0018797016,0.0015025095,0.00022994675],"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":[0.00033730012,0.00027372633,0.0025773072,0.00026403958,0.0001922626,0.00017781401,0.00026804136,0.7682724,0.054114208,0.03154338,0.0017423274,0.1402372],"study_design_scores_gemma":[0.000013813556,0.00008154924,0.000688053,0.00000779838,0.000012804066,0.00003952673,0.00001717139,0.95914,0.0037585453,0.03585073,0.00038013863,0.000009954447],"about_ca_topic_score_codex":0.0012035627,"about_ca_topic_score_gemma":0.0016631387,"teacher_disagreement_score":0.001262342,"about_ca_system_score_codex":0.00070928945,"about_ca_system_score_gemma":0.0005699242,"threshold_uncertainty_score":0.0066759586},"labels":[],"label_agreement":null},{"id":"W3168615033","doi":"10.1007/978-3-031-17587-9_7","title":"SPeCiaL: Self-supervised Pretraining for Continual Learning","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":15,"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":"Forgetting; Computer science; Artificial intelligence; Machine learning; Process (computing); Supervised learning; Unsupervised learning; Artificial neural network","score_opus":0.022553856770973903,"score_gpt":0.2512191963977332,"score_spread":0.2286653396267593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168615033","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005845368,0.018974874,0.73822373,0.0024427404,0.028935706,0.0003366739,0.003823278,0.04315882,0.1582588],"genre_scores_gemma":[0.03658401,0.006885745,0.14971885,0.0014176889,0.009412837,0.0002867319,0.012358878,0.010167604,0.7731677],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971575,0.000020546338,0.000015950161,0.00011737901,0.00009848004,0.000031908065],"domain_scores_gemma":[0.99918646,0.00022000146,0.000023516825,0.00018996952,0.000266133,0.00011398948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042467666,0.0014237841,0.0012519557,0.00085709704,0.00041024483,0.001212041,0.0015641713,0.0012128714,0.15344368],"category_scores_gemma":[0.0012614417,0.00053566793,0.000623298,0.001017932,0.00034421188,0.0025122066,0.00140229,0.0018711168,0.09885663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107872605,0.00011158901,0.00007836762,0.000366674,0.00002736999,0.00006789207,0.000025149764,0.0024073136,0.0076434254,0.0031734882,0.4390385,0.5469524],"study_design_scores_gemma":[0.000038367943,0.00024130297,0.0012519028,0.00020479059,0.00006428486,0.0010114977,0.00003753932,0.06898592,0.031895034,0.027709886,0.86848295,0.00007656837],"about_ca_topic_score_codex":0.000768264,"about_ca_topic_score_gemma":0.0017607975,"teacher_disagreement_score":0.15344368,"about_ca_system_score_codex":0.0004348696,"about_ca_system_score_gemma":0.00048374207,"threshold_uncertainty_score":0.5133202},"labels":[],"label_agreement":null},{"id":"W3169278660","doi":"10.48550/arxiv.2106.07636","title":"Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Kernel (algebra); Exploit; Machine learning; Sample (material); Task (project management); Artificial intelligence; Multiple kernel learning; Kernel method; Data mining; Mathematics; Support vector machine","score_opus":0.4448532497117624,"score_gpt":0.2457316516869512,"score_spread":0.19912159802481116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169278660","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039060894,0.00036542845,0.95762914,0.00033756602,0.000047544116,0.00012839849,0.000100902784,0.0016426696,0.0006874633],"genre_scores_gemma":[0.6595585,0.00015464578,0.33756268,0.000394592,0.00012076638,0.0003865015,0.0005669412,0.000353265,0.000902043],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9894476,0.006518382,0.0005401009,0.0017032983,0.0014101937,0.00038040098],"domain_scores_gemma":[0.9196334,0.05442463,0.0044167973,0.016798306,0.0028598637,0.0018669174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018051565,0.0017779032,0.0025894335,0.0024053643,0.0008149026,0.0019708814,0.0056756213,0.0039756973,0.0019406362],"category_scores_gemma":[0.1001978,0.0007474041,0.0017277022,0.0017329504,0.0036455588,0.006315256,0.006029997,0.0042915405,0.0009969847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024890911,0.0010238735,0.03694877,0.0007588954,0.00087227125,0.0005657419,0.00077829364,0.2791921,0.017378703,0.08173337,0.0059870025,0.5722719],"study_design_scores_gemma":[0.00008276152,0.00036803953,0.0015591966,0.00004158813,0.00004739726,0.00025133239,0.000051682488,0.9421157,0.005792027,0.048818897,0.00083092216,0.000040384242],"about_ca_topic_score_codex":0.0006528962,"about_ca_topic_score_gemma":0.00079670106,"teacher_disagreement_score":0.018051565,"about_ca_system_score_codex":0.0010834355,"about_ca_system_score_gemma":0.0016318338,"threshold_uncertainty_score":0.09546691},"labels":[],"label_agreement":null},{"id":"W3169315049","doi":"10.1007/s10994-021-06080-w","title":"On the benefits of representation regularization in invariance based domain generalization","year":2022,"lang":"en","type":"article","venue":"Machine Learning","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Mila - Quebec Artificial Intelligence Institute; Vector Institute; Western University; Université Laval","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs; Canadian Institute for Advanced Research","keywords":"Regularization (linguistics); Invariant (physics); Computer science; Generalization error; Artificial intelligence; Representation (politics); Generalization; Machine learning; Feature learning; Robustness (evolution); Algorithm; Mathematics; Unsupervised learning","score_opus":0.021319547624807592,"score_gpt":0.24230291248694885,"score_spread":0.22098336486214126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169315049","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":[0.02001654,0.0002470842,0.97789943,0.0003090921,0.000026842548,0.00003697558,0.00002768625,0.00048179485,0.00095457776],"genre_scores_gemma":[0.6657709,0.00049787236,0.3292849,0.0006429783,0.00015621736,0.00016652215,0.0003541668,0.00033050342,0.00279601],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878174,0.00044884198,0.000049937244,0.00037138513,0.00024583723,0.00010234044],"domain_scores_gemma":[0.9966157,0.0016139998,0.0002614338,0.0010387448,0.00031397378,0.00015603544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002685024,0.00090901315,0.0011799713,0.0007831973,0.0007548933,0.00093707483,0.0017320432,0.0014956568,0.0013908605],"category_scores_gemma":[0.009696527,0.00039588133,0.0010652412,0.00082644384,0.0020417618,0.003006952,0.003071974,0.002901051,0.000632932],"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":[0.00022187237,0.00024173102,0.002548186,0.000117011485,0.000116632255,0.00020361521,0.00033325938,0.6598622,0.019309195,0.07636348,0.0045271153,0.23615567],"study_design_scores_gemma":[0.000008071393,0.000051258605,0.0002277162,0.000006905382,0.000008782202,0.000049535494,0.000016416046,0.97613084,0.0016542922,0.021265872,0.00056870363,0.000011596255],"about_ca_topic_score_codex":0.0023994367,"about_ca_topic_score_gemma":0.0019089128,"teacher_disagreement_score":0.002685024,"about_ca_system_score_codex":0.00073568115,"about_ca_system_score_gemma":0.0010511171,"threshold_uncertainty_score":0.014199913},"labels":[],"label_agreement":null},{"id":"W3169317873","doi":"10.48550/arxiv.2106.03632","title":"Quantifying and Improving Transferability in Domain Generalization","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"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 Waterloo","funders":"","keywords":"Transferability; Computer science; Classifier (UML); Generalization; Benchmark (surveying); Invariant (physics); Artificial intelligence; Machine learning; Domain (mathematical analysis); Algorithm; Transfer of learning; Theoretical computer science; Mathematics","score_opus":0.09783610275853746,"score_gpt":0.20525254910746235,"score_spread":0.10741644634892489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169317873","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.089456685,0.00092474127,0.90337116,0.0008459046,0.00008675595,0.00011305704,0.0001859195,0.0028138072,0.0022020082],"genre_scores_gemma":[0.76636916,0.00053021737,0.22774751,0.0007520423,0.00013192851,0.00021808078,0.0011771034,0.0005699534,0.002504085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963995,0.0012000436,0.00024738946,0.0012493161,0.0006569431,0.00024676858],"domain_scores_gemma":[0.9871029,0.006283153,0.00093598425,0.0042698365,0.00091644924,0.000491672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006372664,0.0016124578,0.0018468607,0.0016282302,0.0010402759,0.0018975837,0.002923212,0.0025532225,0.0017429539],"category_scores_gemma":[0.026469497,0.0006018448,0.0018882189,0.0014018854,0.002701179,0.006744658,0.006653125,0.0046767322,0.0008742631],"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":[0.00041783872,0.00037419616,0.01027495,0.00027932646,0.00024938252,0.0002522335,0.000467008,0.5985684,0.010745241,0.030170398,0.00615902,0.34204194],"study_design_scores_gemma":[0.00002557752,0.00009287532,0.0009949591,0.000022629025,0.000023140132,0.000107274245,0.00006984897,0.9540252,0.0038271446,0.039693322,0.0010985091,0.000019433797],"about_ca_topic_score_codex":0.0027237597,"about_ca_topic_score_gemma":0.0022483713,"teacher_disagreement_score":0.006372664,"about_ca_system_score_codex":0.0022808078,"about_ca_system_score_gemma":0.001622122,"threshold_uncertainty_score":0.033702254},"labels":[],"label_agreement":null},{"id":"W3169425788","doi":"10.18653/v1/2021.repl4nlp-1.21","title":"Predicting the Success of Domain Adaptation in Text Similarity","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thomson Reuters (Canada)","funders":"","keywords":"Computer science; Exploit; Similarity (geometry); Domain (mathematical analysis); Adaptation (eye); Domain adaptation; Task (project management); Selection (genetic algorithm); Point (geometry); Artificial intelligence; Machine learning; Data mining; Psychology; Mathematics; Engineering","score_opus":0.03325727457945828,"score_gpt":0.2713782545948602,"score_spread":0.2381209800154019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169425788","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95330054,0.0015059276,0.041855615,0.0004889985,0.000057506142,0.000089715504,0.00026591078,0.00030654168,0.0021293352],"genre_scores_gemma":[0.99241227,0.00015572624,0.0063723777,0.00003973415,0.00004087645,0.000022559034,0.0003206981,0.000035899553,0.0005997649],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699533,0.0015368636,0.00019003295,0.0006335121,0.00045433588,0.00018990105],"domain_scores_gemma":[0.93549293,0.052107546,0.0036019061,0.0035468212,0.0030371353,0.0022136946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008453649,0.0007223275,0.0007002652,0.0034117184,0.00061491894,0.001288731,0.0006818063,0.0017248893,0.00071086106],"category_scores_gemma":[0.046250436,0.00024173957,0.00050397264,0.0020044385,0.001211187,0.004028634,0.001552875,0.0018351908,0.0007769972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011728645,0.001042334,0.41436857,0.00039798478,0.0005435734,0.00042273162,0.00083091017,0.27986902,0.012952964,0.0031509125,0.0062353397,0.27901283],"study_design_scores_gemma":[0.00003067705,0.0004149,0.05502552,0.00003452658,0.00007331176,0.00024296966,0.0002450879,0.9285135,0.008454222,0.006196589,0.000720091,0.00004854539],"about_ca_topic_score_codex":0.0023817148,"about_ca_topic_score_gemma":0.0027771867,"teacher_disagreement_score":0.008453649,"about_ca_system_score_codex":0.00069208315,"about_ca_system_score_gemma":0.00039446677,"threshold_uncertainty_score":0.044707716},"labels":[],"label_agreement":null},{"id":"W3169578542","doi":"10.48550/arxiv.2011.00344","title":"A Distribution Dependent Analysis of Meta-Learning","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Empirical risk minimization; Gaussian; Weighting; Context (archaeology); Covariance; Computer science; Transfer of learning; Meta learning (computer science); Statistical learning theory; Multi-task learning; Artificial intelligence; Mathematics; Algorithm; Representation (politics); Task (project management); Machine learning; Statistics; Support vector machine","score_opus":0.13345830448500853,"score_gpt":0.20680331353280576,"score_spread":0.07334500904779723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169578542","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":[0.007284826,0.0004783202,0.98918533,0.0007925592,0.000032495293,0.00003281916,0.00004917769,0.00016799428,0.001976494],"genre_scores_gemma":[0.7374803,0.001619849,0.24903646,0.0011387604,0.00046855927,0.00057470705,0.00042560298,0.0006699153,0.008585811],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937075,0.0031077084,0.00020146449,0.001117617,0.0014825716,0.0003831589],"domain_scores_gemma":[0.95683324,0.03311825,0.0018834443,0.0047535785,0.0026908072,0.0007207452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012855449,0.0018713153,0.0020283253,0.0018401641,0.0010096292,0.0027649584,0.003946816,0.0032039236,0.004005063],"category_scores_gemma":[0.069860555,0.0012753189,0.001963208,0.0013725432,0.003558356,0.0088598365,0.005338233,0.007547784,0.00063882174],"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":[0.00015578227,0.00010801141,0.0018185127,0.00027506,0.00023808365,0.00013735692,0.00023859052,0.52393204,0.0026177182,0.4265643,0.0024612034,0.041453347],"study_design_scores_gemma":[0.000010732188,0.000050056435,0.00028978498,0.000034419787,0.000025444439,0.000043599168,0.000015821306,0.8382397,0.00082609465,0.15957384,0.00087529054,0.000015160987],"about_ca_topic_score_codex":0.0011515981,"about_ca_topic_score_gemma":0.001111968,"teacher_disagreement_score":0.012855449,"about_ca_system_score_codex":0.0029934992,"about_ca_system_score_gemma":0.0020083857,"threshold_uncertainty_score":0.067986965},"labels":[],"label_agreement":null},{"id":"W3170865782","doi":"","title":"Environment Inference for Invariant Learning","year":2021,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Inference; Invariant (physics); Computer science; Benchmark (surveying); Artificial intelligence; Machine learning; Generalization; Domain (mathematical analysis); Mathematics","score_opus":0.04867667267188184,"score_gpt":0.3042943594813409,"score_spread":0.25561768680945907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170865782","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":[0.008740771,0.00017174827,0.988818,0.00030528658,0.000035963407,0.000027867409,0.00010104951,0.00052893267,0.0012703611],"genre_scores_gemma":[0.6921069,0.0003304487,0.30033684,0.0010592031,0.0002325529,0.0002462264,0.00095359015,0.0005196651,0.004214547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944728,0.0024065895,0.00020746127,0.0016799973,0.0007757035,0.00045749883],"domain_scores_gemma":[0.98692524,0.0064035133,0.0010263899,0.004295898,0.0008364457,0.0005124689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008183206,0.0011951113,0.0017748305,0.001330662,0.0014710256,0.0026701235,0.0034817131,0.0017850761,0.0038591528],"category_scores_gemma":[0.029245377,0.00069538056,0.001233681,0.001192651,0.0036860644,0.006377815,0.0053169746,0.004552646,0.0008371335],"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":[0.00028283292,0.00018877082,0.0040886896,0.000116245326,0.000118359574,0.00011820718,0.00027688697,0.6437113,0.002026298,0.24129511,0.0048641264,0.10291314],"study_design_scores_gemma":[0.000013323907,0.000027401373,0.00019006197,0.0000124401595,0.000008586947,0.000020625377,0.000019720683,0.7861734,0.00088942266,0.21167248,0.00096038514,0.000012142737],"about_ca_topic_score_codex":0.0026102865,"about_ca_topic_score_gemma":0.0032663064,"teacher_disagreement_score":0.008183206,"about_ca_system_score_codex":0.0020834913,"about_ca_system_score_gemma":0.0023645577,"threshold_uncertainty_score":0.043277502},"labels":[],"label_agreement":null},{"id":"W3171727251","doi":"10.1088/2632-2153/ac4f3f","title":"Probing transfer learning with a model of synthetic correlated datasets","year":2022,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"European Commission; Royal Society; Wellcome Trust; Wellcome","keywords":"Computer science; Generalization; Transfer of learning; Artificial intelligence; Salient; Machine learning; Task (project management); Synthetic data; Parametric statistics; Feature (linguistics); Artificial neural network; Binary classification; Sample (material); Data mining; Mathematics","score_opus":0.009529413654964517,"score_gpt":0.21710741550436286,"score_spread":0.20757800184939834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171727251","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":[0.35483596,0.00035347385,0.6400897,0.0017594894,0.0000632227,0.000111568464,0.00028220442,0.00037479599,0.0021296048],"genre_scores_gemma":[0.97909147,0.00008451466,0.019404495,0.00016469065,0.000034291643,0.00010206097,0.00023123881,0.000032760658,0.0008544511],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980799,0.0012644422,0.000054489923,0.00032324833,0.00016205407,0.00011582454],"domain_scores_gemma":[0.98398376,0.012523276,0.0010316652,0.0014620848,0.00060307,0.00039621236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072945342,0.00067014067,0.0008755495,0.00060468144,0.0004158595,0.0012150649,0.0018734105,0.002183796,0.0013795032],"category_scores_gemma":[0.036750175,0.00050215883,0.0007402446,0.00059871015,0.0023718409,0.002692262,0.002459248,0.0026527846,0.00022709921],"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":[0.00015355748,0.00013003824,0.0017379945,0.00008160371,0.00005082483,0.000097461045,0.00011193439,0.96109056,0.0013053925,0.026985724,0.00075405993,0.0075007933],"study_design_scores_gemma":[0.000005359333,0.000019453848,0.0001448009,0.000003722522,0.0000022572424,0.000009175189,0.0000070272704,0.9911748,0.00022243209,0.008353416,0.000054121818,0.000003416229],"about_ca_topic_score_codex":0.0021882402,"about_ca_topic_score_gemma":0.0012736038,"teacher_disagreement_score":0.0072945342,"about_ca_system_score_codex":0.0015125877,"about_ca_system_score_gemma":0.0006175057,"threshold_uncertainty_score":0.038577676},"labels":[],"label_agreement":null},{"id":"W3172122468","doi":"","title":"Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection","year":2021,"lang":"en","type":"article","venue":"CaltechAUTHORS (California Institute of Technology)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Nvidia","keywords":"Resampling; Computer science; Artificial intelligence; Object (grammar); Feature (linguistics); Object detection; Image segmentation; Image (mathematics); Pattern recognition (psychology); Segmentation; Computation; Computer vision; Algorithm","score_opus":0.05821363653058965,"score_gpt":0.31151665927688693,"score_spread":0.2533030227462973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172122468","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":[0.02876767,0.0018171882,0.96203274,0.0023352557,0.00025734404,0.000074602816,0.00014616209,0.0026601853,0.0019089455],"genre_scores_gemma":[0.49875426,0.0012200882,0.4903098,0.0018677971,0.0004920649,0.00014589632,0.0007166067,0.00066636596,0.005827147],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915767,0.00018840181,0.000042270975,0.00034149503,0.00017468895,0.00009544088],"domain_scores_gemma":[0.9979982,0.00056542375,0.00013240555,0.0009757319,0.00021378497,0.0001143835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025825778,0.0010058044,0.0011243809,0.0006416093,0.00035113015,0.0014751042,0.001702395,0.0014796004,0.001825128],"category_scores_gemma":[0.007021716,0.00048597623,0.00062788994,0.0006190632,0.0014220198,0.003976687,0.001593891,0.0027275898,0.0013793416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005099328,0.00022878755,0.004872214,0.00018348567,0.00018077386,0.00014258447,0.00030692393,0.045118622,0.04665644,0.029800206,0.011277391,0.8607227],"study_design_scores_gemma":[0.000059563645,0.0003873669,0.0051950305,0.000094123054,0.000106144886,0.0007189394,0.00020561993,0.8149473,0.07810618,0.080945514,0.019109996,0.00012415444],"about_ca_topic_score_codex":0.0020838154,"about_ca_topic_score_gemma":0.003783744,"teacher_disagreement_score":0.0025825778,"about_ca_system_score_codex":0.00070789224,"about_ca_system_score_gemma":0.0006606552,"threshold_uncertainty_score":0.013658166},"labels":[],"label_agreement":null},{"id":"W3172691730","doi":"","title":"Parameterless Transductive Feature Re-representation for Few-Shot Learning","year":2021,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"Carleton University","funders":"","keywords":"Feature (linguistics); Computer science; Artificial intelligence; Shot (pellet); Representation (politics); Pattern recognition (psychology); Feature learning; Machine learning; Chemistry","score_opus":0.09124599111745589,"score_gpt":0.3547058943733342,"score_spread":0.2634599032558783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172691730","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010043449,0.0006446638,0.98593706,0.0002004257,0.000109980814,0.000076479795,0.0002719244,0.0020028078,0.0007131768],"genre_scores_gemma":[0.58527684,0.0008136289,0.3954784,0.0008640138,0.00029691766,0.000564555,0.0046660663,0.00072832854,0.011311162],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859744,0.00038683056,0.00008092017,0.00050695834,0.00027756754,0.00015018796],"domain_scores_gemma":[0.99756145,0.0009536828,0.000118011616,0.0008772309,0.00038791285,0.00010173935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016045979,0.0013890358,0.0025295815,0.0011312574,0.00067238003,0.0012330959,0.0033536255,0.0025789973,0.0046493956],"category_scores_gemma":[0.0066145402,0.00058437604,0.0011786703,0.0013903421,0.0010414881,0.0038339056,0.0027521506,0.0034276813,0.0026688525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050204823,0.00052626745,0.0009151775,0.00028882216,0.00015750893,0.00016548958,0.00016577674,0.15784769,0.017198863,0.014863846,0.016199343,0.7911691],"study_design_scores_gemma":[0.000013534318,0.00009158743,0.00021217443,0.00001773542,0.000018234696,0.000070677976,0.000028126065,0.97514516,0.0037466926,0.019292912,0.001342089,0.00002110267],"about_ca_topic_score_codex":0.0033522265,"about_ca_topic_score_gemma":0.003765006,"teacher_disagreement_score":0.0046493956,"about_ca_system_score_codex":0.00090911007,"about_ca_system_score_gemma":0.0010576834,"threshold_uncertainty_score":0.015553772},"labels":[],"label_agreement":null},{"id":"W3173628900","doi":"10.1609/aaai.v35i15.17632","title":"Progressive Multi-task Learning with Controlled Information Flow for Joint Entity and Relation Extraction","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":55,"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 Ottawa","funders":"Beijing Advanced Innovation Center for Big Data and Brain Computing; Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment; National Natural Science Foundation of China","keywords":"Multi-task learning; Computer science; Benchmark (surveying); Artificial intelligence; Task (project management); Machine learning; Joint (building); Relation (database); Representation (politics); Relationship extraction; Exploit; Feature learning; Information extraction; Data mining","score_opus":0.06065020142216308,"score_gpt":0.2900157999577008,"score_spread":0.22936559853553773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173628900","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":[0.030626453,0.00039980205,0.9661419,0.00029398137,0.000042162264,0.00010328066,0.000105239575,0.0011855752,0.0011015921],"genre_scores_gemma":[0.77046883,0.00031217345,0.22374976,0.0003808668,0.00008846661,0.00039401354,0.0005649702,0.00014239176,0.0038985696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991893,0.00025857618,0.000046260786,0.00026752084,0.00013983129,0.0000985018],"domain_scores_gemma":[0.9979929,0.0010154379,0.00018886897,0.00046353182,0.0002212054,0.00011811175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025063262,0.0012891269,0.0011185516,0.00062013295,0.00051473087,0.00089478696,0.0026726958,0.0014204849,0.0019651065],"category_scores_gemma":[0.005921136,0.00061226304,0.0012039584,0.0010658021,0.0010308233,0.0034206407,0.0025998,0.0031426551,0.00069459964],"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":[0.00048878626,0.00066512666,0.0019442678,0.00023035756,0.00016212347,0.00026466526,0.0003203789,0.65104735,0.012359104,0.019012725,0.004114731,0.30939034],"study_design_scores_gemma":[0.000013106546,0.000042032847,0.000095790514,0.0000034281336,0.0000092362,0.00001374174,0.000005975875,0.9882541,0.001156661,0.010118827,0.00028158908,0.0000055704995],"about_ca_topic_score_codex":0.0039484305,"about_ca_topic_score_gemma":0.004711137,"teacher_disagreement_score":0.0039484305,"about_ca_system_score_codex":0.0010844376,"about_ca_system_score_gemma":0.0014195578,"threshold_uncertainty_score":0.013254881},"labels":[],"label_agreement":null},{"id":"W3175018633","doi":"10.18653/v1/2021.findings-acl.106","title":"Minimax and Neyman-Pearson meta-learning for outlier languages","year":2021,"lang":"en","type":"article","venue":"Edinburgh Research Explorer (University of Edinburgh)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Minimax; Outlier; Computer science; Artificial intelligence; Natural language processing; Machine learning; Mathematics; Mathematical optimization","score_opus":0.15390532974858176,"score_gpt":0.3444586266272386,"score_spread":0.19055329687865682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175018633","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":[0.013774752,0.00046544685,0.98306376,0.00057385984,0.00004127278,0.000046288296,0.00008448456,0.0007759279,0.0011741273],"genre_scores_gemma":[0.6036667,0.00041127892,0.38644078,0.0011929898,0.00016178483,0.0004727922,0.00059305783,0.0006599761,0.006400591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716455,0.0016618345,0.00011735425,0.00057358906,0.00028115386,0.00020157934],"domain_scores_gemma":[0.9911185,0.0066843033,0.0005273575,0.0009685511,0.00047754456,0.00022376535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077509773,0.0015114257,0.0028841964,0.00118893,0.0007906819,0.002221583,0.004265054,0.0031114388,0.00283328],"category_scores_gemma":[0.017507654,0.0011192722,0.0018353121,0.0011591535,0.0025149335,0.003912586,0.0036866143,0.0047368095,0.00086245826],"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":[0.00025314363,0.00014852664,0.0009170769,0.00023221727,0.00022405754,0.00012406375,0.00019372304,0.87062556,0.0017389948,0.056096233,0.0030039125,0.06644252],"study_design_scores_gemma":[0.000014525133,0.000034990968,0.000064010994,0.000015010597,0.000010128666,0.000018184244,0.000010720206,0.97066915,0.0005018921,0.028330777,0.00031996705,0.00001071342],"about_ca_topic_score_codex":0.0019464265,"about_ca_topic_score_gemma":0.0026632776,"teacher_disagreement_score":0.0077509773,"about_ca_system_score_codex":0.0018219409,"about_ca_system_score_gemma":0.0018634462,"threshold_uncertainty_score":0.040991545},"labels":[],"label_agreement":null},{"id":"W3175570208","doi":"10.1609/aaai.v35i12.17323","title":"Multi-task Learning by Leveraging the Semantic Information","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Computer science; Leverage (statistics); Artificial intelligence; Machine learning; Task (project management); Divergence (linguistics); Multi-task learning; Matching (statistics); Semantic space; Kullback–Leibler divergence; Semantic matching; Generalization; Mathematics; Statistics","score_opus":0.0598701591180961,"score_gpt":0.27602168735278787,"score_spread":0.21615152823469178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175570208","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":[0.020607268,0.0005373235,0.9758522,0.00045149022,0.00008332994,0.00012927434,0.00013359371,0.0010506711,0.0011549009],"genre_scores_gemma":[0.59276086,0.00041113392,0.40033054,0.0009115582,0.00031425254,0.00045589215,0.001254723,0.0003714195,0.0031897307],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99637586,0.0011568454,0.00019511957,0.0012846749,0.00067758036,0.0003098763],"domain_scores_gemma":[0.9945497,0.0030824828,0.0004264842,0.0008951022,0.0006897323,0.0003564905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049209837,0.0023530861,0.0024241335,0.0019969512,0.0011544252,0.0020314741,0.0034656825,0.0029121258,0.0018594699],"category_scores_gemma":[0.0130420895,0.0007503347,0.001784467,0.0019589579,0.0015753732,0.005736191,0.004844936,0.004358952,0.0010192639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006880096,0.0010519226,0.0042702584,0.0005050526,0.00036267957,0.00029263156,0.00056355936,0.3670233,0.016805964,0.016683187,0.008306423,0.58344704],"study_design_scores_gemma":[0.00003343735,0.00009256541,0.0004547068,0.00001570319,0.000029870778,0.00006555913,0.000057295223,0.96364135,0.002746224,0.03172916,0.0011089834,0.00002510248],"about_ca_topic_score_codex":0.002905435,"about_ca_topic_score_gemma":0.0032647017,"teacher_disagreement_score":0.0049209837,"about_ca_system_score_codex":0.0012568831,"about_ca_system_score_gemma":0.0023329097,"threshold_uncertainty_score":0.026024997},"labels":[],"label_agreement":null},{"id":"W3175853876","doi":"10.1609/aaai.v35i11.17159","title":"Online Class-Incremental Continual Learning with Adversarial Shapley Value","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":175,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Forgetting; Computer science; Machine learning; Class (philosophy); Artificial intelligence; Adversarial system; Memory footprint; Task (project management); Stability (learning theory); Engineering","score_opus":0.05678288123306853,"score_gpt":0.2831853711185195,"score_spread":0.22640248988545097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175853876","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":[0.066441014,0.0008056259,0.9273199,0.00055946247,0.00010861366,0.00010993065,0.00013786842,0.0020319135,0.002485689],"genre_scores_gemma":[0.8699688,0.00023935948,0.1235368,0.00043758142,0.00011821906,0.00014299896,0.00038099033,0.00017193196,0.00500335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890685,0.0003735574,0.000058098027,0.00031857946,0.00023666667,0.00010629047],"domain_scores_gemma":[0.99617106,0.0020935063,0.00030051902,0.0008075978,0.00042558127,0.00020168527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003223598,0.0012770243,0.0018906567,0.000657025,0.0005722673,0.0010963379,0.003903857,0.001779827,0.0027900059],"category_scores_gemma":[0.006947002,0.0005460815,0.00093613827,0.0006307428,0.0017686421,0.0036940954,0.0026064645,0.003318869,0.0007364998],"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":[0.00037048056,0.0002669166,0.0016735207,0.00012755835,0.0001376663,0.000115532195,0.00015760702,0.7816913,0.003280417,0.016386427,0.0039035482,0.19188902],"study_design_scores_gemma":[0.000008773543,0.000037362784,0.00006060035,0.0000042694533,0.0000048945785,0.000016687456,0.000005856647,0.9922259,0.00059904036,0.0068540433,0.0001763568,0.000006247254],"about_ca_topic_score_codex":0.0023264405,"about_ca_topic_score_gemma":0.002733754,"teacher_disagreement_score":0.003903857,"about_ca_system_score_codex":0.0011425889,"about_ca_system_score_gemma":0.0011082529,"threshold_uncertainty_score":0.01704824},"labels":[],"label_agreement":null},{"id":"W3176707157","doi":"10.1145/3448250","title":"Deep learning for AI","year":2021,"lang":"en","type":"article","venue":"Communications of the ACM","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":627,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Artificial neural network; Natural language processing; Cognitive science; Machine learning; Psychology","score_opus":0.05786285437600762,"score_gpt":0.32420184247153594,"score_spread":0.2663389880955283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176707157","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":[0.008735998,0.06106312,0.8465945,0.027995951,0.002139533,0.00006980251,0.0014272784,0.0026859425,0.049287822],"genre_scores_gemma":[0.50820446,0.05225392,0.36089197,0.0048403684,0.002398146,0.000273647,0.0027000543,0.0007800638,0.06765732],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997477,0.00006823175,0.000013765347,0.000075615964,0.000069024856,0.000025589032],"domain_scores_gemma":[0.9991498,0.00047944958,0.000036434183,0.0001448958,0.00014287513,0.000046414192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006737582,0.00059144985,0.00066268194,0.00051094417,0.00040477695,0.0016015216,0.0008447696,0.0012792959,0.006806713],"category_scores_gemma":[0.003124455,0.00030167578,0.00037137358,0.00083407474,0.0011614525,0.0028268045,0.0012157353,0.0035382276,0.002104314],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007548207,0.0000744229,0.0006848274,0.0006091077,0.00010529547,0.000053041407,0.00010782837,0.053940646,0.002337162,0.48342222,0.056186795,0.40240327],"study_design_scores_gemma":[0.000007969271,0.0000171105,0.00027177742,0.000090388334,0.000012323865,0.00003148297,0.000024573998,0.12653424,0.0008020685,0.8302194,0.041974723,0.000013973081],"about_ca_topic_score_codex":0.00526602,"about_ca_topic_score_gemma":0.0043288847,"teacher_disagreement_score":0.006806713,"about_ca_system_score_codex":0.0009826215,"about_ca_system_score_gemma":0.0007486188,"threshold_uncertainty_score":0.022770703},"labels":[],"label_agreement":null},{"id":"W3177014898","doi":"10.3390/electronics10121491","title":"Virtual to Real-World Transfer Learning: A Systematic Review","year":2021,"lang":"en","type":"review","venue":"Electronics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Transfer of learning; Computer science; Field (mathematics); Bridge (graph theory); Inductive transfer; Domain (mathematical analysis); Virtual learning environment; Transfer of training; Artificial intelligence; Machine learning; Human–computer interaction; Data science; Multimedia; Knowledge management; Robot learning","score_opus":0.03541363485149401,"score_gpt":0.32261516648762945,"score_spread":0.28720153163613543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177014898","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":[0.00022805645,0.9983633,0.000375495,0.0003244159,0.00011198232,0.00010420454,0.000118595875,0.000009927477,0.00036392818],"genre_scores_gemma":[0.0029923306,0.99538404,0.00068210554,0.00042836185,0.00005904483,0.00023256715,0.00011018378,0.000004960831,0.0001063536],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.996872,0.0011497765,0.00085675623,0.0003272881,0.00070931535,0.00008482639],"domain_scores_gemma":[0.9773717,0.018371368,0.0019839853,0.0004487085,0.0015931706,0.00023109607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064598317,0.0010536566,0.0039319377,0.006907697,0.00052473164,0.0023561982,0.0021497,0.001490444,0.007638631],"category_scores_gemma":[0.038868148,0.0005850697,0.003741302,0.006303797,0.000978688,0.0032207817,0.001995774,0.0012710777,0.0007349668],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099485755,0.000048400205,0.00034424878,0.65969014,0.0020966325,0.00008890537,0.00023946383,0.00027007697,0.00014260602,0.001452687,0.0073219626,0.32820538],"study_design_scores_gemma":[0.000113630515,0.00020034965,0.0018431115,0.8740927,0.011018954,0.00047862827,0.00032070026,0.0002002257,0.00018661557,0.0024824112,0.10902085,0.000041798983],"about_ca_topic_score_codex":0.003724135,"about_ca_topic_score_gemma":0.010105172,"teacher_disagreement_score":0.007638631,"about_ca_system_score_codex":0.0022667558,"about_ca_system_score_gemma":0.010366132,"threshold_uncertainty_score":0.034163237},"labels":[],"label_agreement":null},{"id":"W3177352298","doi":"10.1109/cvpr46437.2021.00228","title":"Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Forgetting; Computer science; Flexibility (engineering); Scalability; Task (project management); Artificial intelligence; Lifelong learning; Filter (signal processing); Machine learning; Computer engineering; Computer vision; Engineering; Mathematics","score_opus":0.03163832748074847,"score_gpt":0.26866534353848015,"score_spread":0.2370270160577317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177352298","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":[0.032804552,0.0005829436,0.96043056,0.00023755492,0.00007908713,0.00008576758,0.0001659133,0.0032748925,0.0023387866],"genre_scores_gemma":[0.66629213,0.0003375021,0.32286742,0.0006786023,0.00008044883,0.00024504805,0.0011143292,0.0005625945,0.00782194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997348,0.00006022416,0.000011034327,0.00009234842,0.00006399119,0.000037523172],"domain_scores_gemma":[0.99932563,0.00026974198,0.000047890808,0.00022721745,0.000086399996,0.00004315262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081500085,0.0008273977,0.0006187535,0.00032632906,0.00028116716,0.0005415337,0.0018249265,0.00110246,0.002540751],"category_scores_gemma":[0.0019249271,0.0003999923,0.0005603507,0.00025990058,0.0007740957,0.0015912214,0.0014878531,0.0017474515,0.0007383519],"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":[0.00021909019,0.00020439454,0.0018499122,0.00016661495,0.00013700119,0.00022872664,0.00013377564,0.5760845,0.02729026,0.015891638,0.008061359,0.36973277],"study_design_scores_gemma":[0.000009597843,0.000038552516,0.00013918385,0.000006381706,0.000007083346,0.000050074417,0.00000549667,0.9884319,0.0046433285,0.005631208,0.0010293013,0.0000078597595],"about_ca_topic_score_codex":0.0020334937,"about_ca_topic_score_gemma":0.0046108016,"teacher_disagreement_score":0.002540751,"about_ca_system_score_codex":0.00067485747,"about_ca_system_score_gemma":0.000636583,"threshold_uncertainty_score":0.008499622},"labels":[],"label_agreement":null},{"id":"W3177455005","doi":"10.65109/mpbl3656","title":"TDprop: Does Adaptive Optimization With Jacobi Preconditioning Help Temporal Difference Learning?","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal; McGill University","funders":"","keywords":"Computer science; Temporal difference learning; Mathematical optimization; Mathematics; Artificial intelligence; Reinforcement learning","score_opus":0.011436512491074937,"score_gpt":0.2124903183998896,"score_spread":0.20105380590881466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177455005","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06443274,0.00072841224,0.9260791,0.0013269915,0.00023065221,0.00007451834,0.00007775175,0.0015987931,0.0054511297],"genre_scores_gemma":[0.6648267,0.0003581499,0.32998142,0.0006922299,0.00010755908,0.00014959738,0.00016326223,0.00042713372,0.003294011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945515,0.00022498291,0.00003483575,0.00013569868,0.000086774715,0.00006268976],"domain_scores_gemma":[0.9965329,0.002297137,0.00019011626,0.000503483,0.00032191546,0.00015442669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026080965,0.0008795731,0.00095620874,0.00026665602,0.00033323586,0.0009809827,0.0013301084,0.0015205359,0.0052126953],"category_scores_gemma":[0.0138092805,0.00034170414,0.00044964795,0.00028562575,0.0012675076,0.0022851164,0.0012470726,0.0022311711,0.0010824724],"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":[0.00077455916,0.00036968273,0.004928076,0.0005589488,0.00017835616,0.0002904414,0.0002832508,0.6316842,0.0145685915,0.07326853,0.00964439,0.26345104],"study_design_scores_gemma":[0.00003571557,0.000095734904,0.0001808518,0.00002403856,0.000010478031,0.000039836832,0.00001603384,0.9858143,0.0025160084,0.010297769,0.00096064835,0.000008599427],"about_ca_topic_score_codex":0.0020981012,"about_ca_topic_score_gemma":0.002484476,"teacher_disagreement_score":0.0052126953,"about_ca_system_score_codex":0.00039660826,"about_ca_system_score_gemma":0.0013082785,"threshold_uncertainty_score":0.017438173},"labels":[],"label_agreement":null},{"id":"W3178756862","doi":"10.1109/crv52889.2021.00011","title":"Few-Shot Learning by Integrating Spatial and Frequency Representation","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Aeronautics and Space Administration","keywords":"Computer science; Artificial intelligence; Discrete cosine transform; Representation (politics); Frequency domain; Spatial frequency; Shot (pellet); Domain (mathematical analysis); Pattern recognition (psychology); Machine learning; Transformation (genetics); Feature learning; Computer vision; Image (mathematics); Mathematics","score_opus":0.024222765333839116,"score_gpt":0.27631891261618896,"score_spread":0.25209614728234986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178756862","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":[0.06796591,0.0008236585,0.9277761,0.00022690497,0.00010949609,0.00012598597,0.0001736439,0.00106618,0.0017321272],"genre_scores_gemma":[0.78135574,0.00063987216,0.21223018,0.00040475567,0.00020584327,0.0001394903,0.0011832936,0.00011237794,0.0037284247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940264,0.000111147856,0.000030328434,0.00023199337,0.00016188274,0.000061907005],"domain_scores_gemma":[0.99887234,0.00045266116,0.00010085533,0.00023852897,0.0002446613,0.00009094496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008566076,0.0007782232,0.0012946128,0.0013211425,0.0004345495,0.00087859185,0.0014552868,0.00089918246,0.0011552233],"category_scores_gemma":[0.003244415,0.00034374246,0.00063652935,0.0008318321,0.00075215957,0.0027765487,0.0013165905,0.0012795633,0.00059903663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036931192,0.0006724256,0.0057234503,0.000378655,0.00020965839,0.00024654582,0.0002888833,0.10365801,0.045417547,0.007973389,0.0059066275,0.8291555],"study_design_scores_gemma":[0.000015344525,0.00021232542,0.0019868668,0.000024411658,0.00005482607,0.000235969,0.00009168706,0.96539694,0.013166467,0.016953602,0.0018226404,0.00003901359],"about_ca_topic_score_codex":0.002346328,"about_ca_topic_score_gemma":0.0030907907,"teacher_disagreement_score":0.002346328,"about_ca_system_score_codex":0.00046015895,"about_ca_system_score_gemma":0.0005856792,"threshold_uncertainty_score":0.0046653748},"labels":[],"label_agreement":null},{"id":"W3183432837","doi":"10.1007/s00521-021-06281-3","title":"Data-driven deep density estimation","year":2021,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Engineering Link (Canada)","funders":"Universität Ulm; Bundesministerium für Wirtschaft und Energie","keywords":"Density estimation; Computer science; Probability density function; Synthetic data; Parametric statistics; Curse of dimensionality; Prior probability; Artificial intelligence; Convolutional neural network; Estimation; Sample (material); Population; Data mining; Pattern recognition (psychology); Statistics; Mathematics; Bayesian probability","score_opus":0.04119777855743574,"score_gpt":0.3065328442618775,"score_spread":0.26533506570444176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183432837","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":[0.010513429,0.00016312307,0.98759776,0.00025030473,0.000033113894,0.000023748566,0.00019910446,0.00072177086,0.0004977023],"genre_scores_gemma":[0.65365547,0.0004453826,0.3376902,0.00056807924,0.00014731432,0.00018171262,0.0025603625,0.00031465682,0.0044367714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943703,0.00016561331,0.000031956475,0.0001609198,0.0001375415,0.000066984634],"domain_scores_gemma":[0.99692494,0.0017719066,0.00020112407,0.00043079082,0.0005558274,0.00011541942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016282744,0.00066051295,0.0010363291,0.0008579775,0.00032424228,0.0010076339,0.0020886748,0.0012926758,0.0022447575],"category_scores_gemma":[0.009360058,0.0007108997,0.00081414933,0.00080878695,0.0010993995,0.0020914415,0.001625295,0.002611886,0.0007257589],"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":[0.00008490134,0.00007442979,0.002050334,0.00009972361,0.00006398345,0.000068982125,0.000066533714,0.8776031,0.0023400434,0.019352974,0.0046970267,0.09349797],"study_design_scores_gemma":[0.0000017711843,0.0000028985587,0.00007755203,0.000003933541,0.0000014024081,0.000006914458,0.0000024212038,0.99497545,0.00037845023,0.004371748,0.0001750895,0.0000023261007],"about_ca_topic_score_codex":0.0071996,"about_ca_topic_score_gemma":0.00719574,"teacher_disagreement_score":0.0071996,"about_ca_system_score_codex":0.0012681372,"about_ca_system_score_gemma":0.0011725354,"threshold_uncertainty_score":0.014315367},"labels":[],"label_agreement":null},{"id":"W3184153358","doi":"10.1109/tpami.2022.3201541","title":"Bayesian Embeddings for Few-Shot Open World Recognition","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Stanford University; National Aeronautics and Space Administration","keywords":"Artificial intelligence; Computer science; Open set; Embedding; Machine learning; Benchmark (surveying); Shot (pellet); Bayesian probability; Measure (data warehouse); Class (philosophy); Prior probability; Parametric statistics; Data mining; Mathematics","score_opus":0.05490974341790233,"score_gpt":0.3117677968617627,"score_spread":0.25685805344386037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184153358","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":[0.013891009,0.00050189067,0.9825433,0.00019758342,0.000052026044,0.00006947156,0.0002581795,0.0015272081,0.00095937116],"genre_scores_gemma":[0.55653834,0.0008228982,0.43173704,0.000556727,0.00021990344,0.00029886907,0.003511536,0.00049913296,0.0058155805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843603,0.00041047556,0.00007923672,0.00055973243,0.0003766793,0.00013773172],"domain_scores_gemma":[0.9966397,0.0014063807,0.00035775735,0.0009466563,0.00044673335,0.00020272122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019168961,0.0012921712,0.0017370459,0.0015507318,0.0005597555,0.0018274827,0.0028730293,0.0020565817,0.0032179814],"category_scores_gemma":[0.009761062,0.00069135043,0.00090179686,0.0011329046,0.0013723569,0.0051743295,0.002905725,0.0034261832,0.0019091788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041630678,0.00052378385,0.0026219906,0.00028975747,0.00013527933,0.00015808654,0.00030941746,0.3031013,0.0091845915,0.035711054,0.009831238,0.6377171],"study_design_scores_gemma":[0.0000074136865,0.00004588722,0.0003477563,0.00001778624,0.000008268972,0.0000657925,0.00003347548,0.961939,0.0024778615,0.03378009,0.0012574316,0.000019279109],"about_ca_topic_score_codex":0.0034332662,"about_ca_topic_score_gemma":0.004142809,"teacher_disagreement_score":0.0034332662,"about_ca_system_score_codex":0.0012182884,"about_ca_system_score_gemma":0.00087100954,"threshold_uncertainty_score":0.0107652545},"labels":[],"label_agreement":null},{"id":"W3184970977","doi":"10.1038/s41598-022-06718-2","title":"Automated human cell classification in sparse datasets using few-shot learning","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"Mitacs","keywords":"Computer science; Artificial intelligence; Machine learning; Deep learning; Domain (mathematical analysis); Domain knowledge; Process (computing); Data mining; Training set; Pattern recognition (psychology)","score_opus":0.07524776780881993,"score_gpt":0.3144405840140182,"score_spread":0.23919281620519828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184970977","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":[0.32477865,0.003319174,0.6570963,0.00064268475,0.0004006192,0.0003318457,0.0022586952,0.0054375953,0.005734425],"genre_scores_gemma":[0.70949626,0.0013693038,0.27202684,0.0006574846,0.00017785416,0.00020711185,0.009844889,0.00015946956,0.006060719],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995333,0.000079443416,0.000022552413,0.0001658001,0.00011949322,0.00007941441],"domain_scores_gemma":[0.99910766,0.00036523028,0.00007726057,0.00016420393,0.00020470795,0.000080871694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008618794,0.0006573554,0.00084138307,0.0016702797,0.0004085415,0.00090353325,0.0012335901,0.001124284,0.0011137744],"category_scores_gemma":[0.0024153867,0.00022076312,0.00056315283,0.0008674283,0.00048958405,0.0010004796,0.001061405,0.0010340421,0.0010242532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067548366,0.0005781317,0.020260772,0.00047922903,0.00017703175,0.0007780796,0.0004971149,0.07742631,0.08510651,0.0028995974,0.019341266,0.7917804],"study_design_scores_gemma":[0.000027146645,0.0003139325,0.010791087,0.000078999394,0.000051843566,0.0009931767,0.0005137861,0.92171544,0.04988864,0.0081696445,0.0074101994,0.000046020956],"about_ca_topic_score_codex":0.004305369,"about_ca_topic_score_gemma":0.008496949,"teacher_disagreement_score":0.004305369,"about_ca_system_score_codex":0.0005576531,"about_ca_system_score_gemma":0.00063634553,"threshold_uncertainty_score":0.008560598},"labels":[],"label_agreement":null},{"id":"W3186163092","doi":"10.48550/arxiv.2107.13741","title":"Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Pattern recognition (psychology); Task (project management); Semi-supervised learning; Supervised learning; Encoder; Machine learning; Process (computing); Training set; Labeled data; Medical imaging; Image (mathematics); Noise (video); Set (abstract data type); Exploit; Artificial neural network","score_opus":0.06673264261701548,"score_gpt":0.21056153301162872,"score_spread":0.14382889039461325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186163092","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":[0.021617906,0.00030608024,0.97547793,0.00016410943,0.000034349243,0.000064037726,0.0000828355,0.0017033706,0.0005494139],"genre_scores_gemma":[0.53209805,0.00029030305,0.46195772,0.00054905796,0.0001293244,0.00039854975,0.0009326946,0.00056542794,0.0030787936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866784,0.00053032994,0.00005411507,0.00047035786,0.0001901683,0.00008715254],"domain_scores_gemma":[0.99549854,0.0025854232,0.0003913557,0.0008952012,0.00048422394,0.00014526192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030956385,0.0012242817,0.0012684284,0.0008041817,0.00043144455,0.0010354498,0.0029148227,0.0018491396,0.0011430721],"category_scores_gemma":[0.007960549,0.00080988865,0.0010809164,0.0006347985,0.0017043053,0.0019848873,0.0021189111,0.0029030282,0.0009103357],"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":[0.00061679754,0.00048274148,0.0024925626,0.00033810546,0.00019949222,0.00017793354,0.0003717334,0.5815949,0.03997281,0.008796857,0.0041053733,0.36085072],"study_design_scores_gemma":[0.0000114676795,0.00006650901,0.00018812985,0.000010297715,0.000007974466,0.00003333508,0.000009532907,0.98883677,0.005865774,0.0045497436,0.00040920678,0.000011215907],"about_ca_topic_score_codex":0.0018105642,"about_ca_topic_score_gemma":0.0027693217,"teacher_disagreement_score":0.0030956385,"about_ca_system_score_codex":0.0009812901,"about_ca_system_score_gemma":0.000899066,"threshold_uncertainty_score":0.016371489},"labels":[],"label_agreement":null},{"id":"W3194952006","doi":"10.48550/arxiv.2108.06325","title":"Continual Backprop: Stochastic Gradient Descent with Persistent Randomness","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Randomness; Initialization; Computer science; Gradient descent; Stochastic gradient descent; Process (computing); Artificial intelligence; Algorithm; Artificial neural network; Mathematics; Statistics","score_opus":0.06062449877952751,"score_gpt":0.17011607112870078,"score_spread":0.10949157234917327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194952006","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":[0.0149903195,0.00026788545,0.9791478,0.00040349414,0.00007547917,0.0000795674,0.00006941177,0.002339223,0.0026267525],"genre_scores_gemma":[0.54960674,0.00029427232,0.4404745,0.00068191066,0.00014003024,0.00044163206,0.00041457397,0.0007607981,0.007185566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915683,0.000304329,0.000043818964,0.00017861272,0.00023805982,0.00007828019],"domain_scores_gemma":[0.99711597,0.0015280369,0.00022514259,0.00055369997,0.00042903188,0.00014823326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026152285,0.0009935962,0.0012377199,0.0006421504,0.0006566284,0.0012882112,0.0029235152,0.0020795828,0.003120867],"category_scores_gemma":[0.0075019696,0.0008354162,0.0007032476,0.0006253294,0.0018443555,0.0017680102,0.002172177,0.0026191855,0.0010666812],"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":[0.00024917917,0.0001758174,0.001259021,0.0001566971,0.00013070997,0.00017076338,0.000105217,0.8087475,0.0031674446,0.036969338,0.0075272056,0.14134106],"study_design_scores_gemma":[0.000013399492,0.000021034435,0.00004139148,0.000007774699,0.0000042105066,0.000018700812,0.000002802289,0.9922145,0.0005660499,0.006570275,0.00053418096,0.0000057506145],"about_ca_topic_score_codex":0.003288097,"about_ca_topic_score_gemma":0.0036044281,"teacher_disagreement_score":0.003288097,"about_ca_system_score_codex":0.00090877275,"about_ca_system_score_gemma":0.0016674546,"threshold_uncertainty_score":0.013830841},"labels":[],"label_agreement":null},{"id":"W3196853733","doi":"10.1145/3441852.3471225","title":"Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"CNIB; Microsoft Research","keywords":"Computer science; Object (grammar); Artificial intelligence; Set (abstract data type); Cognitive neuroscience of visual object recognition; Data set; Computer vision; Human–computer interaction","score_opus":0.06840675299731007,"score_gpt":0.33110839447979934,"score_spread":0.26270164148248926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196853733","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":[0.1627524,0.0019101731,0.68475515,0.02115462,0.0021000255,0.006405773,0.077292636,0.024748053,0.018881142],"genre_scores_gemma":[0.11890177,0.00040970492,0.80666006,0.0013277351,0.00019613327,0.0021973334,0.066697255,0.0015432801,0.0020667918],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.98798674,0.006242008,0.0012083963,0.0019857758,0.0022024156,0.0003747041],"domain_scores_gemma":[0.93482006,0.028407663,0.0017919688,0.021215558,0.01151806,0.002246774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022794154,0.00086236745,0.0007223227,0.0026077342,0.0022776632,0.0032430692,0.0050043995,0.002229311,0.002400864],"category_scores_gemma":[0.08741522,0.00058590196,0.0013573827,0.0027500794,0.0019931858,0.0056217527,0.006078349,0.003584585,0.0028333042],"study_design_candidate":"qualitative","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":[0.00072983507,0.0030989647,0.052097563,0.0027707492,0.00029496456,0.0013405777,0.012532051,0.018007433,0.01914458,0.020272229,0.31169766,0.55801326],"study_design_scores_gemma":[0.0004255778,0.0010199404,0.04491197,0.0017936471,0.00018768983,0.002131668,0.018863266,0.12642303,0.047374345,0.08351732,0.6729178,0.00043368488],"about_ca_topic_score_codex":0.007537515,"about_ca_topic_score_gemma":0.017177723,"teacher_disagreement_score":0.022794154,"about_ca_system_score_codex":0.001840094,"about_ca_system_score_gemma":0.0026174258,"threshold_uncertainty_score":0.12054849},"labels":[],"label_agreement":null},{"id":"W3196892826","doi":"10.1109/tcyb.2021.3107292","title":"Mutual Variational Inference: An Indirect Variational Inference Approach for Unsupervised Domain Adaptation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Mitacs","keywords":"Inference; Computer science; Artificial intelligence; Pattern recognition (psychology); Estimator; Regularization (linguistics); Latent variable; Discriminative model; Domain adaptation; Feature learning; Machine learning; Mathematics; Statistics","score_opus":0.04950507357845684,"score_gpt":0.2821592638519151,"score_spread":0.23265419027345824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196892826","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014866852,0.0001724933,0.997523,0.00012045269,0.000017775163,0.000017601933,0.000026869659,0.00012271202,0.0005124344],"genre_scores_gemma":[0.3178198,0.00088683766,0.67169976,0.0007108618,0.00031275133,0.000505745,0.0006662426,0.0004888525,0.0069091823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989831,0.0005090755,0.000038539645,0.00020592827,0.00019761402,0.0000657715],"domain_scores_gemma":[0.9978231,0.0015595832,0.00013442717,0.00022575953,0.00018705826,0.000070015194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033335513,0.0010323097,0.0015823866,0.0009111762,0.0005064667,0.0010539899,0.0033197005,0.0017130597,0.0019086638],"category_scores_gemma":[0.0063152383,0.0008859788,0.001499288,0.0011103727,0.0014930185,0.0017063097,0.0024336248,0.0031676108,0.00046692393],"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":[0.00006697565,0.00007627422,0.0008181918,0.00014165217,0.00021939664,0.000106603846,0.00014814909,0.8282088,0.0024229556,0.08572626,0.0031125443,0.078952156],"study_design_scores_gemma":[0.0000039422957,0.000007637756,0.00004265332,0.0000049009395,0.0000059369845,0.000011242996,0.0000037176978,0.984171,0.00022652696,0.01503031,0.00048705164,0.000005022414],"about_ca_topic_score_codex":0.005111304,"about_ca_topic_score_gemma":0.00581456,"teacher_disagreement_score":0.005111304,"about_ca_system_score_codex":0.0012458895,"about_ca_system_score_gemma":0.0016900911,"threshold_uncertainty_score":0.017629743},"labels":[],"label_agreement":null},{"id":"W3199813582","doi":"10.2139/ssrn.4188684","title":"Hcdg: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Generalization; Consistency (knowledge bases); Artificial intelligence; Segmentation; Computer science; Image segmentation; Image (mathematics); Domain (mathematical analysis); Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.011898693618055338,"score_gpt":0.28119378151570584,"score_spread":0.2692950878976505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199813582","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018686898,0.00017308145,0.9959805,0.0000795838,0.00002120609,0.000050373612,0.00012969607,0.0014967142,0.00020018683],"genre_scores_gemma":[0.11298979,0.00037104214,0.8808723,0.00045872986,0.00014978909,0.0002660181,0.0018231958,0.0010606167,0.0020085156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816495,0.00059504743,0.000095501855,0.00059767213,0.00039939518,0.00014733808],"domain_scores_gemma":[0.99703836,0.0011929371,0.00017432954,0.0009200063,0.0005081857,0.00016611797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044885976,0.0012142828,0.0025326274,0.0025708913,0.0008531843,0.0017203989,0.004681798,0.0031176496,0.0032461453],"category_scores_gemma":[0.007582715,0.0010572623,0.001981966,0.0022545445,0.0014836143,0.0025286002,0.0046862043,0.0039233603,0.0012932633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003710066,0.00026379092,0.0015384082,0.00038048558,0.00033331543,0.00018101715,0.00023175754,0.30106896,0.013727834,0.030839587,0.017331332,0.6337326],"study_design_scores_gemma":[0.000017453991,0.00004101643,0.00026099678,0.000016488506,0.000019750312,0.000052016036,0.000017688757,0.9750182,0.0024835693,0.020256646,0.0018025239,0.000013777883],"about_ca_topic_score_codex":0.009726207,"about_ca_topic_score_gemma":0.011148476,"teacher_disagreement_score":0.009726207,"about_ca_system_score_codex":0.001224382,"about_ca_system_score_gemma":0.0021919091,"threshold_uncertainty_score":0.023738265},"labels":[],"label_agreement":null},{"id":"W3200580571","doi":"10.48550/arxiv.2109.05675","title":"Online Unsupervised Learning of Visual Representations and Categories","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Categorical variable; Computer science; Artificial intelligence; Unsupervised learning; Representation (politics); Class (philosophy); Contrast (vision); Component (thermodynamics); Machine learning; Visual learning; Concept learning; Feature learning; Pattern recognition (psychology); Mathematics","score_opus":0.07116931112499322,"score_gpt":0.22557653757903934,"score_spread":0.15440722645404611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200580571","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":[0.089741275,0.00031397532,0.90551895,0.0002811864,0.00005868562,0.00007271497,0.00023505744,0.0019289973,0.0018491097],"genre_scores_gemma":[0.8243309,0.00020398997,0.16848525,0.0002561542,0.00008962296,0.00013409794,0.0011108065,0.00019654339,0.0051926663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938655,0.0001320105,0.000017682627,0.00028422664,0.00011076302,0.000068745336],"domain_scores_gemma":[0.99836606,0.0006271101,0.0001725459,0.00047699644,0.00025777126,0.00009944205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074964465,0.00053806894,0.0007213885,0.00079810875,0.0003552613,0.00078813743,0.0024992656,0.0011332629,0.0016560467],"category_scores_gemma":[0.004382757,0.0004275866,0.0007292778,0.00062487426,0.00093527045,0.0027820598,0.0014835374,0.0017538446,0.0007608366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030510087,0.000534244,0.0055019995,0.0002254157,0.00016692594,0.00013268666,0.0003017008,0.25268325,0.028616218,0.0165963,0.008215756,0.6867204],"study_design_scores_gemma":[0.0000074893433,0.000034647917,0.0008351215,0.000007049752,0.000007914927,0.000051165975,0.000030304112,0.9783111,0.0049132784,0.015169524,0.000623416,0.000008935917],"about_ca_topic_score_codex":0.0024932886,"about_ca_topic_score_gemma":0.0041432087,"teacher_disagreement_score":0.0024992656,"about_ca_system_score_codex":0.000761733,"about_ca_system_score_gemma":0.0006412297,"threshold_uncertainty_score":0.005540073},"labels":[],"label_agreement":null},{"id":"W3200693755","doi":"10.1007/978-3-030-86486-6_38","title":"Continual Learning with Dual Regularizations","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Carleton University","funders":"","keywords":"Computer science; Forgetting; Interleaving; Dual (grammatical number); Artificial intelligence; Set (abstract data type); Representation (politics); Machine learning; Process (computing); Key (lock)","score_opus":0.012988653077602878,"score_gpt":0.22655033146337047,"score_spread":0.2135616783857676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200693755","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009052796,0.000941626,0.9839716,0.00040525256,0.00011087508,0.000023247107,0.00006272806,0.0003671263,0.005064604],"genre_scores_gemma":[0.4765752,0.0014622704,0.48100853,0.00053785386,0.0005594328,0.00027978298,0.00071478134,0.00061489036,0.0382473],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994394,0.0002138827,0.00002625282,0.00015928084,0.0001167251,0.000044479286],"domain_scores_gemma":[0.998519,0.00088709587,0.000060874358,0.00024566663,0.00018851011,0.000098885655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019311514,0.0007551073,0.0010727793,0.00064384757,0.0003888104,0.001044082,0.0018429427,0.0016701961,0.0061575626],"category_scores_gemma":[0.0047762017,0.00057791546,0.00067766977,0.0006841182,0.0013370699,0.0025104135,0.0032071539,0.002917332,0.001400735],"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":[0.00033916545,0.00025528372,0.0005444852,0.0003819791,0.00013757072,0.00009506634,0.00015520345,0.2989223,0.008110464,0.24333799,0.014833696,0.43288678],"study_design_scores_gemma":[0.000011496185,0.00003665063,0.00007185219,0.000018534918,0.000009432725,0.00003113956,0.000009174356,0.91952443,0.00087443774,0.077547476,0.0018559715,0.000009395842],"about_ca_topic_score_codex":0.0010526357,"about_ca_topic_score_gemma":0.0012857419,"teacher_disagreement_score":0.0061575626,"about_ca_system_score_codex":0.00060341024,"about_ca_system_score_gemma":0.000551086,"threshold_uncertainty_score":0.020599127},"labels":[],"label_agreement":null},{"id":"W3204322147","doi":"10.1007/978-3-030-87196-3_31","title":"Few-Shot Domain Adaptation with Polymorphic Transformers","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Domain adaptation; Transformer; Domain (mathematical analysis); Shot (pellet); Artificial intelligence; Electrical engineering; Engineering; Mathematics; Voltage","score_opus":0.02422040998477934,"score_gpt":0.23479222907096492,"score_spread":0.21057181908618558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204322147","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065975217,0.00019423952,0.98938775,0.000073976444,0.00003470567,0.000030435707,0.000054517892,0.0016895719,0.0019373744],"genre_scores_gemma":[0.46289325,0.00046988885,0.5237645,0.00029468298,0.00007334046,0.00015326789,0.000811026,0.0008919101,0.010648216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991327,0.00030349073,0.00004564565,0.0002742273,0.0001764626,0.00006747625],"domain_scores_gemma":[0.9982439,0.00087396806,0.00004227338,0.00056548533,0.00020444088,0.00006992138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014180156,0.00067801017,0.0007812306,0.0006497239,0.00040265484,0.0010711647,0.0018302221,0.0009892484,0.005202984],"category_scores_gemma":[0.0052877306,0.00046030764,0.0008381012,0.0007218534,0.00097277743,0.0033397088,0.0035739972,0.0019603756,0.0017652762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040565868,0.0002092244,0.0009926102,0.00017599268,0.00010208636,0.00018346145,0.00026205755,0.07776844,0.019424224,0.07784128,0.008461331,0.8141737],"study_design_scores_gemma":[0.00002781605,0.00006275681,0.00023195845,0.000024810517,0.000029755218,0.00017720956,0.000064433974,0.8662242,0.009919855,0.11893927,0.0042796186,0.000018344146],"about_ca_topic_score_codex":0.0011868051,"about_ca_topic_score_gemma":0.0017294498,"teacher_disagreement_score":0.005202984,"about_ca_system_score_codex":0.00053014327,"about_ca_system_score_gemma":0.000625097,"threshold_uncertainty_score":0.017405689},"labels":[],"label_agreement":null},{"id":"W3204478713","doi":"10.48550/arxiv.2011.11872","title":"Mixture-based Feature Space Learning for Few-shot Image Classification","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Compute Canada","keywords":"Pattern recognition (psychology); Artificial intelligence; Feature learning; Feature (linguistics); Computer science; Discriminative model; Feature vector; Mixture model; Cluster analysis; Context (archaeology); Feature extraction; Representation (politics); Extractor; Contextual image classification; Machine learning; Image (mathematics); Engineering","score_opus":0.09941407703492308,"score_gpt":0.20297785049731912,"score_spread":0.10356377346239604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204478713","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008796164,0.0004204264,0.987844,0.00008702739,0.00003596123,0.00004505437,0.00018365387,0.0021476555,0.0004400962],"genre_scores_gemma":[0.4689552,0.00054822915,0.5234262,0.0002914394,0.00017446488,0.0003287592,0.0027841877,0.00032609023,0.0031654586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998879,0.00023684544,0.000052115287,0.00040180425,0.0002699263,0.00016025949],"domain_scores_gemma":[0.99891376,0.0003792575,0.000114474016,0.0002612195,0.00024753212,0.000083732455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001399495,0.0015222657,0.0018957586,0.0020213518,0.0005708884,0.0011719239,0.0031814596,0.0014441912,0.0031144058],"category_scores_gemma":[0.004132794,0.0004902619,0.0014967268,0.0018154822,0.0008278594,0.003042943,0.0021630402,0.0027955703,0.0022960904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036003935,0.0003598667,0.0032327354,0.00019678322,0.00020122534,0.0001456416,0.0001981179,0.17464606,0.017987547,0.011901861,0.008469686,0.7823005],"study_design_scores_gemma":[0.0000081454955,0.000057356232,0.00039221987,0.000011105554,0.0000147349365,0.00006683023,0.000018580717,0.9828892,0.004531276,0.010878406,0.0011150893,0.00001712286],"about_ca_topic_score_codex":0.00400972,"about_ca_topic_score_gemma":0.003786956,"teacher_disagreement_score":0.00400972,"about_ca_system_score_codex":0.000988854,"about_ca_system_score_gemma":0.00070080196,"threshold_uncertainty_score":0.010418713},"labels":[],"label_agreement":null},{"id":"W3205784920","doi":"10.1609/aaai.v36i8.20874","title":"Hindsight Network Credit Assignment: Efficient Credit Assignment in Networks of Discrete Stochastic Units","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Backpropagation; Hindsight bias; Computer science; Estimator; Artificial neural network; Function (biology); Artificial intelligence; Variance (accounting); Mathematical optimization; Mathematics; Economics; Statistics","score_opus":0.05522808581807579,"score_gpt":0.26579053390657126,"score_spread":0.21056244808849547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205784920","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0401289,0.00023062556,0.9566074,0.0004872998,0.00005142405,0.00006264723,0.000051313775,0.00057523395,0.0018051497],"genre_scores_gemma":[0.8321287,0.00016658711,0.16272567,0.00027882238,0.0000717768,0.00014049382,0.000117424606,0.00012572762,0.004244805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993881,0.00023453688,0.000028571296,0.00016172206,0.00011826492,0.00006869511],"domain_scores_gemma":[0.997335,0.0015309533,0.00026052474,0.00039628736,0.00032113958,0.00015604043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00160755,0.0007257184,0.0009809892,0.0004872098,0.000568132,0.00090773473,0.0018348973,0.0014402672,0.002951315],"category_scores_gemma":[0.011276164,0.00042118967,0.00035917346,0.0005810005,0.0013579077,0.0022746183,0.0016657336,0.00221707,0.0003814777],"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":[0.0001502948,0.000102349244,0.0019400418,0.00008976909,0.00004715275,0.00007141021,0.00015708037,0.7990278,0.0028948733,0.046138298,0.0023935686,0.14698733],"study_design_scores_gemma":[0.0000074875975,0.0000145413005,0.00009382362,0.000005043309,0.000003416339,0.000010098627,0.000005017878,0.98277307,0.0007519485,0.01603604,0.0002955383,0.0000039915117],"about_ca_topic_score_codex":0.0047247396,"about_ca_topic_score_gemma":0.0051205335,"teacher_disagreement_score":0.0047247396,"about_ca_system_score_codex":0.0012327672,"about_ca_system_score_gemma":0.0013168945,"threshold_uncertainty_score":0.009873152},"labels":[],"label_agreement":null},{"id":"W3206786886","doi":"10.18653/v1/2022.findings-acl.316","title":"Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations","year":2022,"lang":"en","type":"article","venue":"Findings of the Association for Computational Linguistics: ACL 2022","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":19,"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 Waterloo","funders":"","keywords":"Computer science; Invariant (physics); Classifier (UML); Source code; Embedding; Adversarial system; Artificial intelligence; Encoder; Autoencoder; Theoretical computer science; Pattern recognition (psychology); Algorithm; Deep learning; Mathematics","score_opus":0.01593641139419208,"score_gpt":0.2523305473785822,"score_spread":0.2363941359843901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206786886","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":[0.057948958,0.00092972006,0.93409723,0.00046163588,0.00008856666,0.00014724434,0.0003433411,0.003039991,0.0029432864],"genre_scores_gemma":[0.83946186,0.0003885376,0.14939755,0.0006139449,0.00013133713,0.00017615138,0.0016657007,0.00021887654,0.007946129],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991598,0.00027889566,0.000035783243,0.00025140942,0.00017796563,0.00009615626],"domain_scores_gemma":[0.9985297,0.00074993167,0.00010437807,0.00042788277,0.00012322505,0.00006493912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017181082,0.0010942911,0.0013653791,0.0006287016,0.000369049,0.00090734044,0.0022490679,0.0014400224,0.0019105269],"category_scores_gemma":[0.0047670393,0.0004048763,0.0007021881,0.00067443214,0.0010969882,0.0029658363,0.0018357806,0.0018612213,0.00096905883],"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":[0.00037038221,0.00029921095,0.0011855,0.00019357886,0.00012251531,0.00017497834,0.000118317934,0.7372962,0.0084207915,0.02093991,0.009501789,0.2213767],"study_design_scores_gemma":[0.000012134371,0.00005767493,0.00010168358,0.0000051149395,0.0000071260033,0.000040902796,0.000008968065,0.9915775,0.0013951027,0.006324028,0.00046159507,0.000008216227],"about_ca_topic_score_codex":0.003816705,"about_ca_topic_score_gemma":0.004213269,"teacher_disagreement_score":0.003816705,"about_ca_system_score_codex":0.0009377407,"about_ca_system_score_gemma":0.00079372455,"threshold_uncertainty_score":0.009086311},"labels":[],"label_agreement":null},{"id":"W3207144050","doi":"","title":"Few-Shot Attribute Learning.","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Generalization; Computer science; Artificial intelligence; Predictability; Machine learning; Supervised learning; Simple (philosophy); Space (punctuation); Mathematics; Statistics; Artificial neural network","score_opus":0.1626231936756466,"score_gpt":0.20496145163696036,"score_spread":0.04233825796131377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207144050","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032056447,0.0010957344,0.961461,0.00034827483,0.00013901544,0.00016705334,0.00052358763,0.0016178932,0.0025910593],"genre_scores_gemma":[0.705144,0.0008570656,0.28089094,0.0007104314,0.00024652213,0.00028795793,0.0043062014,0.00027328986,0.007283571],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987192,0.00040084083,0.000054372475,0.0004899883,0.00025453907,0.0000810506],"domain_scores_gemma":[0.9963689,0.0019991007,0.00018732366,0.0009310034,0.00033881687,0.00017478212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002252011,0.00085191824,0.0012659283,0.0012413545,0.0005587435,0.0012057292,0.0027896424,0.0016432827,0.002731567],"category_scores_gemma":[0.0092525175,0.0004638426,0.0010635336,0.0011007865,0.0012560113,0.0035398938,0.0019462429,0.0025933045,0.0011994033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000514344,0.00079987384,0.0072765024,0.0008120099,0.00046093718,0.0002942563,0.00057209015,0.29571426,0.014268955,0.048688456,0.020327348,0.6102709],"study_design_scores_gemma":[0.0000140924285,0.000076196906,0.0009057823,0.00002322288,0.000024932766,0.00014235836,0.00005569434,0.94092965,0.0042945296,0.050690018,0.0028209637,0.000022565988],"about_ca_topic_score_codex":0.0023916615,"about_ca_topic_score_gemma":0.0030625316,"teacher_disagreement_score":0.0027896424,"about_ca_system_score_codex":0.0009201212,"about_ca_system_score_gemma":0.00072812394,"threshold_uncertainty_score":0.011909902},"labels":[],"label_agreement":null},{"id":"W3207180064","doi":"10.48550/arxiv.2101.10423","title":"Online Continual Learning in Image Classification: An Empirical Survey","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Forgetting; Computer science; Machine learning; Artificial intelligence; Classifier (UML); Class (philosophy); Variety (cybernetics)","score_opus":0.19615599897747463,"score_gpt":0.2663611067145922,"score_spread":0.07020510773711755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207180064","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":[0.076492175,0.5030525,0.38910034,0.0053003663,0.0013317262,0.00037132733,0.001027422,0.0040230528,0.019301172],"genre_scores_gemma":[0.5605532,0.17152105,0.2467462,0.0027960686,0.0025968528,0.00042049302,0.004491189,0.0008381848,0.010036677],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99711573,0.0007267277,0.00028544106,0.0009384886,0.00076047593,0.0001731621],"domain_scores_gemma":[0.9870613,0.009555017,0.0004870986,0.0012741346,0.0013017774,0.0003206403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005350684,0.0019231885,0.0025445544,0.0030421533,0.0007363089,0.0027840647,0.0034997035,0.0021867652,0.003295624],"category_scores_gemma":[0.018178513,0.00080361136,0.0012431619,0.003926835,0.001320538,0.006746836,0.0018045255,0.0035459255,0.002688681],"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":[0.0003234151,0.00057258067,0.007392174,0.001595202,0.0001634329,0.000060210936,0.00011345871,0.023395143,0.0007538604,0.006116717,0.015440745,0.944073],"study_design_scores_gemma":[0.00012658509,0.0013400542,0.014462726,0.0019178811,0.0004515143,0.0012890561,0.0008295643,0.81981957,0.007776639,0.047370255,0.10442792,0.00018815402],"about_ca_topic_score_codex":0.0037939977,"about_ca_topic_score_gemma":0.0026932443,"teacher_disagreement_score":0.005350684,"about_ca_system_score_codex":0.0013271897,"about_ca_system_score_gemma":0.0015586421,"threshold_uncertainty_score":0.028297424},"labels":[],"label_agreement":null},{"id":"W3207216914","doi":"10.48550/arxiv.2110.07376","title":"Domain Adaptation on Semantic Segmentation with Separate Affine Transformation in Batch Normalization","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Discriminator; Normalization (sociology); Affine transformation; Computer science; Artificial intelligence; Segmentation; Transformation (genetics); Pattern recognition (psychology); Domain (mathematical analysis); Adaptation (eye); Mathematics","score_opus":0.052007729851523556,"score_gpt":0.19063807581667977,"score_spread":0.13863034596515622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207216914","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014287612,0.00028981583,0.98180765,0.000117656666,0.00009003473,0.00004285748,0.00008290418,0.0017032269,0.0015782522],"genre_scores_gemma":[0.52017546,0.00057164254,0.46465555,0.0006114857,0.00020207433,0.00019734736,0.0011791477,0.00089435163,0.0115129175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924904,0.00018663194,0.000030987718,0.00032761105,0.00012597487,0.000079742465],"domain_scores_gemma":[0.99940443,0.00019142419,0.000050700448,0.00020813943,0.00009908516,0.000046095625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010263147,0.0011334837,0.0011688587,0.00077787694,0.0005510655,0.0008379259,0.0014706041,0.0010941703,0.002826954],"category_scores_gemma":[0.0021409497,0.00043715167,0.0012642208,0.00097622944,0.0012930465,0.0018898533,0.0017718729,0.0021654333,0.0015095011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041594217,0.00024365734,0.0016550964,0.00017145282,0.00019385693,0.0002361171,0.00029117864,0.3410596,0.055123452,0.03097461,0.008838615,0.5607964],"study_design_scores_gemma":[0.000012169074,0.00004148566,0.00043036047,0.000007412844,0.000020455087,0.000092283335,0.000023614517,0.97264963,0.010601032,0.013642027,0.0024604804,0.00001897914],"about_ca_topic_score_codex":0.0037146963,"about_ca_topic_score_gemma":0.0037565785,"teacher_disagreement_score":0.0037146963,"about_ca_system_score_codex":0.00078133965,"about_ca_system_score_gemma":0.00091502373,"threshold_uncertainty_score":0.009457111},"labels":[],"label_agreement":null},{"id":"W3207497018","doi":"10.1109/icra48506.2021.9561466","title":"LiDAR few-shot domain adaptation via integrated CycleGAN and 3D object detector with joint learning delay","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Lidar; Artificial intelligence; Point cloud; Task (project management); Object detection; Minimum bounding box; Margin (machine learning); Domain (mathematical analysis); Detector; Joint (building); Bounding overwatch; Object (grammar); Network architecture; Adaptation (eye); Machine learning; Pattern recognition (psychology); Image (mathematics)","score_opus":0.01937140428939694,"score_gpt":0.2220868300012766,"score_spread":0.20271542571187967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207497018","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":[0.06710343,0.0004618833,0.92442065,0.00028196472,0.000121589175,0.00013559208,0.00013951253,0.0044575324,0.0028778838],"genre_scores_gemma":[0.7696787,0.00022523818,0.2216434,0.00056327105,0.000055971384,0.00028274267,0.0006504784,0.00021793951,0.006682235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997347,0.000034650937,0.000008590764,0.00011464527,0.000059126996,0.000048316993],"domain_scores_gemma":[0.9996427,0.0001193061,0.00003051617,0.00008700248,0.00008716891,0.00003333027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006883269,0.001023545,0.00072037586,0.00035016309,0.00028645972,0.0005049584,0.002078634,0.0011133469,0.0016817262],"category_scores_gemma":[0.001404538,0.00058626226,0.0004965483,0.0003202024,0.00070379704,0.0012781511,0.001622271,0.0015748806,0.00079836504],"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":[0.00029082134,0.00025611822,0.001884499,0.00010808584,0.00013156161,0.00020706379,0.00013636566,0.5529222,0.04611597,0.005299214,0.0043579917,0.38829014],"study_design_scores_gemma":[0.000007114228,0.00003803386,0.00020064498,0.0000049099576,0.00000814551,0.00003542287,0.0000066800826,0.99100447,0.0064729904,0.0015951332,0.00061824167,0.0000081856],"about_ca_topic_score_codex":0.0049751597,"about_ca_topic_score_gemma":0.0071284752,"teacher_disagreement_score":0.0049751597,"about_ca_system_score_codex":0.000830121,"about_ca_system_score_gemma":0.0009170757,"threshold_uncertainty_score":0.009892404},"labels":[],"label_agreement":null},{"id":"W3209365819","doi":"10.1145/3459637.3482458","title":"Norma","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Centroid; Domain (mathematical analysis); Cluster analysis; Pattern recognition (psychology); Class (philosophy); Feature (linguistics); Process (computing); Machine learning; Adaptation (eye); Image (mathematics); Feature extraction; Domain adaptation; Feature learning; Mathematics","score_opus":0.013004060560368442,"score_gpt":0.22546656784979396,"score_spread":0.21246250728942553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209365819","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005474891,0.0008416633,0.9580511,0.000381321,0.0003912,0.0001812428,0.0016213917,0.013911358,0.019145835],"genre_scores_gemma":[0.118374534,0.0009016138,0.8160919,0.0011981612,0.00023055669,0.0007343173,0.0141328825,0.0027267146,0.045609284],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978745,0.0004831209,0.00012236161,0.0007031748,0.0006657696,0.00015105003],"domain_scores_gemma":[0.9983176,0.00032533144,0.0001032279,0.0006772088,0.00048451277,0.00009219394],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001804596,0.0011213912,0.00102626,0.0013851989,0.00087888906,0.0021186632,0.0029574444,0.001538426,0.0173634],"category_scores_gemma":[0.0053920373,0.0004751803,0.00117,0.0011190893,0.0008184151,0.0030681067,0.0032278236,0.0021860984,0.015479531],"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":[0.00032560172,0.00020239834,0.001101328,0.00034898205,0.00010120549,0.00012308202,0.0001021984,0.043240543,0.009692833,0.057740953,0.07436996,0.81265104],"study_design_scores_gemma":[0.000047496138,0.00015441244,0.00081809884,0.0000749713,0.00003813209,0.00047026193,0.000095554096,0.69685495,0.018490877,0.10868169,0.1742082,0.00006545721],"about_ca_topic_score_codex":0.0021552078,"about_ca_topic_score_gemma":0.0039155115,"teacher_disagreement_score":0.9826366,"about_ca_system_score_codex":0.00092645094,"about_ca_system_score_gemma":0.001578486,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W3210561222","doi":"10.1145/3459637.3482380","title":"Pulling Up by the Causal Bootstraps","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research; Vector Institute; Microsoft Research","keywords":"Computer science; Machine learning; Debiasing; Artificial intelligence; Spurious relationship; Causal inference; Bootstrapping (finance); Benchmarking; Causation; Causal model; Data mining; Econometrics; Psychology; Statistics","score_opus":0.036162575602385824,"score_gpt":0.2725850856762464,"score_spread":0.23642251007386056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210561222","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":[0.0785961,0.00071896316,0.91236365,0.0012918259,0.00018784007,0.00024146297,0.00029306152,0.0031696712,0.0031374178],"genre_scores_gemma":[0.8013454,0.00029270982,0.19370008,0.0011045542,0.00014017824,0.00029655447,0.00075471133,0.0005805856,0.0017852572],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945192,0.0026877057,0.00025511187,0.0011395493,0.001082471,0.00031603215],"domain_scores_gemma":[0.95976037,0.022259396,0.002003851,0.013080696,0.0023458044,0.0005498705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012065409,0.0011460945,0.0011161015,0.0011281961,0.0012298159,0.0017714882,0.002551501,0.0016438147,0.003901225],"category_scores_gemma":[0.08929535,0.0008429495,0.0011259661,0.00093931233,0.0026520893,0.00432667,0.0046439418,0.0035435725,0.0010728887],"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":[0.0011447453,0.00047881276,0.04581451,0.0008974914,0.00049511413,0.0010349978,0.0017717232,0.29898733,0.02278551,0.15103355,0.01530448,0.46025184],"study_design_scores_gemma":[0.00008720563,0.00024070831,0.003677948,0.00017952386,0.000106307474,0.00045390023,0.00023967157,0.79748154,0.021709312,0.16711095,0.008645816,0.000067055604],"about_ca_topic_score_codex":0.0019780376,"about_ca_topic_score_gemma":0.0037090266,"teacher_disagreement_score":0.012065409,"about_ca_system_score_codex":0.00093462144,"about_ca_system_score_gemma":0.0022039916,"threshold_uncertainty_score":0.06380874},"labels":[],"label_agreement":null},{"id":"W3210701102","doi":"10.1007/978-3-030-88004-0_8","title":"Attentive Contrast Learning Network for Fine-Grained Classification","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Discriminative model; Artificial intelligence; Pairwise comparison; Contrast (vision); Feature learning; Leverage (statistics); Feature (linguistics); Representation (politics); Pattern recognition (psychology); Benchmark (surveying); Generator (circuit theory); Machine learning; Power (physics)","score_opus":0.029991139601150366,"score_gpt":0.26005333933723634,"score_spread":0.230062199736086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210701102","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045602717,0.0035414032,0.9349003,0.00047185915,0.00044138503,0.00012550903,0.0004798142,0.004440612,0.009996496],"genre_scores_gemma":[0.618139,0.0015031481,0.34922713,0.0007987835,0.0003392021,0.00017369217,0.0017691948,0.00034183016,0.027708065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981624,0.000023534676,0.0000068188906,0.00009647119,0.000024773677,0.000032206364],"domain_scores_gemma":[0.99971503,0.00011415098,0.000017245122,0.00005798953,0.000067533794,0.000027933913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005130718,0.0009662948,0.00080945465,0.0004736714,0.0003985108,0.0009103192,0.0019907146,0.0011855863,0.0067282766],"category_scores_gemma":[0.00087264227,0.0003816707,0.00065331446,0.0005562978,0.00044308687,0.0015809964,0.0014232153,0.0020495798,0.0019533453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037293247,0.00035991645,0.0010365432,0.0001975291,0.00014817092,0.00016426743,0.00009725463,0.05011848,0.05318685,0.015666297,0.015132941,0.86351883],"study_design_scores_gemma":[0.00001847975,0.00016289212,0.0008455138,0.000029327137,0.00009329109,0.00011453211,0.000029169038,0.95436436,0.018952208,0.019236317,0.006130252,0.000023548573],"about_ca_topic_score_codex":0.0038077089,"about_ca_topic_score_gemma":0.006426492,"teacher_disagreement_score":0.0067282766,"about_ca_system_score_codex":0.00070750073,"about_ca_system_score_gemma":0.0005602943,"threshold_uncertainty_score":0.022508264},"labels":[],"label_agreement":null},{"id":"W3212640884","doi":"","title":"Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Generalization; Computer science; Benchmark (surveying); Artificial intelligence; Meta learning (computer science); Property (philosophy); Probably approximately correct learning; Norm (philosophy); Upper and lower bounds; Statistical learning theory; Theoretical computer science; Algorithm; Machine learning; Generalization error; Mathematics; Unsupervised learning; Task (project management)","score_opus":0.07340860932113553,"score_gpt":0.198234698922775,"score_spread":0.12482608960163948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212640884","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":[0.009549852,0.0022093917,0.97918254,0.0021990628,0.00013332572,0.00008925638,0.00023018545,0.00034277717,0.0060636424],"genre_scores_gemma":[0.6981818,0.005682026,0.27946743,0.00415417,0.001738297,0.0012661665,0.001004554,0.0010922274,0.007413319],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99211144,0.0029616838,0.00037529258,0.0016353204,0.0023174162,0.0005989068],"domain_scores_gemma":[0.93885887,0.047024082,0.0027927016,0.0070188595,0.0030947612,0.0012107892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017168283,0.0027542347,0.003140899,0.0030882263,0.0017797379,0.00453835,0.006438899,0.004748514,0.0062210094],"category_scores_gemma":[0.09142778,0.0014057156,0.0027124006,0.003254053,0.006347851,0.016399002,0.009829555,0.012754143,0.0013586249],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021180852,0.0001515066,0.0014610969,0.0005716027,0.00027327475,0.00018742803,0.0003455426,0.3004134,0.002453354,0.63697344,0.006706684,0.050250854],"study_design_scores_gemma":[0.000012668993,0.00008069666,0.00028087522,0.00011006388,0.000038060243,0.00009892876,0.000030253166,0.5534508,0.0008464171,0.4436294,0.0013877717,0.000033930184],"about_ca_topic_score_codex":0.0014303753,"about_ca_topic_score_gemma":0.0013850532,"teacher_disagreement_score":0.017168283,"about_ca_system_score_codex":0.0048918705,"about_ca_system_score_gemma":0.001949958,"threshold_uncertainty_score":0.090795636},"labels":[],"label_agreement":null},{"id":"W3213044144","doi":"10.48550/arxiv.2111.07971","title":"Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Exploit; Perception; Domain (mathematical analysis); Artificial intelligence; Human–computer interaction; Focus (optics); Segmentation; Adaptation (eye); Machine learning; Driving simulator; Computer security","score_opus":0.130858352525539,"score_gpt":0.2278842024047157,"score_spread":0.0970258498791767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213044144","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":[0.113865875,0.0006102904,0.8814035,0.00076264335,0.000058019676,0.000098482116,0.00014732881,0.0013870527,0.0016668723],"genre_scores_gemma":[0.86538225,0.00022321829,0.13098839,0.0005228177,0.000053183932,0.0002072565,0.0005778442,0.0002067949,0.0018382741],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956185,0.00018653374,0.000019703757,0.00013510596,0.00003519274,0.00006169083],"domain_scores_gemma":[0.99748,0.0018165968,0.00015385883,0.00019225423,0.00022760556,0.00012971554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016437257,0.001278822,0.0010991293,0.00065586506,0.00040422613,0.00092722464,0.002368535,0.001787694,0.0016542973],"category_scores_gemma":[0.0064741634,0.0010091243,0.0008776752,0.00045203138,0.0011870046,0.0021562397,0.001630149,0.0027431103,0.0005038936],"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":[0.000107731066,0.00013662386,0.0012259083,0.0000690286,0.000059234764,0.00003219402,0.00011086075,0.93306065,0.002421022,0.0038256608,0.0012615444,0.05768951],"study_design_scores_gemma":[0.000005588217,0.000015451486,0.00006396577,0.0000044488015,0.000003177757,0.000003607174,0.000010035746,0.9967668,0.00033272323,0.0027129843,0.00007873906,0.000002574489],"about_ca_topic_score_codex":0.006747185,"about_ca_topic_score_gemma":0.0066648265,"teacher_disagreement_score":0.006747185,"about_ca_system_score_codex":0.0012562661,"about_ca_system_score_gemma":0.0012321279,"threshold_uncertainty_score":0.013415813},"labels":[],"label_agreement":null},{"id":"W3213380193","doi":"","title":"Self-Supervised Learning with Kernel Dependence Maximization","year":2021,"lang":"en","type":"article","venue":"UCL Discovery (University College London)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Mutual information; Estimator; Pattern recognition (psychology); Machine learning; Kernel (algebra); Maximization; Transfer of learning; Mathematics; Statistics; Mathematical optimization","score_opus":0.007582901601700507,"score_gpt":0.17652225600873603,"score_spread":0.16893935440703553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213380193","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":[0.007079203,0.00010615347,0.99087197,0.00015843239,0.000016127124,0.00003326724,0.00007117291,0.0008766327,0.00078702223],"genre_scores_gemma":[0.51974237,0.00023522098,0.47078156,0.000604362,0.00020651256,0.00034843315,0.001307686,0.0007268651,0.0060469555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99822146,0.0007224536,0.00006570845,0.00046919263,0.0004035004,0.00011769932],"domain_scores_gemma":[0.9949496,0.0021532583,0.00044056083,0.0013925596,0.00083205255,0.00023208662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027806407,0.0011396866,0.0015072547,0.0010796059,0.0004886229,0.0013253316,0.0033635371,0.0017230576,0.0021971192],"category_scores_gemma":[0.0077456427,0.00063563633,0.0009738485,0.0010097619,0.0019255291,0.002563064,0.0027525625,0.002402477,0.0012175187],"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":[0.00023681144,0.00040957695,0.002669799,0.0002924525,0.0002732017,0.00014617048,0.0002559579,0.6381735,0.009855769,0.05875479,0.012693903,0.27623802],"study_design_scores_gemma":[0.000005700797,0.0000191548,0.000083238796,0.000003717243,0.000003696608,0.000012382423,0.0000042512247,0.98594993,0.0010005883,0.012543262,0.00036871177,0.000005342283],"about_ca_topic_score_codex":0.0018668238,"about_ca_topic_score_gemma":0.002870786,"teacher_disagreement_score":0.0033635371,"about_ca_system_score_codex":0.0012382859,"about_ca_system_score_gemma":0.0015141683,"threshold_uncertainty_score":0.014705658},"labels":[],"label_agreement":null},{"id":"W3213703420","doi":"10.32920/22734362","title":"Edge-preserving Domain Adaptation for semantic segmentation of Medical Images","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer science; Domain adaptation; Artificial intelligence; Adaptation (eye); Domain (mathematical analysis); Enhanced Data Rates for GSM Evolution; Image (mathematics); Process (computing); Pattern recognition (psychology); Transformation (genetics); Computer vision; Image segmentation; Labeled data; Mathematics","score_opus":0.07401185805104676,"score_gpt":0.336702291871469,"score_spread":0.26269043382042223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213703420","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031484615,0.00029495472,0.96490055,0.00016173194,0.000035392288,0.000059392238,0.00010549581,0.0019836088,0.00097429176],"genre_scores_gemma":[0.42535573,0.00057808997,0.56639403,0.0005018118,0.00009304103,0.0001316383,0.0013229966,0.00056664436,0.005056012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995701,0.000112397094,0.000019557297,0.00014086707,0.00011242722,0.00004461026],"domain_scores_gemma":[0.99944097,0.00016929078,0.00006836464,0.00018419944,0.00009729084,0.000039778253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009834085,0.0005766315,0.0007210472,0.0013286651,0.0002882278,0.0006508528,0.0010916142,0.0009895981,0.0012097671],"category_scores_gemma":[0.0018952362,0.00039510278,0.0008307303,0.0010098601,0.0008495783,0.0010043987,0.0011460694,0.0013488348,0.0008224287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036580113,0.00018562966,0.0015264275,0.00013831539,0.00012490607,0.0001755481,0.00015510517,0.25848693,0.08156456,0.0074359905,0.0063910023,0.6434498],"study_design_scores_gemma":[0.00001133657,0.000045330846,0.0007407429,0.000008293142,0.000011904464,0.000183331,0.00002218391,0.9655994,0.022522053,0.008784049,0.0020553756,0.0000160359],"about_ca_topic_score_codex":0.0021986386,"about_ca_topic_score_gemma":0.0029860185,"teacher_disagreement_score":0.0021986386,"about_ca_system_score_codex":0.0006103276,"about_ca_system_score_gemma":0.0006835613,"threshold_uncertainty_score":0.0052008033},"labels":[],"label_agreement":null},{"id":"W3214945533","doi":"10.1016/j.artint.2021.103635","title":"CVPR 2020 continual learning in computer vision competition: Approaches, results, current challenges and future directions","year":2022,"lang":"en","type":"article","venue":"CINECA IRIS Institutial research information system (University of Pisa)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Benchmarking; Computer science; Artificial intelligence; Forgetting; Benchmark (surveying); Task (project management); Field (mathematics); Machine learning; Set (abstract data type); Deep learning; Competition (biology)","score_opus":0.07669307990777106,"score_gpt":0.279338122157276,"score_spread":0.20264504224950491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214945533","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":[0.12991995,0.28012878,0.26034164,0.090439074,0.0401047,0.0023891383,0.022136461,0.04317614,0.13136417],"genre_scores_gemma":[0.3813247,0.04296256,0.34005782,0.016500693,0.011507749,0.0018017354,0.110526145,0.008997528,0.08632117],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9857018,0.003927632,0.00045696,0.0025734736,0.00544501,0.0018950466],"domain_scores_gemma":[0.98082733,0.0040313746,0.0003627102,0.0019712145,0.008285392,0.0045219553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02870553,0.004668684,0.0039659133,0.004724724,0.0023246594,0.006753362,0.0073270113,0.00677973,0.010782147],"category_scores_gemma":[0.02557523,0.0007179107,0.0014211148,0.003230127,0.0023545173,0.0066750273,0.0060652154,0.0060381163,0.008593992],"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":[0.00092782936,0.0014428536,0.0022041937,0.0012342794,0.00039305037,0.00015249086,0.0001374737,0.017120758,0.0022842186,0.0072873994,0.5028774,0.4639381],"study_design_scores_gemma":[0.0009391653,0.0043516774,0.016736398,0.002129209,0.00053583184,0.0012257175,0.0014642397,0.3140037,0.014268064,0.058328383,0.5855457,0.00047184943],"about_ca_topic_score_codex":0.020311443,"about_ca_topic_score_gemma":0.023136089,"teacher_disagreement_score":0.02870553,"about_ca_system_score_codex":0.0037027732,"about_ca_system_score_gemma":0.005739036,"threshold_uncertainty_score":0.15181112},"labels":[],"label_agreement":null},{"id":"W3215957616","doi":"10.1109/tip.2021.3128311","title":"A Prototypical Knowledge Oriented Adaptation Framework for Semantic Segmentation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Discriminative model; Segmentation; Leverage (statistics); Overfitting; Discriminator; Domain adaptation; Convolutional neural network; Feature learning; Transfer of learning; Pattern recognition (psychology)","score_opus":0.03283645738904421,"score_gpt":0.3168077497371934,"score_spread":0.28397129234814916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215957616","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060253115,0.00023499713,0.99122757,0.00012157217,0.00003709043,0.000028073902,0.00006167808,0.0007718306,0.0014918613],"genre_scores_gemma":[0.51697594,0.0008202079,0.4726446,0.0006049325,0.00016477412,0.00017302399,0.0007973412,0.00038663228,0.0074325665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995907,0.000073347706,0.000014674214,0.000173297,0.00010129544,0.000046707908],"domain_scores_gemma":[0.9997532,0.00006401434,0.00002868178,0.0000749705,0.000053643776,0.000025483794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057206873,0.00076546625,0.0006346394,0.0006779107,0.00036375268,0.0007419356,0.0014162833,0.0012102391,0.0017153845],"category_scores_gemma":[0.0010654513,0.00028445013,0.00070071314,0.0008099075,0.0011656194,0.0014013988,0.0015738604,0.0014578584,0.00082055974],"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":[0.00016334282,0.00016873139,0.000813276,0.00017335027,0.00011542365,0.00034571925,0.0002877235,0.48102167,0.04959658,0.051231522,0.0067223595,0.4093603],"study_design_scores_gemma":[0.000005124666,0.000028928931,0.00021391595,0.000007705598,0.000009217231,0.00014366391,0.000019209408,0.96884143,0.0061246203,0.021544907,0.0030482935,0.000012913091],"about_ca_topic_score_codex":0.002038137,"about_ca_topic_score_gemma":0.002542811,"teacher_disagreement_score":0.002038137,"about_ca_system_score_codex":0.0005757298,"about_ca_system_score_gemma":0.0008174478,"threshold_uncertainty_score":0.0057385564},"labels":[],"label_agreement":null},{"id":"W3217026627","doi":"10.48550/arxiv.2111.15430","title":"The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Compute Canada","keywords":"Softmax function; Discriminative model; Margin (machine learning); Computer science; Artificial intelligence; Overfitting; Calibration; Machine learning; Artificial neural network; Mathematical optimization; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.09479604473760554,"score_gpt":0.2060714126245323,"score_spread":0.11127536788692675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217026627","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014867247,0.00044694648,0.9780176,0.0007075771,0.000119548866,0.0000684377,0.00013928176,0.0029792595,0.0026540193],"genre_scores_gemma":[0.48698476,0.0005744904,0.49440464,0.0016992661,0.00033666156,0.0004390547,0.0009840026,0.0024863044,0.012090846],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981487,0.00055160624,0.00007960309,0.00064857205,0.00042262152,0.00014889924],"domain_scores_gemma":[0.99600524,0.0014285112,0.00044000047,0.0014502842,0.0004803964,0.0001955275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034599511,0.0015294738,0.0013051585,0.0011167204,0.0010737317,0.001989685,0.00378192,0.0026675998,0.005838247],"category_scores_gemma":[0.0155766485,0.00072179904,0.001174599,0.001231556,0.0022777098,0.0047711856,0.005594905,0.0051903185,0.0025779756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006323796,0.00022702402,0.0029877098,0.00024918473,0.00014942624,0.00027147168,0.0007407057,0.38039646,0.019696929,0.074088186,0.017878722,0.5026818],"study_design_scores_gemma":[0.000033758173,0.00005491567,0.00035450407,0.00005385052,0.000024735935,0.000081347935,0.00004645119,0.9298825,0.006880871,0.057533827,0.00501928,0.000033894437],"about_ca_topic_score_codex":0.0023517422,"about_ca_topic_score_gemma":0.003485302,"teacher_disagreement_score":0.005838247,"about_ca_system_score_codex":0.0014679374,"about_ca_system_score_gemma":0.0015984138,"threshold_uncertainty_score":0.019530892},"labels":[],"label_agreement":null},{"id":"W4200294443","doi":"10.1109/iros51168.2021.9636449","title":"Latent Attention Augmentation for Robust Autonomous Driving Policies","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; McGill University","funders":"","keywords":"Reinforcement learning; Computer science; Pipeline (software); Artificial intelligence; Adaptability; Machine learning; Domain (mathematical analysis); Segmentation; Robot; State space; Latent semantic analysis; Adaptation (eye)","score_opus":0.10033025223318776,"score_gpt":0.3167034949752955,"score_spread":0.21637324274210773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200294443","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":[0.036372006,0.00016681834,0.9607194,0.00015959567,0.00003306841,0.000036749665,0.00004679582,0.0012396915,0.0012258876],"genre_scores_gemma":[0.93551683,0.0000709156,0.062413447,0.00010953467,0.000025678102,0.00008658289,0.000103899954,0.000091819755,0.001581249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967194,0.000089230845,0.000014736049,0.000100634934,0.0000647237,0.000058729904],"domain_scores_gemma":[0.9989819,0.0005903885,0.000106281324,0.00012953515,0.00013359441,0.000058312777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087759126,0.00068450725,0.00075234845,0.00034824616,0.0003176381,0.00054123957,0.0010047111,0.000726283,0.0017286317],"category_scores_gemma":[0.0036303543,0.00044784448,0.00044678999,0.00022941417,0.0008055564,0.0013480873,0.0012564504,0.0016528158,0.00038092077],"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":[0.00013401694,0.00010389185,0.0005271165,0.00004352736,0.000024306464,0.000043820328,0.000082226456,0.91519153,0.0060374644,0.0062535927,0.00095271826,0.07060579],"study_design_scores_gemma":[0.0000030220954,0.000013414759,0.0000430549,0.000001687992,0.000001643439,0.0000035025307,0.0000022756674,0.99714917,0.00068913243,0.0019810176,0.000110042405,0.0000020469672],"about_ca_topic_score_codex":0.0035237016,"about_ca_topic_score_gemma":0.0034827474,"teacher_disagreement_score":0.0035237016,"about_ca_system_score_codex":0.00082108186,"about_ca_system_score_gemma":0.00093220687,"threshold_uncertainty_score":0.007006407},"labels":[],"label_agreement":null},{"id":"W4205328115","doi":"10.1109/smc52423.2021.9658756","title":"Accounting for the Effect of Inter-Task Similarity in Continual Learning Models","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Forgetting; Computer science; Similarity (geometry); Task (project management); Consolidation (business); Artificial intelligence; Context (archaeology); Machine learning; Incremental learning; Memory consolidation; Cognitive psychology; Psychology; Accounting; Engineering","score_opus":0.055004168407974674,"score_gpt":0.29560070949865835,"score_spread":0.24059654109068368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205328115","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":[0.42023233,0.001072926,0.5742826,0.00062897976,0.00015561549,0.00014692772,0.00010247049,0.0010531581,0.0023249448],"genre_scores_gemma":[0.9709651,0.00017003562,0.027643418,0.00009792281,0.00003474472,0.0000705208,0.00007636636,0.00003905098,0.0009028922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999047,0.00023542295,0.000090284935,0.00028670227,0.00019373585,0.00014689182],"domain_scores_gemma":[0.99028045,0.005372551,0.00094126386,0.00230415,0.0007218352,0.0003797845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004778616,0.0011399791,0.0011447328,0.00056424213,0.0005224048,0.0012253083,0.0022256582,0.0013345369,0.0013378474],"category_scores_gemma":[0.019774232,0.0004733805,0.0005594837,0.0005735945,0.0014043107,0.0030710702,0.002157922,0.0027433475,0.00031448234],"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":[0.0009577065,0.0009783527,0.01478472,0.00038020549,0.00029738815,0.00037524573,0.0006166082,0.67636716,0.020749811,0.011877627,0.0013147447,0.27130038],"study_design_scores_gemma":[0.000026504844,0.0003228369,0.0018428827,0.000019211611,0.000041595184,0.00012004555,0.00004567078,0.98187315,0.0045032483,0.010793992,0.00038383377,0.000027077449],"about_ca_topic_score_codex":0.0028494042,"about_ca_topic_score_gemma":0.0031035596,"teacher_disagreement_score":0.004778616,"about_ca_system_score_codex":0.0006587313,"about_ca_system_score_gemma":0.0012206377,"threshold_uncertainty_score":0.025272012},"labels":[],"label_agreement":null},{"id":"W4205886089","doi":"10.1007/s43674-021-00022-8","title":"Toward durable representations for continual learning","year":2021,"lang":"en","type":"article","venue":"Advances in Computational Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 Waterloo","funders":"Electronics and Telecommunications Research Institute","keywords":"Forgetting; Computer science; Regularization (linguistics); Ask price; Artificial intelligence; Machine learning; Process (computing); Forcing (mathematics); Metric (unit); Reduction (mathematics); Cognitive psychology; Mathematics; Engineering; Psychology","score_opus":0.04618450496025814,"score_gpt":0.35264365796515734,"score_spread":0.30645915300489923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205886089","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":[0.021154875,0.00051102974,0.97535706,0.0005551782,0.000076107775,0.00002564106,0.00014891406,0.00062154426,0.0015496549],"genre_scores_gemma":[0.7135054,0.0008157373,0.27642778,0.00045435491,0.00023817373,0.00020220617,0.0009352492,0.00032858163,0.0070924824],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992859,0.0002101721,0.00004983178,0.00026020422,0.00012151677,0.00007248518],"domain_scores_gemma":[0.99453676,0.002796721,0.0003004558,0.0016381163,0.00044352308,0.0002845163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018312922,0.00062039396,0.0011541547,0.0009951541,0.0006370264,0.0015970903,0.0025450953,0.0017753621,0.0048344177],"category_scores_gemma":[0.010953159,0.00067372294,0.00078887533,0.00089852867,0.0018120885,0.005631517,0.0037673586,0.004370751,0.001011873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002530236,0.00027215952,0.0019471459,0.0002823529,0.00014550635,0.00015337192,0.00049877935,0.31089276,0.005262993,0.377575,0.007640255,0.29507664],"study_design_scores_gemma":[0.000008828142,0.0000301667,0.0001367699,0.000020829706,0.000010041302,0.000027156282,0.000030724244,0.75554156,0.00054974854,0.24246414,0.0011712399,0.000008846742],"about_ca_topic_score_codex":0.0016251892,"about_ca_topic_score_gemma":0.0028526832,"teacher_disagreement_score":0.0048344177,"about_ca_system_score_codex":0.0009028814,"about_ca_system_score_gemma":0.00066026056,"threshold_uncertainty_score":0.016172707},"labels":[],"label_agreement":null},{"id":"W4206056756","doi":"10.1007/978-3-030-90439-5_46","title":"Unsupervised Pixel-Wise Weighted Adversarial Domain Adaptation","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Leverage (statistics); Weighting; Pattern recognition (psychology); Domain adaptation; Pixel; Machine learning; Domain (mathematical analysis); Feature (linguistics); Convolutional neural network; Adaptation (eye); Classifier (UML); Mathematics","score_opus":0.019715056771635906,"score_gpt":0.23448757379452811,"score_spread":0.2147725170228922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206056756","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036748417,0.0002792705,0.9936212,0.00006307814,0.000054342392,0.000022404996,0.00007817816,0.00054373866,0.0016629859],"genre_scores_gemma":[0.36249867,0.0014384851,0.59851956,0.0005292083,0.0002215121,0.00021917476,0.0019138189,0.000811604,0.033848036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996402,0.0000791667,0.000011849115,0.0001358695,0.000091612696,0.00004134872],"domain_scores_gemma":[0.99941957,0.0002621283,0.00003312,0.00016596283,0.000087416076,0.00003184645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006973496,0.0009326347,0.0010804932,0.00047431886,0.00026434165,0.0007580872,0.0018227926,0.0011748127,0.0035369615],"category_scores_gemma":[0.0016514112,0.00048660915,0.0009145165,0.0007682809,0.00074022653,0.001205635,0.0021724147,0.0020047878,0.0020723636],"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":[0.00015492892,0.00012488142,0.00036028586,0.00016100876,0.00014015884,0.00012521187,0.00005529432,0.5495808,0.027108844,0.02130355,0.011272637,0.38961238],"study_design_scores_gemma":[0.000002887577,0.000018068524,0.000111940295,0.0000072094517,0.000009394069,0.00006761763,0.000004944254,0.9865819,0.0036705825,0.008054116,0.0014642435,0.0000071613604],"about_ca_topic_score_codex":0.0015087647,"about_ca_topic_score_gemma":0.002290273,"teacher_disagreement_score":0.0035369615,"about_ca_system_score_codex":0.00041301028,"about_ca_system_score_gemma":0.00045180434,"threshold_uncertainty_score":0.011832356},"labels":[],"label_agreement":null},{"id":"W4220695975","doi":"10.36939/ir.202203291405","title":"Unsupervised Domain Adaptation using Satellite Images for Significantly Different Infrastructure Objects","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Manitoba Hydro","keywords":"Computer science; Domain (mathematical analysis); Intersection (aeronautics); Segmentation; Artificial intelligence; Land cover; Deep learning; Domain adaptation; Satellite; Process (computing); Satellite imagery; Remote sensing; Machine learning; Geography; Cartography; Land use; Engineering; Mathematics","score_opus":0.02192766729168261,"score_gpt":0.2746934257421149,"score_spread":0.2527657584504323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220695975","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55052257,0.001445226,0.42931294,0.0004254457,0.0004599579,0.00027801658,0.0015114547,0.0068837823,0.009160542],"genre_scores_gemma":[0.87285984,0.00036788915,0.11824521,0.000250102,0.000051787774,0.000107331434,0.00452787,0.00022511958,0.0033648994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994435,0.00011091753,0.00002104073,0.0002490979,0.00008798264,0.000087441265],"domain_scores_gemma":[0.99950826,0.00011560544,0.000047949285,0.00015952592,0.00013549549,0.000033199278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084406347,0.0012263388,0.00058787933,0.0009304016,0.00034609684,0.00093716325,0.000993884,0.00082260586,0.0008486462],"category_scores_gemma":[0.0019027385,0.00030389248,0.0012514258,0.0009641465,0.0006268761,0.0011988841,0.0009743459,0.0013417228,0.0007797758],"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":[0.000653748,0.0003769902,0.014555307,0.00025816704,0.00032638374,0.0005041868,0.00035066772,0.5178831,0.042638853,0.003294464,0.0109770745,0.408181],"study_design_scores_gemma":[0.000022772152,0.000069361355,0.0048313956,0.000027692084,0.00004243539,0.00013721923,0.00017581902,0.97221035,0.015962359,0.002364875,0.0041239765,0.000031707074],"about_ca_topic_score_codex":0.010100122,"about_ca_topic_score_gemma":0.0104201725,"teacher_disagreement_score":0.010100122,"about_ca_system_score_codex":0.00074758544,"about_ca_system_score_gemma":0.0008975767,"threshold_uncertainty_score":0.020082653},"labels":[],"label_agreement":null},{"id":"W4220725176","doi":"10.1109/icsc52841.2022.00015","title":"MixedGAN: Facilitating GAN for Domain Adaptation Learning based on Mixing Different Domain Images for Semantic Segmentation","year":2022,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Domain (mathematical analysis); Convolutional neural network; Domain adaptation; Feature (linguistics); Pattern recognition (psychology); Image (mathematics); Pixel; Adaptation (eye); Deep learning; Labeled data; Artificial neural network; Machine learning; Mathematics","score_opus":0.025181326899881756,"score_gpt":0.2634679964428302,"score_spread":0.23828666954294844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220725176","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":[0.02431223,0.00035718043,0.9683488,0.00022161915,0.000095176234,0.0000636963,0.00014512351,0.0022421123,0.0042140214],"genre_scores_gemma":[0.6618028,0.000511422,0.32781312,0.0006194601,0.00008572788,0.00018464391,0.0010500731,0.00053989707,0.007392726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997434,0.000076140925,0.000007908465,0.00009531959,0.000043649725,0.00003367773],"domain_scores_gemma":[0.99973136,0.000102160775,0.000019770347,0.00008264857,0.00004310247,0.000020864174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005508859,0.0010025001,0.00048456862,0.00042157288,0.00023245532,0.0005278456,0.00093991787,0.00071724225,0.0021815877],"category_scores_gemma":[0.0012715644,0.0003163476,0.00062908017,0.00035677484,0.000638963,0.0011403325,0.0011714447,0.0013891133,0.00071013556],"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":[0.0003244681,0.00020903442,0.0018259983,0.00017360231,0.0001616444,0.00032868204,0.00022283825,0.62941796,0.048211496,0.034643743,0.011036461,0.2734441],"study_design_scores_gemma":[0.000006761379,0.000022743528,0.00014765683,0.0000064513347,0.0000080749505,0.000064758526,0.00001129699,0.9857158,0.005419283,0.006689226,0.0019010071,0.0000069782172],"about_ca_topic_score_codex":0.0020244804,"about_ca_topic_score_gemma":0.0034806132,"teacher_disagreement_score":0.0021815877,"about_ca_system_score_codex":0.00046547502,"about_ca_system_score_gemma":0.00041242366,"threshold_uncertainty_score":0.0072981715},"labels":[],"label_agreement":null},{"id":"W4220727198","doi":"10.31219/osf.io/4yz8f","title":"Learning strides in convolutional neural networks","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction","keywords":"Upsampling; Computer science; Pooling; Regularization (linguistics); Computational complexity theory; Convolutional neural network; Algorithm; Network architecture; Artificial intelligence; Stochastic gradient descent; Artificial neural network; Image (mathematics)","score_opus":0.02693017571803132,"score_gpt":0.2649890454092965,"score_spread":0.2380588696912652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220727198","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":[0.089933425,0.0011341546,0.90297014,0.00037187882,0.00010189174,0.00006313312,0.0001841593,0.0024477,0.002793517],"genre_scores_gemma":[0.7025101,0.00092449115,0.29091015,0.0003022416,0.000103836624,0.00017166391,0.00044312942,0.00046194368,0.0041723466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993861,0.00013971992,0.000050662642,0.00023144724,0.00013230965,0.000059756472],"domain_scores_gemma":[0.9986193,0.000626348,0.00017168008,0.00030966668,0.0001786195,0.00009438619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017459823,0.00097478874,0.00082297466,0.0008380796,0.0004932678,0.0009117129,0.0014251616,0.0010282825,0.0016498867],"category_scores_gemma":[0.0072573344,0.00090908416,0.00060918357,0.0006508789,0.0014234099,0.0026234589,0.0017647957,0.00197784,0.0007938736],"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":[0.00028842888,0.00014248666,0.0048878817,0.00025045325,0.00011135428,0.00019132943,0.00024537236,0.64494395,0.012709846,0.059061304,0.0047001233,0.2724675],"study_design_scores_gemma":[0.000022557679,0.00007383875,0.0005699761,0.000029861165,0.0000150667165,0.000075442505,0.000020241487,0.93982106,0.004585011,0.05308152,0.0016865293,0.000018887484],"about_ca_topic_score_codex":0.0019053483,"about_ca_topic_score_gemma":0.0028697473,"teacher_disagreement_score":0.0019053483,"about_ca_system_score_codex":0.0008250146,"about_ca_system_score_gemma":0.0007342929,"threshold_uncertainty_score":0.009233773},"labels":[],"label_agreement":null},{"id":"W4221152138","doi":"10.1609/aaai.v36i8.20890","title":"Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-trained Source Models","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Université Laval","funders":"","keywords":"Computer science; Exploit; Leverage (statistics); Interpretability; Domain adaptation; Domain (mathematical analysis); Artificial intelligence; Machine learning; Transformation (genetics); Context (archaeology); Maximization; Data mining; Adaptation (eye); Pattern recognition (psychology); Classifier (UML); Mathematics; Mathematical optimization","score_opus":0.07656031427760422,"score_gpt":0.28523607339203366,"score_spread":0.20867575911442943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221152138","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":[0.012191762,0.00021752682,0.9858372,0.00011487941,0.00003143289,0.000039475148,0.000057153462,0.00083890656,0.0006716349],"genre_scores_gemma":[0.5423358,0.0003977578,0.45073512,0.00038561624,0.00012832122,0.00023880154,0.0012876542,0.0005296894,0.0039612586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888486,0.00042533016,0.000049623734,0.0003976418,0.00016548851,0.0000770306],"domain_scores_gemma":[0.9975452,0.0012993421,0.00020933792,0.0005196382,0.00030325507,0.00012331225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001930214,0.0012308916,0.0011597135,0.0011392744,0.0005730347,0.0012106103,0.0014843054,0.0012706533,0.0017018499],"category_scores_gemma":[0.0057020104,0.0004646803,0.0012376992,0.0009959674,0.0010767797,0.0020930495,0.002244293,0.0033216227,0.0010162591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045865652,0.0003710989,0.0047422354,0.00024654358,0.0002142734,0.00044040306,0.0007514026,0.42191112,0.024876783,0.024405304,0.00756548,0.51401675],"study_design_scores_gemma":[0.0000083637715,0.000025527543,0.00036843558,0.000012063326,0.000012946355,0.00005232704,0.00004229147,0.97978485,0.0028264795,0.01576126,0.0010940921,0.000011312685],"about_ca_topic_score_codex":0.0019397165,"about_ca_topic_score_gemma":0.0023460423,"teacher_disagreement_score":0.0019397165,"about_ca_system_score_codex":0.0008598406,"about_ca_system_score_gemma":0.0009908114,"threshold_uncertainty_score":0.010208011},"labels":[],"label_agreement":null},{"id":"W4221159290","doi":"10.48550/arxiv.2203.13381","title":"Probing Representation Forgetting in Supervised and Unsupervised Continual Learning","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Forgetting; Representation (politics); Computer science; Artificial intelligence; Task (project management); Classifier (UML); Machine learning; Cognitive psychology; Psychology; Engineering","score_opus":0.07666561706497431,"score_gpt":0.20829727818052102,"score_spread":0.1316316611155467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221159290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5892345,0.0011275299,0.4059384,0.0005739784,0.00007592214,0.00009913018,0.00019436234,0.0009252324,0.0018309987],"genre_scores_gemma":[0.96871066,0.00012599351,0.030128803,0.00008337851,0.000032449963,0.00005959839,0.00020197555,0.000060105132,0.00059704407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980026,0.00066559477,0.000118162476,0.0006329238,0.00041770397,0.00016309283],"domain_scores_gemma":[0.9755657,0.01593448,0.0021152038,0.004470935,0.001256259,0.00065748044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006330721,0.0008022602,0.0009893501,0.000704504,0.0005064592,0.0010657578,0.0020659114,0.0014623482,0.00077900925],"category_scores_gemma":[0.041815534,0.00040849322,0.00049852283,0.00054452696,0.0023407694,0.0033841482,0.0019903556,0.0024573281,0.00016269591],"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":[0.0010798132,0.0008385264,0.022222323,0.0005721147,0.00027227745,0.000252229,0.00072745455,0.6996227,0.01617629,0.020949403,0.0020363324,0.23525049],"study_design_scores_gemma":[0.000021060865,0.0003250135,0.0035880138,0.000026396247,0.000019117904,0.00010738434,0.000057582063,0.9620641,0.007841563,0.02545355,0.00046467586,0.00003148729],"about_ca_topic_score_codex":0.0018086702,"about_ca_topic_score_gemma":0.0018476775,"teacher_disagreement_score":0.006330721,"about_ca_system_score_codex":0.0012177408,"about_ca_system_score_gemma":0.0008050661,"threshold_uncertainty_score":0.033480406},"labels":[],"label_agreement":null},{"id":"W4224255863","doi":"10.24963/ijcai.2022/177","title":"Continual Semantic Segmentation Leveraging Image-level Labels and Rehearsal","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Forgetting; Computer science; Leverage (statistics); Pascal (unit); Artificial intelligence; Segmentation; Machine learning; Deep learning; Deep neural networks","score_opus":0.08905293292888322,"score_gpt":0.28852288096350404,"score_spread":0.19946994803462081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224255863","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":[0.18650462,0.0010165956,0.7981532,0.00063256687,0.00016416129,0.00014044193,0.00034804753,0.008481812,0.0045584994],"genre_scores_gemma":[0.816378,0.0003451489,0.17605664,0.00036657194,0.000078796904,0.00010557834,0.0011955815,0.0005588973,0.0049147825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993136,0.00011930858,0.000035969824,0.00032379173,0.00010856157,0.00009877906],"domain_scores_gemma":[0.9981477,0.00056295044,0.00015949378,0.0007350678,0.00024860847,0.00014624867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013824281,0.00145036,0.0011469962,0.00080838974,0.0005098957,0.0012677937,0.0029065197,0.001332308,0.002260032],"category_scores_gemma":[0.004509275,0.00065078115,0.0009007436,0.000796558,0.0014475452,0.004885665,0.002727932,0.0027896794,0.0012449992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077216927,0.00069189584,0.005779948,0.00029984114,0.00020941668,0.00019292574,0.0008811603,0.1838629,0.075429894,0.009392211,0.0063499836,0.7161376],"study_design_scores_gemma":[0.000027017002,0.00026364828,0.0014674244,0.00003205657,0.000057851146,0.00012176025,0.000103382125,0.94856393,0.027932089,0.018489206,0.0028977026,0.000043952277],"about_ca_topic_score_codex":0.00469138,"about_ca_topic_score_gemma":0.008458031,"teacher_disagreement_score":0.00469138,"about_ca_system_score_codex":0.0007595453,"about_ca_system_score_gemma":0.0011122952,"threshold_uncertainty_score":0.0093281865},"labels":[],"label_agreement":null},{"id":"W4225620948","doi":"10.48550/arxiv.2112.09153","title":"An Empirical Investigation of the Role of Pre-training in Lifelong Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Forgetting; Computer science; Lifelong learning; Artificial intelligence; Context (archaeology); Task (project management); Machine learning; Popularity; Set (abstract data type); Variety (cybernetics); Training (meteorology); Cognitive psychology; Psychology; Engineering","score_opus":0.07271094776994619,"score_gpt":0.2187424208096863,"score_spread":0.14603147303974012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225620948","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89697057,0.005839295,0.088397756,0.0016461608,0.00013740416,0.00017184432,0.0004995528,0.0008608407,0.0054766005],"genre_scores_gemma":[0.98253214,0.000421658,0.015287851,0.00024339826,0.000036780664,0.00007268217,0.00048725907,0.00009212673,0.00082609797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759054,0.0012623328,0.00014498486,0.0005194522,0.00028272215,0.00020004362],"domain_scores_gemma":[0.93923473,0.047004543,0.0024535279,0.007750722,0.0021968505,0.0013595865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008888658,0.00088013895,0.0007595685,0.0005449698,0.0008791659,0.0011798734,0.0016688647,0.0015349159,0.0018869683],"category_scores_gemma":[0.067786194,0.00036004063,0.0005083307,0.0007312431,0.0014337417,0.0042571574,0.0015449675,0.0032313464,0.0005312825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020705462,0.0020953328,0.123828925,0.0014185433,0.00044274575,0.00046526061,0.0010994752,0.36558548,0.0066580107,0.013072459,0.016511908,0.46675128],"study_design_scores_gemma":[0.00019902708,0.0019025913,0.03685848,0.00026506025,0.00013888741,0.0006102278,0.00067126437,0.91057503,0.0077373763,0.034449305,0.0065003093,0.000092386166],"about_ca_topic_score_codex":0.0030986022,"about_ca_topic_score_gemma":0.0053273216,"teacher_disagreement_score":0.008888658,"about_ca_system_score_codex":0.00091416674,"about_ca_system_score_gemma":0.0008124367,"threshold_uncertainty_score":0.047008276},"labels":[],"label_agreement":null},{"id":"W4225707798","doi":"10.1109/tits.2022.3161939","title":"Instance-Level Knowledge Transfer for Data-Driven Driver Model Adaptation With Homogeneous Domains","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":13,"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":"Beijing Institute of Technology; National Natural Science Foundation of China","keywords":"Overtaking; Divergence (linguistics); Transfer of learning; Adaptation (eye); Computer science; Transfer function; Advanced driver assistance systems; Radial basis function; Driving simulator; Artificial intelligence; Simulation; Engineering; Machine learning; Artificial neural network","score_opus":0.111961431367178,"score_gpt":0.2834444285197868,"score_spread":0.17148299715260878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225707798","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":[0.046232782,0.0003226605,0.951301,0.000114837785,0.000050979124,0.00006976677,0.00008732241,0.0011494225,0.0006712782],"genre_scores_gemma":[0.83943856,0.00019032379,0.15749218,0.00023638447,0.000057660145,0.00019677586,0.0005859311,0.00012465846,0.0016775116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942577,0.00013888252,0.00003777345,0.0002352229,0.000105565414,0.000056813627],"domain_scores_gemma":[0.9986946,0.00063217076,0.00010783395,0.00025980856,0.00023929142,0.00006633138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013966064,0.0009050214,0.0010852684,0.00067704485,0.00032331626,0.000681139,0.002062285,0.0012850142,0.0010276384],"category_scores_gemma":[0.0051393523,0.000457438,0.00095011014,0.00065006176,0.0006108339,0.0016471604,0.0016464917,0.0019236744,0.00045331597],"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":[0.00019280697,0.00030987698,0.0030993493,0.00012560902,0.00017551206,0.000163196,0.0001982185,0.6997053,0.010613971,0.0032649322,0.0016504125,0.28050086],"study_design_scores_gemma":[0.000004045884,0.00002600674,0.00027966124,0.00000329872,0.0000073501596,0.000016116153,0.0000102755375,0.9965166,0.0013432677,0.0015755484,0.00021197066,0.000006002139],"about_ca_topic_score_codex":0.0039520254,"about_ca_topic_score_gemma":0.0025845955,"teacher_disagreement_score":0.0039520254,"about_ca_system_score_codex":0.00070029753,"about_ca_system_score_gemma":0.00074384426,"threshold_uncertainty_score":0.007858038},"labels":[],"label_agreement":null},{"id":"W4226054115","doi":"10.1007/978-3-030-99739-7_6","title":"A Light-Weight Strategy for Restraining Gender Biases in Neural Rankers","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":15,"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 Waterloo; Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Machine learning; Training set; Sampling (signal processing); Sampling bias; Statistics; Sample size determination; Mathematics","score_opus":0.0781440851206188,"score_gpt":0.29463180214013907,"score_spread":0.21648771701952027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226054115","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":[0.056418005,0.00085398584,0.931347,0.00066369347,0.00036199956,0.0000877345,0.000240661,0.002947641,0.007079296],"genre_scores_gemma":[0.66863996,0.00037139416,0.30581167,0.0007218945,0.0003121585,0.0001457184,0.0006008188,0.00079297036,0.022603374],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995289,0.000106374115,0.000027218877,0.00015662413,0.0000968914,0.00008404587],"domain_scores_gemma":[0.9989197,0.00033893695,0.00006414955,0.0002702732,0.00032257574,0.00008431913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014797194,0.0008838014,0.0007232078,0.00061528507,0.0005688841,0.0008682163,0.00197449,0.0014985864,0.0062421877],"category_scores_gemma":[0.0056040306,0.0004133358,0.0005480249,0.00067588285,0.00077496696,0.0016830043,0.001765851,0.0021304423,0.0023087007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038849888,0.00026783792,0.0015011908,0.00012141598,0.00012242662,0.00007172204,0.00017856085,0.04299221,0.04869472,0.0146800475,0.009635976,0.88134533],"study_design_scores_gemma":[0.00006212274,0.00020152352,0.0016711237,0.000035922865,0.00010921966,0.00016917095,0.00010051093,0.93592566,0.028097179,0.028527789,0.0050545787,0.000045188528],"about_ca_topic_score_codex":0.0032125681,"about_ca_topic_score_gemma":0.007826976,"teacher_disagreement_score":0.0062421877,"about_ca_system_score_codex":0.0005030315,"about_ca_system_score_gemma":0.00084190717,"threshold_uncertainty_score":0.02088219},"labels":[],"label_agreement":null},{"id":"W4226165407","doi":"10.1109/cogmi52975.2021.00013","title":"Impact Patterns of Combining Model Pruning and Continual Learning on Model Performance","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Pruning; Computer science; Machine learning; Artificial intelligence; Software deployment","score_opus":0.02559435759032219,"score_gpt":0.2689923622871192,"score_spread":0.243398004696797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226165407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8927992,0.006134825,0.088982955,0.0021846497,0.00040094336,0.00023680642,0.00047074875,0.0035655117,0.0052243],"genre_scores_gemma":[0.9561788,0.0005167633,0.041383274,0.0002869049,0.00006834569,0.00007495905,0.0005415716,0.0003102593,0.0006390965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9916875,0.0032958142,0.00065675017,0.0018797296,0.001549077,0.00093118916],"domain_scores_gemma":[0.9368987,0.04734175,0.0021116908,0.008299733,0.0040041874,0.0013439042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01394456,0.0022552493,0.0014858857,0.0016166869,0.0012396335,0.0024064332,0.0024767476,0.0025749025,0.0010165035],"category_scores_gemma":[0.067837395,0.0008665068,0.0009742513,0.0010866827,0.001930271,0.0055997116,0.002609815,0.0033641001,0.0004423085],"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":[0.0023997861,0.0012557557,0.05520501,0.0007861241,0.0010454446,0.0006158237,0.00068489125,0.66802657,0.0138817895,0.004122273,0.0056077135,0.24636884],"study_design_scores_gemma":[0.00012288571,0.0013441873,0.008387811,0.0001962744,0.000300034,0.00049972563,0.00073220854,0.961859,0.017986035,0.0061729597,0.002302708,0.00009622955],"about_ca_topic_score_codex":0.0072566327,"about_ca_topic_score_gemma":0.008907944,"teacher_disagreement_score":0.01394456,"about_ca_system_score_codex":0.00090483803,"about_ca_system_score_gemma":0.0016146002,"threshold_uncertainty_score":0.0737468},"labels":[],"label_agreement":null},{"id":"W4226195328","doi":"10.1109/tcsvt.2022.3169145","title":"Extending Momentum Contrast With Cross Similarity Consistency Regularization","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Regularization (linguistics); Artificial intelligence; Contrast (vision); Computer science; Similarity (geometry); Machine learning; Consistency (knowledge bases); Pattern recognition (psychology); Mathematics; Theoretical computer science; Image (mathematics)","score_opus":0.021805400150782188,"score_gpt":0.25196856426940484,"score_spread":0.23016316411862264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226195328","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":[0.06111367,0.0003142807,0.9334259,0.00052418344,0.0000821265,0.00007324558,0.00007693632,0.001465342,0.0029244483],"genre_scores_gemma":[0.8113302,0.0001570222,0.18232632,0.00057419465,0.00020293362,0.00014492324,0.00030373316,0.00037416976,0.0045865597],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910235,0.0002876573,0.00003552851,0.00021586513,0.000285305,0.00007343139],"domain_scores_gemma":[0.99800044,0.00081088155,0.0002261229,0.0004798334,0.00037391958,0.00010873246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021090745,0.00091110973,0.00086644525,0.00069604255,0.0004250116,0.001143504,0.0017543087,0.0015073437,0.0015998291],"category_scores_gemma":[0.007038273,0.0003446565,0.0004926956,0.00058625726,0.0012961735,0.002305327,0.0025261934,0.0019414776,0.00045872707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005584595,0.0004989881,0.006712423,0.00018423224,0.00022073624,0.00027739172,0.00030754344,0.43960243,0.030692691,0.07202972,0.009078565,0.43983677],"study_design_scores_gemma":[0.000020448515,0.00007056094,0.0002934848,0.0000069399543,0.000007605486,0.000045673845,0.000007341456,0.98668134,0.0024745713,0.009702805,0.0006814875,0.000007724172],"about_ca_topic_score_codex":0.0013923502,"about_ca_topic_score_gemma":0.0017355434,"teacher_disagreement_score":0.0021090745,"about_ca_system_score_codex":0.00076001376,"about_ca_system_score_gemma":0.0009829608,"threshold_uncertainty_score":0.011153996},"labels":[],"label_agreement":null},{"id":"W4226450763","doi":"10.1609/aaai.v36i7.20700","title":"Augmentation-Free Self-Supervised Learning on Graphs","year":2022,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Computer science; Cluster analysis; Graph; Hyperparameter; Artificial intelligence; Machine learning; Semantics (computer science); Node (physics); Theoretical computer science; Programming language","score_opus":0.07924101591751169,"score_gpt":0.3003966368884876,"score_spread":0.2211556209709759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226450763","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":[0.021949574,0.00020899804,0.9744057,0.00020792412,0.000028095332,0.000062152954,0.00016302417,0.0019788966,0.0009956677],"genre_scores_gemma":[0.5653653,0.00034130938,0.42778635,0.0004669495,0.00012806727,0.0003248307,0.0017004313,0.00050717714,0.0033796933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843985,0.0006678446,0.00005434814,0.0005070529,0.00023345939,0.000097460594],"domain_scores_gemma":[0.9953923,0.0022011409,0.0003688447,0.0013812734,0.00048491394,0.00017156338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018475754,0.0010558622,0.001278668,0.001553207,0.0005809294,0.0008454045,0.0026383458,0.001410619,0.0013769817],"category_scores_gemma":[0.0066021946,0.00067635026,0.0010774292,0.0011469327,0.0020583782,0.002966354,0.0022878402,0.0019467422,0.0007911006],"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":[0.00025536766,0.00022726673,0.0025457728,0.00036825865,0.00015426711,0.00026174512,0.0004278529,0.62930906,0.014406861,0.04299191,0.009905982,0.2991456],"study_design_scores_gemma":[0.000008805766,0.00002337718,0.000164669,0.000008279489,0.000005990014,0.000038500722,0.000014524929,0.9688798,0.0016215208,0.028521681,0.00070559426,0.0000071710224],"about_ca_topic_score_codex":0.001618996,"about_ca_topic_score_gemma":0.0031674437,"teacher_disagreement_score":0.0026383458,"about_ca_system_score_codex":0.00075533724,"about_ca_system_score_gemma":0.0007227034,"threshold_uncertainty_score":0.009771049},"labels":[],"label_agreement":null},{"id":"W4234494856","doi":"10.7551/mitpress/8727.003.0023","title":"3D Laser Scan Classification Using Web Data and Domain Adaptation","year":2010,"lang":"en","type":"book-chapter","venue":"The MIT Press eBooks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Naval Research; Multidisciplinary University Research Initiative; National Science Foundation","keywords":"Domain adaptation; Adaptation (eye); Computer science; Domain (mathematical analysis); Artificial intelligence; Psychology; Mathematics; Neuroscience; Classifier (UML)","score_opus":0.13969953079324685,"score_gpt":0.2860734685894576,"score_spread":0.14637393779621075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234494856","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":[0.18871725,0.0008656007,0.8000733,0.00034245377,0.00009359692,0.00014049075,0.00074229646,0.004960356,0.004064626],"genre_scores_gemma":[0.6520277,0.00056661974,0.33953488,0.00024506723,0.00007836234,0.00023155814,0.003899819,0.00021628657,0.003199747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909663,0.0002834162,0.000038600676,0.00027911962,0.00022866951,0.00007352908],"domain_scores_gemma":[0.9984686,0.00061730325,0.00010250642,0.000448038,0.00030956618,0.000054036096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013246754,0.00073204347,0.0007869097,0.0018289768,0.0003850139,0.00091349724,0.0014391869,0.001308529,0.0009348495],"category_scores_gemma":[0.0028850797,0.00043374847,0.0009940474,0.002373603,0.00071158115,0.0018432908,0.0012731027,0.0012551765,0.0013555599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002252627,0.0004080235,0.0077851084,0.00012178067,0.00013383757,0.00028344104,0.0001646483,0.2339204,0.023694923,0.0017887999,0.0064382213,0.72503567],"study_design_scores_gemma":[0.000008403676,0.00004087857,0.0028337967,0.0000102841905,0.000015062168,0.00013093647,0.00008509002,0.98110604,0.010773393,0.003379142,0.0015984679,0.00001847382],"about_ca_topic_score_codex":0.004229844,"about_ca_topic_score_gemma":0.004512943,"teacher_disagreement_score":0.004229844,"about_ca_system_score_codex":0.00064535363,"about_ca_system_score_gemma":0.000485784,"threshold_uncertainty_score":0.008410454},"labels":[],"label_agreement":null},{"id":"W4242475448","doi":"10.21203/rs.3.rs-75530/v1","title":"Domain Randomization for Neural Network Classification","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Randomization; Artificial neural network; Computer science; Domain (mathematical analysis); Artificial intelligence; Machine learning; Data mining; Mathematics; Randomized controlled trial; Medicine","score_opus":0.16793349758802423,"score_gpt":0.4110200494416484,"score_spread":0.24308655185362416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242475448","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011847393,0.0013995424,0.9845515,0.00065340596,0.0001030208,0.000040081293,0.00010373735,0.0004990175,0.00080234103],"genre_scores_gemma":[0.6053254,0.001953879,0.37876186,0.0009710425,0.000781373,0.0005158334,0.0012084588,0.00048474284,0.009997436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797565,0.0011859374,0.000070518996,0.00046075447,0.00021464634,0.00009245445],"domain_scores_gemma":[0.990164,0.00751458,0.00034601186,0.0012747883,0.00046357771,0.0002371945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004339476,0.00087387755,0.0018779489,0.00107453,0.00082175387,0.0012220442,0.0021333983,0.0022162653,0.0027151054],"category_scores_gemma":[0.018644786,0.00064176554,0.0010205834,0.0010956336,0.002049693,0.00399043,0.0020374109,0.0041357037,0.00076361425],"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":[0.0006409088,0.0003910786,0.0021035601,0.00040787464,0.0002910801,0.00009362454,0.00012539979,0.49643084,0.0061213006,0.20855358,0.016118266,0.26872256],"study_design_scores_gemma":[0.000021220922,0.000025982594,0.00017936232,0.000012881602,0.000012751673,0.000016948929,0.000006481058,0.8991442,0.00078693544,0.0991573,0.0006250985,0.000010915388],"about_ca_topic_score_codex":0.0018883987,"about_ca_topic_score_gemma":0.0016502911,"teacher_disagreement_score":0.004339476,"about_ca_system_score_codex":0.0012598219,"about_ca_system_score_gemma":0.0010903743,"threshold_uncertainty_score":0.022949576},"labels":[],"label_agreement":null},{"id":"W4247125673","doi":"10.36227/techrxiv.14565078","title":"Methods for maintenance of neural networks in continual learning scenarios","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"MNIST database; Computer science; Artificial neural network; Artificial intelligence; Novelty detection; Machine learning; Novelty; Forgetting; Representation (politics); Perspective (graphical); Correctness; Data mining; Algorithm","score_opus":0.03403171184187791,"score_gpt":0.3440928230028309,"score_spread":0.310061111160953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247125673","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00565976,0.00032311722,0.9925211,0.000120254685,0.000027337623,0.000040219704,0.000045506837,0.00072996295,0.00053263444],"genre_scores_gemma":[0.37546888,0.0008134773,0.61704206,0.00019174215,0.0002319997,0.00042642126,0.0004556076,0.0005111637,0.0048586214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803656,0.00044254138,0.00015340785,0.0006230165,0.0005799067,0.00016466249],"domain_scores_gemma":[0.98772615,0.0052562845,0.0015794033,0.0031005368,0.0019591567,0.00037844668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049409103,0.0011063789,0.0011109107,0.0024083767,0.00076749787,0.001693488,0.00498716,0.0017129302,0.0030540368],"category_scores_gemma":[0.022212537,0.0008545691,0.0012526225,0.001268263,0.0019226529,0.0047678356,0.0034307498,0.0030097824,0.00081516715],"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":[0.00017312467,0.00012192603,0.004236532,0.0002593811,0.00015334925,0.00018613764,0.00031581052,0.5416363,0.00428503,0.079823814,0.0033793994,0.36542922],"study_design_scores_gemma":[0.000006036338,0.00003345649,0.00027350395,0.0000141794235,0.000012327018,0.00004287925,0.000018179431,0.9767369,0.000792468,0.02116989,0.000890177,0.000010046321],"about_ca_topic_score_codex":0.0038677962,"about_ca_topic_score_gemma":0.0039572883,"teacher_disagreement_score":0.00498716,"about_ca_system_score_codex":0.0019448114,"about_ca_system_score_gemma":0.0013300311,"threshold_uncertainty_score":0.026130378},"labels":[],"label_agreement":null},{"id":"W4250925808","doi":"10.1109/cvprw.2009.5206576","title":"Shared Kernel Information Embedding for discriminative inference","year":2009,"lang":"en","type":"article","venue":"2009 IEEE Conference on Computer Vision and Pattern Recognition","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canadian Institute for Advanced Research","keywords":"Discriminative model; Latent variable; Inference; Embedding; Computer science; Kernel (algebra); Artificial intelligence; Machine learning; Pattern recognition (psychology); Multiple kernel learning; Latent variable model; Kernel method; Mathematics; Support vector machine","score_opus":0.06050012875337479,"score_gpt":0.32466168514029065,"score_spread":0.26416155638691585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250925808","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":[0.002957531,0.0002089617,0.9957496,0.00010646055,0.000012628591,0.000013476825,0.000098011165,0.00042122393,0.00043211388],"genre_scores_gemma":[0.4870191,0.0006590587,0.5029105,0.00037074738,0.00017264686,0.0003589583,0.0021646477,0.00048360522,0.0058607105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998014,0.0008780248,0.00008961439,0.0005100709,0.0003728192,0.00013542625],"domain_scores_gemma":[0.9962011,0.0019942583,0.00029003917,0.0010407297,0.0003579759,0.00011594239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021915575,0.0010215008,0.0016407985,0.0012856042,0.00044722442,0.0010811707,0.0020396933,0.0012550462,0.0034617258],"category_scores_gemma":[0.01047215,0.0007550803,0.001083455,0.0017875718,0.0011951756,0.0031853954,0.0022879166,0.0028698316,0.001147107],"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":[0.00020926676,0.00017297643,0.001504653,0.0002720209,0.00018224864,0.00010179704,0.0001936027,0.45757204,0.002412346,0.17942037,0.009230359,0.34872827],"study_design_scores_gemma":[0.000007837728,0.000017151444,0.0001254059,0.000009985847,0.0000067715023,0.000018362616,0.000006871254,0.92613983,0.0004066185,0.07223019,0.0010209937,0.00001003755],"about_ca_topic_score_codex":0.0030161922,"about_ca_topic_score_gemma":0.003473824,"teacher_disagreement_score":0.0034617258,"about_ca_system_score_codex":0.0012572082,"about_ca_system_score_gemma":0.0011994364,"threshold_uncertainty_score":0.011590183},"labels":[],"label_agreement":null},{"id":"W4281924738","doi":"10.1016/j.jmp.2022.102677","title":"Chaining models of serial recall can produce positional errors","year":2022,"lang":"en","type":"article","venue":"Journal of Mathematical Psychology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chaining; Computer science; Serial position effect; Recall; Associative property; Clearance; Content-addressable memory; Arithmetic; Artificial intelligence; Algorithm; Natural language processing; Cognitive psychology; Psychology; Free recall; Mathematics","score_opus":0.0634245002656686,"score_gpt":0.327111285839967,"score_spread":0.2636867855742984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281924738","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57664347,0.0003830306,0.41038412,0.0010501015,0.00016384343,0.000038684364,0.00019675429,0.0015260736,0.009613963],"genre_scores_gemma":[0.9809951,0.00012664012,0.013259008,0.00008221367,0.000031921343,0.000021541584,0.000106236774,0.00013491831,0.0052424017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995096,0.00013259228,0.000031470878,0.00014821236,0.00010835902,0.00006989901],"domain_scores_gemma":[0.984355,0.010872859,0.0009050577,0.0024764524,0.001093617,0.0002970162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016310262,0.0005690622,0.00078099984,0.00070858235,0.00048647096,0.0014509548,0.0012644628,0.0016805978,0.005604929],"category_scores_gemma":[0.018063119,0.0006211409,0.00071844284,0.000769323,0.0008115399,0.0038464428,0.00084400247,0.0021526231,0.00082047214],"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":[0.00081855967,0.0005312356,0.012056959,0.00024009593,0.00024667283,0.000805112,0.0007217032,0.5493208,0.015399453,0.29045373,0.0048383763,0.12456727],"study_design_scores_gemma":[0.00001994677,0.00003984223,0.0014214392,0.000010144611,0.00003198102,0.000107188374,0.000028458757,0.82751864,0.0033702531,0.167158,0.00026871343,0.000025394238],"about_ca_topic_score_codex":0.0022681518,"about_ca_topic_score_gemma":0.0026880852,"teacher_disagreement_score":0.005604929,"about_ca_system_score_codex":0.0007145008,"about_ca_system_score_gemma":0.0006431549,"threshold_uncertainty_score":0.01875031},"labels":[],"label_agreement":null},{"id":"W4282977123","doi":"10.1109/cvprw56347.2022.00445","title":"AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Semi-supervised learning; Computer science; Labeled data; Artificial intelligence; Training set; Entropy (arrow of time); Machine learning; Set (abstract data type); Data set; Co-training; Supervised learning; Pattern recognition (psychology); Data mining; Artificial neural network","score_opus":0.06501587995759424,"score_gpt":0.27477240171846357,"score_spread":0.20975652176086934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282977123","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":[0.016331956,0.0004481979,0.97016734,0.0002593362,0.000099549725,0.0002659905,0.00045564037,0.009826167,0.0021458147],"genre_scores_gemma":[0.24437025,0.00026594131,0.74191374,0.00081888866,0.00019027578,0.00069283135,0.004852104,0.00092810823,0.0059679286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750453,0.00091458176,0.00010594052,0.000831666,0.0005179307,0.0001253298],"domain_scores_gemma":[0.99655765,0.0012363475,0.00023464294,0.001282275,0.0005260499,0.00016300041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004038074,0.0020933035,0.001958955,0.0013499722,0.0009731232,0.0015452368,0.0044447384,0.0021686722,0.002891332],"category_scores_gemma":[0.00745574,0.0009782563,0.0013101514,0.0011023801,0.0016230651,0.0040137423,0.0037790558,0.0031569984,0.002175644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006468369,0.0006827989,0.0033145836,0.000399638,0.00037397863,0.00024610432,0.0003379753,0.19682504,0.014715903,0.015305003,0.032065935,0.7350862],"study_design_scores_gemma":[0.000038340655,0.00009703632,0.00020284372,0.000017182998,0.000013588646,0.00006776262,0.000035775713,0.98026365,0.004586354,0.011944611,0.0027152991,0.000017641252],"about_ca_topic_score_codex":0.002052739,"about_ca_topic_score_gemma":0.0042432128,"teacher_disagreement_score":0.0044447384,"about_ca_system_score_codex":0.0008676262,"about_ca_system_score_gemma":0.0016511164,"threshold_uncertainty_score":0.021355689},"labels":[],"label_agreement":null},{"id":"W4283708942","doi":"10.1109/jbhi.2022.3186882","title":"Attention-Based Dynamic Subspace Learners for Medical Image Analysis","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cluster analysis; Artificial intelligence; Interpretability; Subspace topology; Discriminative model; Embedding; Pattern recognition (psychology); Metric (unit); Machine learning; Image retrieval; Linear subspace; Image (mathematics); Mathematics","score_opus":0.020915181143407964,"score_gpt":0.33049082215767,"score_spread":0.30957564101426205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283708942","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015847825,0.00080561364,0.9803608,0.0002966127,0.000037490405,0.00006653866,0.00012544841,0.0014554663,0.0010042869],"genre_scores_gemma":[0.61170775,0.0012551186,0.3782337,0.00070866774,0.00027416533,0.00028600483,0.0009756782,0.0003682078,0.006190626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923456,0.00025151335,0.00003321932,0.00020253463,0.00019664265,0.00008150987],"domain_scores_gemma":[0.998579,0.00070804276,0.00010699424,0.00017310657,0.00033320545,0.00009959178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013886258,0.0009997402,0.0011547887,0.0016343677,0.000496385,0.00088661,0.0019126167,0.0013567402,0.0025878514],"category_scores_gemma":[0.004690632,0.00039632784,0.0014697599,0.0011886758,0.00071858737,0.0017626634,0.0019606517,0.0021172743,0.0009930572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034223724,0.0002081334,0.0022963958,0.0001646791,0.00016445325,0.00021377865,0.00029849293,0.3332237,0.013740251,0.014298603,0.0061884606,0.6288608],"study_design_scores_gemma":[0.000007222759,0.00003671523,0.00023775944,0.000009496455,0.00001190659,0.00004621338,0.00002251601,0.9857371,0.0022035441,0.010776408,0.00090095267,0.000010075989],"about_ca_topic_score_codex":0.0064443457,"about_ca_topic_score_gemma":0.006687895,"teacher_disagreement_score":0.0064443457,"about_ca_system_score_codex":0.0010040742,"about_ca_system_score_gemma":0.001022302,"threshold_uncertainty_score":0.012813628},"labels":[],"label_agreement":null},{"id":"W4283796586","doi":"10.1609/aaai.v36i7.20700","title":"Augmentation-Free Self-Supervised Learning on Graphs","year":2022,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":183,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Computer science; Cluster analysis; Graph; Artificial intelligence; Hyperparameter; Machine learning; Semantics (computer science); Node (physics); Theoretical computer science; Programming language","score_opus":0.014584815891501129,"score_gpt":0.22906406675504645,"score_spread":0.21447925086354533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283796586","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":[0.021725198,0.00023380622,0.9740771,0.00020380516,0.00003102962,0.00006807919,0.0001907632,0.0023853735,0.0010848892],"genre_scores_gemma":[0.554745,0.0003523395,0.43728605,0.0005169854,0.00013124633,0.00033859833,0.0020677769,0.0005951343,0.0039668866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857974,0.0005859817,0.00004814955,0.00047981256,0.00021144385,0.000095020696],"domain_scores_gemma":[0.9960717,0.0018644382,0.00031233617,0.0011559564,0.00043959427,0.00015602286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017225804,0.0011182587,0.0012806992,0.001515562,0.00057203,0.0008149636,0.0027447152,0.0014699725,0.0016072111],"category_scores_gemma":[0.006009284,0.00068172603,0.0010742039,0.0011339405,0.0019340531,0.0028839468,0.002230092,0.00200455,0.00094193366],"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":[0.00026065673,0.00023753013,0.0024262639,0.00039003915,0.00015454169,0.0002643008,0.00040026332,0.6082341,0.015200954,0.036283057,0.011951577,0.32419664],"study_design_scores_gemma":[0.000009307067,0.000024642524,0.000164042,0.000008740429,0.000006058673,0.00003955083,0.000014925109,0.97389877,0.0016859111,0.023376442,0.0007640952,0.0000074817763],"about_ca_topic_score_codex":0.0017591738,"about_ca_topic_score_gemma":0.0037318654,"teacher_disagreement_score":0.0027447152,"about_ca_system_score_codex":0.00075841846,"about_ca_system_score_gemma":0.000720031,"threshold_uncertainty_score":0.009109974},"labels":[],"label_agreement":null},{"id":"W4283802608","doi":"10.1609/aaai.v36i10.21292","title":"ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":93,"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 Ottawa","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment; National Natural Science Foundation of China","keywords":"Overfitting; Discriminative model; Computer science; Artificial intelligence; Natural language processing; Machine learning; Task (project management); Representation (politics); Feature learning; Regularization (linguistics); Class (philosophy); Pattern recognition (psychology); Artificial neural network","score_opus":0.12852492276051675,"score_gpt":0.32681015371591216,"score_spread":0.1982852309553954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283802608","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019939419,0.000863248,0.9752128,0.0002810063,0.000089657115,0.00014668981,0.00030401387,0.0018697664,0.0012934317],"genre_scores_gemma":[0.4663683,0.0006576645,0.52223474,0.0007535798,0.00026528168,0.00056731893,0.0024637629,0.0003965629,0.006292847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898297,0.00034227906,0.000042452717,0.00040707312,0.00016533265,0.00005997902],"domain_scores_gemma":[0.9980117,0.001157484,0.00016934752,0.00028021148,0.0002850373,0.00009616436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024662216,0.0012193513,0.0011469219,0.0019372423,0.00057434535,0.0010858942,0.0031227488,0.0018323368,0.0020666588],"category_scores_gemma":[0.0060286056,0.0004347505,0.000998158,0.0011152135,0.0011157028,0.0031536808,0.0018181344,0.0026772576,0.0009153821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006311301,0.00065416755,0.005424569,0.0004668237,0.00032581188,0.00031899463,0.000514699,0.21330766,0.018578991,0.03293346,0.011418507,0.7154252],"study_design_scores_gemma":[0.000017996204,0.00012118944,0.00043654797,0.000020020274,0.00002312222,0.00006018097,0.000031556112,0.9724299,0.0029520825,0.021564623,0.0023280971,0.0000146985],"about_ca_topic_score_codex":0.0021063597,"about_ca_topic_score_gemma":0.0039966777,"teacher_disagreement_score":0.0031227488,"about_ca_system_score_codex":0.0012289961,"about_ca_system_score_gemma":0.0007209136,"threshold_uncertainty_score":0.013042748},"labels":[],"label_agreement":null},{"id":"W4283821931","doi":"10.1609/aaai.v36i6.20642","title":"Cross-Domain Few-Shot Graph Classification","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Computer science; Encoder; Domain adaptation; Graph; Artificial intelligence; Metric (unit); Task (project management); Machine learning; Domain (mathematical analysis); Theoretical computer science; Transfer of learning; Feature (linguistics); Pattern recognition (psychology); Mathematics; Classifier (UML)","score_opus":0.1337759251364533,"score_gpt":0.32966337636611137,"score_spread":0.19588745122965806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283821931","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61111194,0.005732844,0.35527965,0.0011223041,0.0006945479,0.000494346,0.0072538736,0.011491549,0.0068189474],"genre_scores_gemma":[0.8693368,0.00039107335,0.10560686,0.0004243849,0.00012297163,0.00020189602,0.020051235,0.0005543535,0.0033104173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692506,0.0008499315,0.000121052006,0.001444006,0.0004038778,0.00025611048],"domain_scores_gemma":[0.99169415,0.00431028,0.0005265353,0.0022149875,0.00079574465,0.0004583037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037905783,0.002311346,0.001707266,0.003146815,0.00097441504,0.0016686455,0.004011596,0.0028011254,0.0016289427],"category_scores_gemma":[0.015219149,0.00040040395,0.0012972604,0.0023953177,0.0012365987,0.003845582,0.002692577,0.0029564602,0.0011405667],"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":[0.0015211821,0.002344985,0.030537343,0.0015081688,0.0010191383,0.0009293924,0.00034229748,0.46252364,0.01712869,0.0072437334,0.04740116,0.42750028],"study_design_scores_gemma":[0.00005618677,0.00032535673,0.005287711,0.000040540308,0.00007237859,0.00040052205,0.00020090576,0.96396583,0.011344408,0.0144807575,0.0037830994,0.000042305353],"about_ca_topic_score_codex":0.005443249,"about_ca_topic_score_gemma":0.007191987,"teacher_disagreement_score":0.005443249,"about_ca_system_score_codex":0.0014256428,"about_ca_system_score_gemma":0.00082633283,"threshold_uncertainty_score":0.020046711},"labels":[],"label_agreement":null},{"id":"W4283830494","doi":"10.1007/978-3-031-16440-8_31","title":"Class Impression for Data-Free Incremental Learning","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"","keywords":"Computer science; Forgetting; Margin (machine learning); Class (philosophy); Artificial intelligence; Machine learning; Cross entropy; Pattern recognition (psychology)","score_opus":0.04018152908418219,"score_gpt":0.2830577955797941,"score_spread":0.24287626649561192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283830494","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":[0.010528344,0.0013977484,0.9278185,0.00096566806,0.0009498427,0.00014287452,0.00047995438,0.005768575,0.051948514],"genre_scores_gemma":[0.47978556,0.0013120985,0.40062732,0.0007749817,0.0013520889,0.00043003936,0.0022478432,0.0027019687,0.11076813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942386,0.00008082271,0.000024598858,0.00015785989,0.00024769377,0.00006522293],"domain_scores_gemma":[0.99776757,0.0009030688,0.000042768523,0.00080573006,0.00033245626,0.00014838969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011187994,0.0005560997,0.0007341754,0.00069082715,0.0006298396,0.0017707978,0.002167165,0.001098776,0.0525484],"category_scores_gemma":[0.0077584875,0.0004113925,0.00051393325,0.00060815184,0.0007381907,0.0036167763,0.002315471,0.0022511617,0.009495822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047930746,0.00014308473,0.00031597732,0.00021374998,0.000026012254,0.000083779414,0.00008020924,0.011911295,0.008030159,0.06668655,0.052471425,0.85955846],"study_design_scores_gemma":[0.000109051995,0.00025918934,0.0012984797,0.000085308544,0.000059381302,0.00037959174,0.000080836166,0.645789,0.016105367,0.2695553,0.06621868,0.000059753052],"about_ca_topic_score_codex":0.00091897126,"about_ca_topic_score_gemma":0.0013907176,"teacher_disagreement_score":0.0525484,"about_ca_system_score_codex":0.00071343424,"about_ca_system_score_gemma":0.0004771934,"threshold_uncertainty_score":0.17579192},"labels":[],"label_agreement":null},{"id":"W4285044505","doi":"10.22215/etd/2022-15023","title":"Multi-Domain Text Classification with Adversarial Training","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Discriminative model; Adversarial system; Computer science; Artificial intelligence; Machine learning; Domain (mathematical analysis); Divergence (linguistics); Invariant (physics); Natural language processing; Pattern recognition (psychology); Mathematics","score_opus":0.04802985171484224,"score_gpt":0.28895673669787053,"score_spread":0.24092688498302828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285044505","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":[0.021543548,0.0010359435,0.9713894,0.0009067253,0.00027062488,0.00010099598,0.00020837692,0.0011775087,0.003366892],"genre_scores_gemma":[0.7083398,0.00091182283,0.2672017,0.0012762353,0.00059193675,0.00036809858,0.0016453385,0.00027426914,0.019390825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919397,0.00025464038,0.000035683086,0.00028189964,0.00013345007,0.00010033991],"domain_scores_gemma":[0.99700207,0.0020173485,0.00018783961,0.00040591627,0.000265275,0.00012162909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019696194,0.0013541694,0.0013010534,0.0007006016,0.00055981305,0.0008267124,0.0017676321,0.0018551605,0.0028676658],"category_scores_gemma":[0.005280795,0.00048812834,0.000982643,0.0008462314,0.0013138083,0.0017051575,0.0022545464,0.0028804482,0.0013949873],"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":[0.0002477917,0.00014922311,0.00081502245,0.0001260471,0.00009105226,0.00013116628,0.000082820254,0.84722066,0.0031724037,0.017051179,0.009673029,0.12123962],"study_design_scores_gemma":[0.000005523315,0.000016848415,0.00005284792,0.0000050182707,0.0000042118986,0.00001564752,0.0000048921265,0.9931693,0.000518455,0.005825853,0.0003774127,0.0000040038803],"about_ca_topic_score_codex":0.0020658781,"about_ca_topic_score_gemma":0.0023002885,"teacher_disagreement_score":0.0028676658,"about_ca_system_score_codex":0.00087752845,"about_ca_system_score_gemma":0.000715823,"threshold_uncertainty_score":0.010416448},"labels":[],"label_agreement":null},{"id":"W4285376649","doi":"10.1016/j.neucom.2022.09.031","title":"Interpretable domain adaptation using unsupervised feature selection on pre-trained source models","year":2022,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Université Laval","funders":"","keywords":"Computer science; Artificial intelligence; Feature selection; Machine learning; Adaptation (eye); Domain (mathematical analysis); Feature (linguistics); Process (computing); Estimator; Transformation (genetics); Stability (learning theory); Domain adaptation; Pattern recognition (psychology); Rank (graph theory); Data mining; Mathematics","score_opus":0.028877416416127937,"score_gpt":0.24525394974051942,"score_spread":0.2163765333243915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285376649","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017504916,0.0004055783,0.9792605,0.00015343436,0.000080916085,0.00003707973,0.00016910487,0.0015553382,0.00083300495],"genre_scores_gemma":[0.6334385,0.00070617127,0.35768119,0.00033804282,0.00018653958,0.00020034351,0.0022786197,0.00046432772,0.00470628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995314,0.0001436574,0.000021075157,0.00016583393,0.000089134286,0.000048883445],"domain_scores_gemma":[0.99881184,0.00056240323,0.000059486007,0.00028597485,0.00023801575,0.00004224844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088846975,0.0011501866,0.00097220205,0.0008708664,0.00040901478,0.00090502907,0.0012158945,0.0011327707,0.0016057687],"category_scores_gemma":[0.003635622,0.0004043003,0.0012656769,0.00086464407,0.00053094333,0.0016413571,0.0015586591,0.0024777446,0.0010032825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045920274,0.00041938957,0.0020281912,0.00024412647,0.00034725407,0.00047762375,0.00022956613,0.32750252,0.046570666,0.011811666,0.011147979,0.5987618],"study_design_scores_gemma":[0.00001026984,0.000028338416,0.00040834805,0.000011576559,0.000023529565,0.0000634012,0.000022093336,0.98402,0.0049546887,0.009605547,0.00083826465,0.000013885131],"about_ca_topic_score_codex":0.002412096,"about_ca_topic_score_gemma":0.0032710321,"teacher_disagreement_score":0.002412096,"about_ca_system_score_codex":0.0004328794,"about_ca_system_score_gemma":0.00064664474,"threshold_uncertainty_score":0.0053718686},"labels":[],"label_agreement":null},{"id":"W4285787895","doi":"10.1109/tpami.2022.3191696","title":"A Review of Generalized Zero-Shot Learning Methods","year":2022,"lang":"en","type":"review","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":396,"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 Windsor","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Categorization; Machine learning; Task (project management); Class (philosophy); Bridge (graph theory)","score_opus":0.10844614276471164,"score_gpt":0.39947218491263725,"score_spread":0.2910260421479256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285787895","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":[0.0009532664,0.930454,0.06001833,0.00090103206,0.00069447473,0.000059470498,0.00020532656,0.00022436076,0.006489683],"genre_scores_gemma":[0.014310015,0.9346061,0.04310111,0.0009188788,0.0017402394,0.00015600082,0.00081608474,0.00010465633,0.004246969],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99914503,0.00016428571,0.00010170552,0.00025462912,0.00028895997,0.0000453477],"domain_scores_gemma":[0.9983606,0.0010055138,0.00008233839,0.000091656206,0.00041278408,0.000047194026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016672584,0.00152592,0.0019818188,0.0030717477,0.00048546417,0.0017844508,0.0025170448,0.0017024502,0.003912671],"category_scores_gemma":[0.0041162325,0.0006846824,0.0012193175,0.0044695744,0.00078884524,0.0027552892,0.0010770772,0.0018266123,0.003103384],"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":[0.000058706177,0.00010226458,0.000587799,0.0068857153,0.00017030326,0.0000904047,0.00010475452,0.0070496006,0.00086003344,0.01839226,0.029900907,0.9357972],"study_design_scores_gemma":[0.000037731414,0.000319655,0.0028375268,0.0049153888,0.0003943478,0.0014912074,0.00021264411,0.04485432,0.0025282777,0.056751244,0.88544756,0.00021004729],"about_ca_topic_score_codex":0.004176221,"about_ca_topic_score_gemma":0.0028149623,"teacher_disagreement_score":0.004176221,"about_ca_system_score_codex":0.0010015506,"about_ca_system_score_gemma":0.0019114247,"threshold_uncertainty_score":0.01308912},"labels":[],"label_agreement":null},{"id":"W4285816564","doi":"10.1109/i2ct54291.2022.9825204","title":"Quantitative Analysis of Transfer Learning for Image Classification","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 7th International conference for Convergence in Technology (I2CT)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Transfer of learning; Computer science; Artificial intelligence; Machine learning; Task (project management); Inductive transfer; Image (mathematics); Contextual image classification; Robot learning; Engineering","score_opus":0.06294828939284272,"score_gpt":0.3358222071443129,"score_spread":0.2728739177514702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285816564","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":[0.028682403,0.0018105826,0.96195054,0.000786701,0.000084872634,0.0001073255,0.000113617956,0.0005229016,0.005941092],"genre_scores_gemma":[0.83022165,0.0012137892,0.16254736,0.00029783536,0.00030557366,0.00028022783,0.00034285872,0.00026955208,0.0045210114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965055,0.0012726629,0.00012463315,0.0003991107,0.0014998937,0.00019826835],"domain_scores_gemma":[0.9744246,0.018331971,0.0013743506,0.0022789533,0.0032182562,0.00037199858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009330191,0.0007478095,0.0007256797,0.0027593537,0.0005859944,0.0016831077,0.0016319839,0.0015459255,0.0041627027],"category_scores_gemma":[0.046373785,0.00026416781,0.0007122407,0.0017026415,0.0025416636,0.0051697907,0.0019075949,0.0019290963,0.0006053587],"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":[0.00029079846,0.00019108705,0.004181757,0.00080467266,0.00017222109,0.00018259243,0.0003984114,0.45360962,0.013848526,0.22400837,0.0032131365,0.29909873],"study_design_scores_gemma":[0.000006375088,0.00011845897,0.0018647528,0.00003778187,0.000018417239,0.00009791342,0.00006028406,0.8849438,0.0049354024,0.106406204,0.0014877382,0.00002273239],"about_ca_topic_score_codex":0.0010002004,"about_ca_topic_score_gemma":0.00037317452,"teacher_disagreement_score":0.009330191,"about_ca_system_score_codex":0.0022553778,"about_ca_system_score_gemma":0.00080233894,"threshold_uncertainty_score":0.049343407},"labels":[],"label_agreement":null},{"id":"W4286902830","doi":"10.48550/arxiv.2110.08232","title":"Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Prediction","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Compute Canada","keywords":"FLOPS; Hyperparameter; Computer science; Regularization (linguistics); Artificial intelligence; Reduction (mathematics); Machine learning; Pruning; Artificial neural network; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.03543727667811387,"score_gpt":0.17174860766228833,"score_spread":0.13631133098417447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286902830","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":[0.050097086,0.0004991561,0.94214094,0.00045885326,0.00008503597,0.000073380645,0.00011119864,0.0039815516,0.0025527754],"genre_scores_gemma":[0.7142951,0.00022069438,0.2749574,0.00068179634,0.0001563342,0.00017680638,0.0005410487,0.0006561994,0.008314602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942386,0.00009933015,0.000022572836,0.00019246142,0.00016892228,0.00009293114],"domain_scores_gemma":[0.99886936,0.00035752327,0.00012262759,0.00034755206,0.0002144026,0.000088492874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085399894,0.0013005399,0.0014617856,0.00068064744,0.0007028161,0.0009508314,0.0035350332,0.0019056022,0.001992124],"category_scores_gemma":[0.0025568279,0.00084749307,0.00084162725,0.0006035371,0.00096861017,0.0024984693,0.0018400642,0.0018692715,0.00097022194],"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":[0.00036407998,0.000348477,0.0024756305,0.000097630924,0.00022184901,0.00033892583,0.0003079616,0.48432887,0.02476279,0.011082623,0.011773743,0.4638974],"study_design_scores_gemma":[0.000008311336,0.000026672906,0.00013062275,0.0000046882706,0.000011751653,0.000033984466,0.000009566745,0.9923524,0.0025517866,0.004324338,0.0005406356,0.000005273397],"about_ca_topic_score_codex":0.004383148,"about_ca_topic_score_gemma":0.0074356403,"teacher_disagreement_score":0.004383148,"about_ca_system_score_codex":0.0008624015,"about_ca_system_score_gemma":0.001177233,"threshold_uncertainty_score":0.008715272},"labels":[],"label_agreement":null},{"id":"W4287206427","doi":"10.48550/arxiv.2104.09399","title":"TREC Deep Learning Track: Reusable Test Collections in the Large Data\\n Regime","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Microsoft (Canada)","funders":"","keywords":"Overfitting; Computer science; Reuse; Test set; Set (abstract data type); Deep learning; Track (disk drive); Data set; Information retrieval; Test data; Artificial intelligence; Training set; Artificial neural network; Test (biology); Selection (genetic algorithm); Data mining; Programming language","score_opus":0.09775210036667754,"score_gpt":0.2110734881241472,"score_spread":0.11332138775746965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287206427","genre_codex":"dataset","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":[0.06155149,0.007933231,0.1866424,0.021697914,0.0154409185,0.0047205817,0.49780563,0.07014174,0.13406608],"genre_scores_gemma":[0.10024453,0.0014649101,0.092437156,0.0038401587,0.0014740031,0.0035566012,0.74279475,0.008926866,0.04526097],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9808286,0.0059265755,0.0015553156,0.0021033366,0.008183085,0.0014031788],"domain_scores_gemma":[0.8851414,0.031723272,0.002209475,0.03801581,0.035900377,0.0070095942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032066636,0.002217854,0.0019972879,0.007429505,0.0032247214,0.0065559247,0.006812853,0.0030472598,0.027019277],"category_scores_gemma":[0.095470294,0.0010538413,0.0020773674,0.00808324,0.0022139088,0.0064232163,0.0074784146,0.006673403,0.024251781],"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":[0.00032731125,0.00047474768,0.0024713809,0.00044698393,0.00017936903,0.00015000657,0.00012944551,0.004236732,0.0022266782,0.0036828942,0.90408504,0.081589356],"study_design_scores_gemma":[0.0010157805,0.0011039398,0.022229442,0.0005284636,0.00022100052,0.0008154953,0.0007388868,0.07781917,0.030842643,0.023138646,0.84113216,0.00041435263],"about_ca_topic_score_codex":0.046514064,"about_ca_topic_score_gemma":0.08431774,"teacher_disagreement_score":0.046514064,"about_ca_system_score_codex":0.005075205,"about_ca_system_score_gemma":0.0071413084,"threshold_uncertainty_score":0.16958654},"labels":[],"label_agreement":null},{"id":"W4287333308","doi":"10.48550/arxiv.2102.01586","title":"U-LanD: Uncertainty-Driven Video Landmark Detection","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Landmark; Artificial intelligence; Margin (machine learning); Key (lock); Computer vision; Frame (networking); Overhead (engineering); Bayesian probability; Bayesian inference; Pattern recognition (psychology); Machine learning","score_opus":0.054134725975866216,"score_gpt":0.1857636321285387,"score_spread":0.13162890615267248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287333308","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008652774,0.00064050505,0.9854835,0.0002403915,0.000060952658,0.00008271886,0.0003887218,0.0036814518,0.00076891715],"genre_scores_gemma":[0.35523325,0.00063027476,0.6343637,0.0006341413,0.00028528957,0.00026972988,0.0029295871,0.0007853364,0.004868703],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985707,0.00041840956,0.000051288353,0.00048695478,0.00033011817,0.00014247607],"domain_scores_gemma":[0.99812716,0.00095926336,0.00019275195,0.00033333877,0.0002617412,0.00012568243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021823167,0.0012987971,0.0018770358,0.001661965,0.00046023304,0.0014197445,0.0030963612,0.0017337153,0.002411495],"category_scores_gemma":[0.007507205,0.0007227368,0.0010704043,0.001276285,0.0011492984,0.0021203856,0.0029369185,0.002211322,0.0016063183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007395635,0.00020249153,0.0037449866,0.00030137703,0.00021860296,0.00027731183,0.00021333141,0.2848223,0.012910535,0.017211527,0.019098371,0.6602596],"study_design_scores_gemma":[0.000021832835,0.000064973145,0.0002997568,0.000014095612,0.000011313533,0.00007792652,0.000021853917,0.9836427,0.0031764803,0.010611194,0.0020417653,0.000016154627],"about_ca_topic_score_codex":0.0074214493,"about_ca_topic_score_gemma":0.008869996,"teacher_disagreement_score":0.0074214493,"about_ca_system_score_codex":0.00089690636,"about_ca_system_score_gemma":0.0013853388,"threshold_uncertainty_score":0.01475656},"labels":[],"label_agreement":null},{"id":"W4287549823","doi":"10.48550/arxiv.2012.12477","title":"IIRC: Incremental Implicitly-Refined Classification","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Benchmark (surveying); Computer science; Task (project management); Class (philosophy); Granularity; Artificial intelligence; Machine learning; Programming language; Engineering; Systems engineering","score_opus":0.16176755457903874,"score_gpt":0.21039649343666547,"score_spread":0.04862893885762673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287549823","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":[0.0836673,0.003466502,0.86051893,0.0025025932,0.0008204333,0.00083962944,0.0048112725,0.033455584,0.009917715],"genre_scores_gemma":[0.5024319,0.0005842594,0.45892784,0.002401382,0.00045138382,0.00077735126,0.020654825,0.0015502683,0.012220825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961778,0.000877805,0.00017937475,0.0013653459,0.0009366289,0.00046316846],"domain_scores_gemma":[0.9912492,0.0028391771,0.00040723552,0.003375403,0.0016772951,0.00045173886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037583567,0.002991546,0.0022773047,0.0016242502,0.0011171673,0.0021223833,0.01012956,0.004161309,0.005369679],"category_scores_gemma":[0.014425999,0.0009681544,0.0019671305,0.0015645339,0.0015443475,0.0070357206,0.0039889026,0.006742658,0.0029268155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013841086,0.0010425685,0.009875617,0.0006118017,0.00035510765,0.0004865513,0.0005280208,0.26253816,0.010010633,0.01661159,0.08536906,0.61118686],"study_design_scores_gemma":[0.00007242039,0.00016988412,0.0006819555,0.000045310964,0.000042738746,0.00014177628,0.00004571166,0.97561103,0.0038832405,0.0124951685,0.006767149,0.000043689728],"about_ca_topic_score_codex":0.01899373,"about_ca_topic_score_gemma":0.023809647,"teacher_disagreement_score":0.01899373,"about_ca_system_score_codex":0.0024728372,"about_ca_system_score_gemma":0.0034229823,"threshold_uncertainty_score":0.037766337},"labels":[],"label_agreement":null},{"id":"W4287644884","doi":"10.48550/arxiv.2010.03533","title":"Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Initialization; Computer science; Generalization; Pruning; Artificial intelligence; Inference; Artificial neural network; Flow (mathematics); Machine learning; Balanced flow; Sparse matrix; Lottery; Mathematics","score_opus":0.08419623647118654,"score_gpt":0.17810863978885821,"score_spread":0.09391240331767167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287644884","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":[0.33713838,0.003595953,0.5964072,0.020187292,0.0009124036,0.00010267918,0.0007484855,0.0007414132,0.04016617],"genre_scores_gemma":[0.9478368,0.0006797623,0.029688649,0.0005578621,0.0002166359,0.00006422748,0.00022629299,0.00020628571,0.020523535],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993704,0.00032152503,0.000022705637,0.0001234141,0.00007892433,0.0000830781],"domain_scores_gemma":[0.9962817,0.002707549,0.00029355206,0.00020460215,0.00022800021,0.00028456195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00208434,0.00040395066,0.0010741702,0.00065735506,0.0008102924,0.0023523855,0.0010682548,0.0020661955,0.010210881],"category_scores_gemma":[0.018568762,0.00049216056,0.00058402837,0.00070081867,0.0018189204,0.005157585,0.0010441144,0.002797775,0.00070977624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004251545,0.00012405582,0.0021843258,0.00013022579,0.000105358034,0.000084994164,0.00027946217,0.13967454,0.0011637923,0.76990426,0.019653318,0.06627049],"study_design_scores_gemma":[0.000027457249,0.000029308087,0.00058971095,0.000019078576,0.000016971231,0.000025533,0.000083891005,0.33328527,0.00033807266,0.6638262,0.0017401443,0.000018412702],"about_ca_topic_score_codex":0.0030655293,"about_ca_topic_score_gemma":0.0033465615,"teacher_disagreement_score":0.010210881,"about_ca_system_score_codex":0.001447448,"about_ca_system_score_gemma":0.00077532785,"threshold_uncertainty_score":0.034158766},"labels":[],"label_agreement":null},{"id":"W4287756009","doi":"10.48550/arxiv.2006.12245","title":"Enhancing Few-Shot Image Classification with Unlabelled Examples","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; Canada Research Chairs; Compute Canada","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Machine learning; Mahalanobis distance; Code (set theory); Feature (linguistics); Cluster analysis; Shot (pellet); Image (mathematics); Set (abstract data type); Contextual image classification; Test set; Training set; Data mining","score_opus":0.14427498622362486,"score_gpt":0.20926722258569208,"score_spread":0.06499223636206722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287756009","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":[0.07851456,0.0011103053,0.9077883,0.0003718477,0.00022197783,0.0002303731,0.000535862,0.0077542155,0.003472586],"genre_scores_gemma":[0.54682606,0.0003599125,0.44154954,0.000774788,0.00016185903,0.0003017541,0.003567054,0.000621341,0.005837677],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814165,0.00040051775,0.00007581535,0.00078719377,0.00044180654,0.00015301749],"domain_scores_gemma":[0.9963981,0.0013430561,0.0002164122,0.00102298,0.0008204674,0.00019900201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021958526,0.0015855666,0.0019143469,0.0016676506,0.00064734864,0.0017860366,0.004543001,0.0025055707,0.0022519468],"category_scores_gemma":[0.008760522,0.00053492555,0.0011518691,0.0011537244,0.0011861388,0.004454563,0.0026383647,0.0029370484,0.0020130246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062183983,0.0012834538,0.0058080503,0.0005877153,0.00024730948,0.00033734157,0.00047526398,0.13018598,0.058463372,0.005768598,0.014200897,0.7820203],"study_design_scores_gemma":[0.000018982932,0.00017380943,0.00076629943,0.000031925716,0.000030815012,0.00015972163,0.00008997392,0.9635265,0.02330374,0.009752504,0.0021166562,0.000029074852],"about_ca_topic_score_codex":0.0025954489,"about_ca_topic_score_gemma":0.0042312862,"teacher_disagreement_score":0.004543001,"about_ca_system_score_codex":0.0010403314,"about_ca_system_score_gemma":0.00077803596,"threshold_uncertainty_score":0.011612892},"labels":[],"label_agreement":null},{"id":"W4287777770","doi":"10.48550/arxiv.2005.07839","title":"Joint Progressive Knowledge Distillation and Unsupervised Domain\\n Adaptation","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Genetec (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Domain adaptation; Domain (mathematical analysis); Divergence (linguistics); Machine learning; Set (abstract data type); Pattern recognition (psychology); Data mining; Classifier (UML)","score_opus":0.13496011554013893,"score_gpt":0.20463509510593503,"score_spread":0.0696749795657961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287777770","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":[0.029170474,0.00049815624,0.9643158,0.00029317007,0.000066325316,0.000075020806,0.00014931381,0.0021454622,0.0032862783],"genre_scores_gemma":[0.7318114,0.0004874914,0.25751057,0.00058763166,0.0001458433,0.00022073468,0.00090026035,0.00029016906,0.008045888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994911,0.00011248289,0.000029997684,0.0001967402,0.00009367525,0.00007597303],"domain_scores_gemma":[0.99887246,0.00046245873,0.00008868688,0.00038344882,0.00014470468,0.000048214035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083307276,0.0012070213,0.0009640377,0.0005923505,0.0003407339,0.0008443628,0.001958185,0.0012361676,0.0022074454],"category_scores_gemma":[0.0031688877,0.00048052322,0.00086536113,0.000812247,0.0012537553,0.0023318788,0.002683505,0.0021066281,0.00083273265],"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":[0.00015629292,0.00020362242,0.0011151567,0.00015353649,0.00010530713,0.00017142517,0.00019059496,0.5647065,0.011372675,0.02057879,0.0044977386,0.3967484],"study_design_scores_gemma":[0.000007891024,0.000027831773,0.00012424102,0.000007477166,0.000010404268,0.000034891855,0.000015845866,0.9872126,0.00366923,0.007519013,0.0013609955,0.000009548841],"about_ca_topic_score_codex":0.0051864204,"about_ca_topic_score_gemma":0.00810961,"teacher_disagreement_score":0.0051864204,"about_ca_system_score_codex":0.00068977196,"about_ca_system_score_gemma":0.0013657627,"threshold_uncertainty_score":0.010312438},"labels":[],"label_agreement":null},{"id":"W4287887120","doi":"10.18653/v1/2022.deeplo-1.3","title":"IDANI: Inference-time Domain Adaptation via Neuron-level Interventions","year":2022,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Azrieli Foundation","keywords":"Inference; Computer science; Domain adaptation; Counterfactual thinking; Artificial intelligence; Domain (mathematical analysis); Representation (politics); Task (project management); Adaptation (eye); Test data; Machine learning; Synthetic data; Psychology; Mathematics","score_opus":0.0744703835987741,"score_gpt":0.2903648758199495,"score_spread":0.2158944922211754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287887120","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":[0.016867798,0.0006098054,0.97344244,0.0002280744,0.00017254257,0.000091139365,0.00014834164,0.006424753,0.0020151783],"genre_scores_gemma":[0.5591871,0.00045951235,0.42973813,0.0009904575,0.00015382124,0.0003296538,0.001131228,0.00075680227,0.0072532143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994837,0.00011301248,0.000026706515,0.00021196633,0.00010813581,0.00005646188],"domain_scores_gemma":[0.9988367,0.00047594737,0.00006434334,0.00038804495,0.00015354005,0.00008157103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010850887,0.0010950681,0.0011354943,0.0005112769,0.00037291844,0.00087116804,0.0028846667,0.0014382079,0.002882628],"category_scores_gemma":[0.004579202,0.0004963077,0.0008973449,0.00053452497,0.000771777,0.0017957063,0.002559569,0.004024645,0.001434473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043270586,0.0004633258,0.002756568,0.00026753792,0.0002627376,0.00020930615,0.00025107613,0.33668646,0.03561214,0.011279502,0.010790779,0.6009879],"study_design_scores_gemma":[0.00002189331,0.000055628105,0.0002688762,0.00000984563,0.000018187895,0.000052505842,0.000019264879,0.98392636,0.0065774363,0.0073060947,0.0017294756,0.00001446695],"about_ca_topic_score_codex":0.0036596868,"about_ca_topic_score_gemma":0.004818763,"teacher_disagreement_score":0.0036596868,"about_ca_system_score_codex":0.00074531836,"about_ca_system_score_gemma":0.0010987734,"threshold_uncertainty_score":0.009643376},"labels":[],"label_agreement":null},{"id":"W4288375829","doi":"","title":"On Direct Distribution Matching for Adapting Segmentation Networks","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Segmentation; Discriminator; Computer science; Artificial intelligence; Matching (statistics); Adversarial system; Context (archaeology); Stability (learning theory); Pattern recognition (psychology); Kernel (algebra); Scale-space segmentation; Image segmentation; Minification; Computer vision; Machine learning; Mathematics; Geography","score_opus":0.021835910152214727,"score_gpt":0.24652317157689896,"score_spread":0.22468726142468423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288375829","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011994815,0.0004230522,0.9850684,0.00018403829,0.000053681586,0.000054605633,0.00008559286,0.0008678369,0.0012679631],"genre_scores_gemma":[0.466138,0.0011066624,0.51375467,0.0008936405,0.00045583004,0.00028615384,0.0014321125,0.001191778,0.01474116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894303,0.0003277054,0.00004615619,0.00037851848,0.000188883,0.00011566476],"domain_scores_gemma":[0.9959454,0.0027146551,0.00015935571,0.00062642235,0.0003878688,0.00016630405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002079198,0.0012786807,0.0023003302,0.0016595039,0.000776505,0.0011685787,0.003056751,0.00273883,0.004982293],"category_scores_gemma":[0.009304451,0.001009232,0.001003993,0.0018567134,0.001406586,0.00299157,0.0030268736,0.002123433,0.001585048],"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":[0.0003024377,0.00015995759,0.00077243434,0.00012245323,0.00010440451,0.00010661348,0.00013435613,0.6003367,0.009458371,0.01476119,0.004711773,0.3690293],"study_design_scores_gemma":[0.000008055601,0.000015705937,0.000109033186,0.0000045758393,0.000006623652,0.000022721895,0.000009374579,0.98940045,0.00085631496,0.009133134,0.00042911156,0.0000048227034],"about_ca_topic_score_codex":0.009483939,"about_ca_topic_score_gemma":0.008528684,"teacher_disagreement_score":0.009483939,"about_ca_system_score_codex":0.0012429062,"about_ca_system_score_gemma":0.0010563985,"threshold_uncertainty_score":0.01885748},"labels":[],"label_agreement":null},{"id":"W4292387707","doi":"10.1109/coins54846.2022.9854982","title":"Encoding High-Level Features: An Approach To Robust Transfer Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Autoencoder; Artificial intelligence; Transfer of learning; Pattern recognition (psychology); Convolutional neural network; ENCODE; Robustness (evolution); Encoding (memory); Contextual image classification; Feature (linguistics); Feature learning; Deep learning; Machine learning; Image (mathematics)","score_opus":0.05953891380903345,"score_gpt":0.24465621740340307,"score_spread":0.18511730359436962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292387707","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":[0.0042757494,0.00017248253,0.9937331,0.00011963021,0.000032006385,0.000034406185,0.000031967706,0.0005860693,0.0010145196],"genre_scores_gemma":[0.5235208,0.000670148,0.46326435,0.00043209392,0.0002764685,0.00031558765,0.00040460585,0.00043501076,0.010680988],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992434,0.00016987928,0.00004075551,0.00021517248,0.00025443255,0.000076368],"domain_scores_gemma":[0.99878377,0.00029373635,0.00013337939,0.00054089585,0.00020828968,0.000039958522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001544794,0.0009292331,0.00085733476,0.0008026009,0.0003737997,0.0009477807,0.002332235,0.0013815825,0.0025736548],"category_scores_gemma":[0.004673495,0.0004159991,0.0009830175,0.0008355157,0.0011203202,0.002008901,0.0024287885,0.0024914488,0.0013350785],"study_design_candidate":"bench_or_experimental","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":[0.0001753845,0.00020532828,0.00082243484,0.00015919066,0.00016389435,0.0002008296,0.00017225028,0.37407047,0.026003866,0.055933863,0.0039016688,0.5381909],"study_design_scores_gemma":[0.00000831195,0.00008722599,0.00020322665,0.000013196273,0.00001760254,0.00008081023,0.000016806509,0.95448667,0.0077412785,0.035069548,0.0022611942,0.000014199736],"about_ca_topic_score_codex":0.00148689,"about_ca_topic_score_gemma":0.0009956316,"teacher_disagreement_score":0.0025736548,"about_ca_system_score_codex":0.00084333715,"about_ca_system_score_gemma":0.00069740013,"threshold_uncertainty_score":0.008609772},"labels":[],"label_agreement":null},{"id":"W4294969213","doi":"10.1609/aaai.v35i11.17171","title":"`Less Than One'-Shot Learning: Learning N Classes From M &lt; N Samples","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Shot (pellet); Class (philosophy); Artificial intelligence; Task (project management); Computer science; Machine learning; Training (meteorology); One shot; Artificial neural network; Engineering","score_opus":0.20659696630416155,"score_gpt":0.315659843378008,"score_spread":0.10906287707384646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294969213","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04144813,0.0005326082,0.9527136,0.000996668,0.00015741959,0.00029459817,0.0002596044,0.0019504128,0.0016469852],"genre_scores_gemma":[0.53624254,0.00036966056,0.4515408,0.0016570514,0.0003491991,0.0004890393,0.0016647002,0.0002863924,0.0074005583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985952,0.00037795637,0.00008200067,0.0005938585,0.00021047879,0.00014055881],"domain_scores_gemma":[0.9966299,0.0016283006,0.00022260868,0.00091769145,0.00030646377,0.00029502253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002658575,0.0015439527,0.002812312,0.0006768897,0.00112813,0.0014096482,0.0052950466,0.003703924,0.0038779397],"category_scores_gemma":[0.007549838,0.00091489305,0.0012795519,0.00094811304,0.002288158,0.0061808373,0.0036857298,0.0038371924,0.0010686518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012729283,0.0011730625,0.00528868,0.0006323374,0.00041134952,0.00045046821,0.0006406671,0.20199408,0.015722016,0.023534877,0.018854385,0.73002523],"study_design_scores_gemma":[0.00005948194,0.00022861548,0.00079328765,0.000027123746,0.000045255612,0.00017708779,0.00009465005,0.9408795,0.0063541587,0.04919506,0.002100868,0.000044872093],"about_ca_topic_score_codex":0.006208773,"about_ca_topic_score_gemma":0.0074389274,"teacher_disagreement_score":0.006208773,"about_ca_system_score_codex":0.0013089561,"about_ca_system_score_gemma":0.0014412071,"threshold_uncertainty_score":0.01406008},"labels":[],"label_agreement":null},{"id":"W4295469822","doi":"10.1109/embc48229.2022.9871402","title":"Edge-preserving Image Synthesis for Unsupervised Domain Adaptation in Medical Image Segmentation","year":2022,"lang":"en","type":"article","venue":"2022 44th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Segmentation; Artificial intelligence; Domain (mathematical analysis); Adaptation (eye); Domain adaptation; Relevance (law); Pattern recognition (psychology); Enhanced Data Rates for GSM Evolution; Image (mathematics); Image segmentation; Labeled data; Process (computing); Transformation (genetics); Computer vision; Mathematics","score_opus":0.039408388136916356,"score_gpt":0.30707823864973854,"score_spread":0.2676698505128222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295469822","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":[0.015420665,0.00014254679,0.9831383,0.00007651454,0.000017405562,0.00003114246,0.00003310469,0.00061661337,0.00052377034],"genre_scores_gemma":[0.32046363,0.0004062248,0.67477834,0.00029028798,0.00006268187,0.0001195436,0.00044032867,0.00031077533,0.0031281828],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997228,0.00006724647,0.000013370999,0.00008862036,0.00008184164,0.000026116108],"domain_scores_gemma":[0.9995516,0.00016674299,0.00004625179,0.00012836808,0.00008095286,0.000026162983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085601525,0.00042571957,0.00051392097,0.000749939,0.00021436755,0.0005264324,0.00075720024,0.00066705095,0.0011033603],"category_scores_gemma":[0.0018191801,0.00028260765,0.0005972192,0.0006654109,0.0006027828,0.000811876,0.00088038057,0.00095544884,0.00063534535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027967646,0.00015739987,0.0014177411,0.0001490623,0.00008962381,0.000119078126,0.00018912696,0.20347767,0.14050928,0.009581952,0.0027851493,0.64124423],"study_design_scores_gemma":[0.0000125260085,0.000073344105,0.00080771174,0.000009233106,0.000016388023,0.00018139758,0.00002339216,0.94606906,0.042855438,0.007279597,0.0026542295,0.000017705353],"about_ca_topic_score_codex":0.00093185034,"about_ca_topic_score_gemma":0.0014116002,"teacher_disagreement_score":0.0011033603,"about_ca_system_score_codex":0.00036037527,"about_ca_system_score_gemma":0.00056739897,"threshold_uncertainty_score":0.004527092},"labels":[],"label_agreement":null},{"id":"W4296199856","doi":"10.1016/j.media.2022.102617","title":"Source-free domain adaptation for image segmentation","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Hôpital Notre-Dame","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Artificial intelligence; Computer vision; Computer science; Domain adaptation; Adaptation (eye); Segmentation; Image (mathematics); Domain (mathematical analysis); Image segmentation; Pattern recognition (psychology); Mathematics; Psychology","score_opus":0.013353712860726735,"score_gpt":0.2706699761746786,"score_spread":0.25731626331395185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296199856","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":[0.005484347,0.00043172302,0.9922753,0.00009712452,0.000037403224,0.000026653672,0.00008750419,0.0010846328,0.00047526293],"genre_scores_gemma":[0.31937772,0.0013176467,0.66976124,0.00047116683,0.00018710237,0.00019845463,0.0016518885,0.0010497058,0.0059851008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995347,0.00014185441,0.000022446919,0.00015110608,0.000100768906,0.000049166847],"domain_scores_gemma":[0.99896324,0.00047670043,0.00005354131,0.00023574225,0.00022026381,0.00005040213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013207739,0.0008842238,0.0013980443,0.0010537374,0.000407956,0.000831745,0.0015719755,0.0016476365,0.0021417998],"category_scores_gemma":[0.0034105093,0.0005738788,0.0011941026,0.001055383,0.0007864839,0.0015398785,0.0018621472,0.0019432164,0.0013920141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046862423,0.00024307476,0.0008154003,0.00042806647,0.00026147647,0.0001814919,0.00015811143,0.29731286,0.06120104,0.013565496,0.009980793,0.6153835],"study_design_scores_gemma":[0.000010997093,0.000031023064,0.00034345425,0.000012232721,0.000022569777,0.00010532164,0.000015556232,0.975749,0.009856481,0.012317118,0.0015218287,0.000014388453],"about_ca_topic_score_codex":0.0026446346,"about_ca_topic_score_gemma":0.0029076764,"teacher_disagreement_score":0.0026446346,"about_ca_system_score_codex":0.00053070043,"about_ca_system_score_gemma":0.0008695169,"threshold_uncertainty_score":0.0071650743},"labels":[],"label_agreement":null},{"id":"W4296218625","doi":"10.1016/j.compeleceng.2022.108326","title":"TPSN: Transformer-based multi-Prototype Search Network for few-shot semantic segmentation","year":2022,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Segmentation; Artificial intelligence; Encoder; Pascal (unit); Consistency (knowledge bases); Transformer; Feature extraction; Pattern recognition (psychology); Computer vision","score_opus":0.031334001579008196,"score_gpt":0.2634984340031686,"score_spread":0.23216443242416038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296218625","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":[0.018608764,0.0010787327,0.96537447,0.00022708517,0.00020871442,0.00016558259,0.001139548,0.010889849,0.0023072285],"genre_scores_gemma":[0.46379066,0.0009550821,0.51366395,0.0006832903,0.0001826294,0.00036062507,0.008109095,0.0011479729,0.011106779],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999488,0.000067704095,0.000027603466,0.00025001913,0.00009805346,0.00006853851],"domain_scores_gemma":[0.999363,0.00022274091,0.00003664569,0.00013382763,0.00018254374,0.000061256964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086937245,0.0013577789,0.0018132378,0.001564076,0.0007078461,0.0010011289,0.003602249,0.0022382662,0.0071233506],"category_scores_gemma":[0.0025175894,0.0007776735,0.0010687826,0.001845727,0.000636425,0.0027325868,0.0019225221,0.002075246,0.0026981118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079439644,0.00033708828,0.0008789537,0.00029493897,0.00019705867,0.0002231688,0.00012043487,0.07732199,0.023906473,0.0071203094,0.02438848,0.8644168],"study_design_scores_gemma":[0.00002062271,0.00005942074,0.00017302476,0.000011482416,0.000029714607,0.00007749231,0.00002276734,0.98635745,0.0052885325,0.00646465,0.0014816794,0.000013214857],"about_ca_topic_score_codex":0.011268979,"about_ca_topic_score_gemma":0.016360352,"teacher_disagreement_score":0.011268979,"about_ca_system_score_codex":0.0010708577,"about_ca_system_score_gemma":0.001424964,"threshold_uncertainty_score":0.023829997},"labels":[],"label_agreement":null},{"id":"W4296962792","doi":"10.1007/978-3-031-16760-7_2","title":"Partial Annotations for the Segmentation of Large Structures with Low Annotation Cost","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"","keywords":"Dice; Annotation; Computer science; Segmentation; Artificial intelligence; Task (project management); Pattern recognition (psychology); Sørensen–Dice coefficient; Image segmentation; Mathematics; Statistics","score_opus":0.018462691204214482,"score_gpt":0.273551194052744,"score_spread":0.25508850284852946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296962792","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007737921,0.00090771087,0.9790638,0.00016367373,0.0001304443,0.000102829705,0.0012233353,0.007942477,0.0027276813],"genre_scores_gemma":[0.100879274,0.0011003604,0.8735017,0.00029693925,0.00022616451,0.0002702331,0.010125353,0.004165493,0.009434541],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871325,0.00024895722,0.00006931388,0.0005407225,0.0002849718,0.00014273796],"domain_scores_gemma":[0.996626,0.0012521165,0.00012093147,0.001344581,0.00048379716,0.00017254688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012034951,0.002226714,0.002149697,0.0022719651,0.0012513804,0.0019745466,0.0031537844,0.0030590235,0.015472295],"category_scores_gemma":[0.0049320585,0.0014791046,0.001675309,0.0029546574,0.0010877226,0.00416159,0.0032348684,0.0029085544,0.010004994],"study_design_candidate":"bench_or_experimental","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":[0.0006389871,0.00015711536,0.0004278294,0.0006997881,0.0001560176,0.00033616138,0.00020362917,0.038194165,0.08061878,0.012096802,0.028632391,0.8378384],"study_design_scores_gemma":[0.00007257367,0.0002058933,0.0012959839,0.00012872058,0.00015880853,0.00077140116,0.00019255129,0.8151037,0.06497409,0.08538415,0.031622566,0.00008956038],"about_ca_topic_score_codex":0.0035965368,"about_ca_topic_score_gemma":0.00894773,"teacher_disagreement_score":0.015472295,"about_ca_system_score_codex":0.0006958824,"about_ca_system_score_gemma":0.0016143243,"threshold_uncertainty_score":0.05175996},"labels":[],"label_agreement":null},{"id":"W4296998418","doi":"10.3389/fninf.2022.919779","title":"A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hotchkiss Brain Institute; York University; McMaster University; St. Michael's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Segmentation; Computer science; Benchmark (surveying); Artificial intelligence; Image segmentation; Deep learning; Pattern recognition (psychology); Magnetic resonance imaging; Machine learning; Computer vision","score_opus":0.010021371776222254,"score_gpt":0.22203000949344381,"score_spread":0.21200863771722156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296998418","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":[0.43426117,0.015185017,0.46676147,0.003218966,0.0013820889,0.0018126226,0.023034323,0.036577214,0.017767092],"genre_scores_gemma":[0.4752505,0.0026180402,0.4532178,0.0009290122,0.00026006222,0.0011743109,0.058262065,0.002988889,0.0052992487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972429,0.00093750254,0.0002908946,0.00069133757,0.00064011186,0.00019733302],"domain_scores_gemma":[0.991439,0.0040298435,0.0004363816,0.0013571272,0.0023933633,0.00034431295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005331798,0.0019181863,0.0011003568,0.0024522697,0.0010532576,0.0018358608,0.002778588,0.0025848832,0.0017987107],"category_scores_gemma":[0.017635465,0.00048358348,0.0011496586,0.002907133,0.0010716624,0.0013509408,0.0018632368,0.0018386268,0.0014895252],"study_design_candidate":"bench_or_experimental","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":[0.0017233613,0.0013257788,0.009098173,0.0024186827,0.0007473318,0.0006973839,0.00048667344,0.5257904,0.017761284,0.0057771225,0.08218882,0.351985],"study_design_scores_gemma":[0.0003114357,0.0007675051,0.009817705,0.00025019373,0.00012930544,0.00078014046,0.00035626953,0.9144069,0.032166,0.012803877,0.02809559,0.00011511681],"about_ca_topic_score_codex":0.011861444,"about_ca_topic_score_gemma":0.012713345,"teacher_disagreement_score":0.011861444,"about_ca_system_score_codex":0.0014477209,"about_ca_system_score_gemma":0.0019961,"threshold_uncertainty_score":0.028197587},"labels":[],"label_agreement":null},{"id":"W4297181725","doi":"10.1016/j.knosys.2022.109808","title":"A novel domain adaptation theory with Jensen–Shannon divergence","year":2022,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Université Laval","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Divergence (linguistics); Marginal distribution; Computer science; Domain (mathematical analysis); USable; Matching (statistics); Kullback–Leibler divergence; Perspective (graphical); Conditional probability distribution; Domain theory; Information theory; Theoretical computer science; Mathematics; Mathematical optimization; Algorithm; Artificial intelligence; Discrete mathematics; Econometrics; Statistics; Random variable","score_opus":0.02713439790211272,"score_gpt":0.23441198381043873,"score_spread":0.207277585908326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297181725","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030800272,0.00048867724,0.9950073,0.00019306604,0.0000602608,0.000023146247,0.000025381598,0.000082612736,0.0010395176],"genre_scores_gemma":[0.52869,0.0022439994,0.45565742,0.0009497524,0.0009035994,0.00041693923,0.00042098426,0.00028550383,0.01043182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976974,0.00084603275,0.00013127971,0.0005301876,0.0006631487,0.00013185538],"domain_scores_gemma":[0.9958306,0.0029010682,0.00017542459,0.00035442013,0.000594796,0.0001436839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003486614,0.00096254813,0.0020222052,0.0014119361,0.000713322,0.002068854,0.0028956367,0.0024427557,0.0019100333],"category_scores_gemma":[0.010119587,0.00066157326,0.0012190923,0.001462312,0.0018774682,0.00443406,0.0033533338,0.0031549206,0.0005605705],"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":[0.00014654009,0.00025103896,0.0013159141,0.00045387066,0.0004447324,0.00027824022,0.00029433012,0.5297572,0.007530627,0.26217824,0.005086682,0.19226262],"study_design_scores_gemma":[0.000004721694,0.000028303995,0.00018893054,0.000011178686,0.000018533738,0.000048695354,0.000010648409,0.94706637,0.00055262353,0.051455915,0.0005967688,0.00001739752],"about_ca_topic_score_codex":0.0029820392,"about_ca_topic_score_gemma":0.0018693329,"teacher_disagreement_score":0.003486614,"about_ca_system_score_codex":0.0012178087,"about_ca_system_score_gemma":0.0011771113,"threshold_uncertainty_score":0.018439233},"labels":[],"label_agreement":null},{"id":"W4297577962","doi":"10.48550/arxiv.2107.13682","title":"Bayesian Embeddings for Few-Shot Open World Recognition","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Aeronautics and Space Administration","keywords":"Open set; Embedding; Computer science; Artificial intelligence; Machine learning; Benchmark (surveying); Shot (pellet); Measure (data warehouse); Class (philosophy); Prior probability; Parametric statistics; Bayesian probability; Scheme (mathematics); Set (abstract data type); Data mining; Mathematics; Statistics; Geography","score_opus":0.1910433763099239,"score_gpt":0.24232313967366678,"score_spread":0.051279763363742875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297577962","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014795847,0.0004937959,0.9816949,0.00023287571,0.00004660473,0.00006403673,0.0002594999,0.0014815961,0.0009308803],"genre_scores_gemma":[0.5828471,0.00077356125,0.4057242,0.00054701034,0.00022149493,0.00030127531,0.0034184922,0.00047833053,0.0056884717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983292,0.0004793243,0.00007832612,0.00061715854,0.0003622278,0.00013366713],"domain_scores_gemma":[0.99608165,0.0017506441,0.00038968684,0.0011103845,0.00043224086,0.00023537224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020345652,0.0012884119,0.0017336014,0.0016636242,0.0005978916,0.0017858299,0.0030690664,0.002283798,0.0031322513],"category_scores_gemma":[0.010558845,0.00075216277,0.0009481221,0.0011918003,0.001605378,0.0054223873,0.0031661387,0.003643793,0.0017206494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041389358,0.0005412118,0.0029672922,0.00032614477,0.00015326701,0.00017316424,0.00035836024,0.35410672,0.00905957,0.044385143,0.010709124,0.5768062],"study_design_scores_gemma":[0.0000075518824,0.00003756646,0.0003358792,0.000016249758,0.000007250616,0.000056569537,0.00003042834,0.9537217,0.0020884532,0.042580318,0.0011005559,0.00001739339],"about_ca_topic_score_codex":0.0033205526,"about_ca_topic_score_gemma":0.0037687405,"teacher_disagreement_score":0.0033205526,"about_ca_system_score_codex":0.0013377608,"about_ca_system_score_gemma":0.0008549515,"threshold_uncertainty_score":0.01075989},"labels":[],"label_agreement":null},{"id":"W4303980694","doi":"10.3390/rs14194915","title":"Multibranch Unsupervised Domain Adaptation Network for Cross Multidomain Orchard Area Segmentation","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Ministry of Science and Technology, Taiwan","keywords":"Computer science; Segmentation; Artificial intelligence; Adaptation (eye); Domain (mathematical analysis); Domain adaptation; Machine learning; Pattern recognition (psychology)","score_opus":0.03706489414739324,"score_gpt":0.27632958920667333,"score_spread":0.2392646950592801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303980694","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":[0.04565826,0.00061209535,0.9485669,0.00019828096,0.000077832025,0.00007744184,0.0001230435,0.0021101234,0.002576063],"genre_scores_gemma":[0.72364193,0.00041849134,0.26598033,0.0003964689,0.00008456923,0.00019482545,0.0009824297,0.0002586217,0.00804227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996165,0.00007769103,0.000013480812,0.00016388383,0.000066905224,0.00006154137],"domain_scores_gemma":[0.9995503,0.00015315585,0.000042212596,0.000083799394,0.00012871297,0.000041909352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006604931,0.00088404637,0.0008500419,0.00068546453,0.00051665946,0.00059681525,0.0014164614,0.0010559306,0.0017255323],"category_scores_gemma":[0.001483921,0.00035644372,0.000684981,0.0008123879,0.00055689696,0.0012874416,0.0012189797,0.0013514889,0.00069054373],"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":[0.00023418902,0.00018846046,0.002394405,0.000068063,0.00009176126,0.00015359721,0.00014146637,0.5647903,0.014544506,0.004481685,0.0054265875,0.40748498],"study_design_scores_gemma":[0.0000027388608,0.000012452751,0.00021765062,0.0000022114875,0.000005171635,0.000019073734,0.000009414148,0.9967302,0.001355823,0.0011765969,0.00046394506,0.000004676195],"about_ca_topic_score_codex":0.007802112,"about_ca_topic_score_gemma":0.00872159,"teacher_disagreement_score":0.007802112,"about_ca_system_score_codex":0.00080608705,"about_ca_system_score_gemma":0.0008550101,"threshold_uncertainty_score":0.0155133605},"labels":[],"label_agreement":null},{"id":"W4306407742","doi":"10.1145/3563947","title":"Lifelong Online Learning from Accumulated Knowledge","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Université Laval","funders":"Universidade de Macau","keywords":"Computer science; Lifelong learning; Task (project management); Artificial intelligence; Machine learning; Process (computing); Transfer of learning; Knowledge base; Online learning; Multi-task learning; Knowledge transfer; Knowledge management","score_opus":0.09332883032205538,"score_gpt":0.3227769710322922,"score_spread":0.2294481407102368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306407742","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":[0.020230548,0.00068559643,0.97586185,0.0005259564,0.00006025943,0.000102862374,0.00014180638,0.0008167634,0.0015742776],"genre_scores_gemma":[0.7585849,0.00065918773,0.23234119,0.0006914213,0.00024267976,0.0005127579,0.0011598711,0.00023049761,0.0055774683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984699,0.00043395252,0.00009279959,0.0005567474,0.00028090426,0.00016560145],"domain_scores_gemma":[0.99138653,0.0059186616,0.0004128315,0.0013052697,0.00060073845,0.0003760037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030193846,0.0015066599,0.002116941,0.00093558006,0.00082892075,0.0017011985,0.0041653616,0.0020846077,0.0034306378],"category_scores_gemma":[0.012944406,0.0008364461,0.00075516483,0.0011146325,0.0018799495,0.0060915975,0.00402462,0.0031700446,0.0010688835],"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":[0.00039200333,0.00065480324,0.0029299192,0.00046115162,0.00014703491,0.00031268463,0.0003725291,0.61096257,0.002869973,0.04257281,0.0069767185,0.33134776],"study_design_scores_gemma":[0.000019259924,0.00007267649,0.00018314201,0.000020719435,0.000013479875,0.00004522245,0.000027089476,0.94782794,0.0009966219,0.049893968,0.00088672875,0.00001312704],"about_ca_topic_score_codex":0.0022852547,"about_ca_topic_score_gemma":0.0034786589,"teacher_disagreement_score":0.0041653616,"about_ca_system_score_codex":0.0012845931,"about_ca_system_score_gemma":0.0018601656,"threshold_uncertainty_score":0.015968204},"labels":[],"label_agreement":null},{"id":"W4306966770","doi":"10.1016/j.eswa.2022.119060","title":"Distilling and transferring knowledge via cGAN-generated samples for image classification and regression","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Regression; Image (mathematics); Artificial intelligence; Linear regression; Machine learning; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.04174860468710112,"score_gpt":0.28772653537682796,"score_spread":0.24597793068972684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306966770","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":[0.037135042,0.00059248396,0.95881695,0.0002444985,0.00009174466,0.00007446024,0.00014714264,0.0019560354,0.0009416927],"genre_scores_gemma":[0.525758,0.00036415047,0.46748483,0.0004678936,0.00014168603,0.00021202842,0.0015354711,0.0003125046,0.003723318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993765,0.00016389997,0.00002950845,0.00022903105,0.00012820025,0.000072907176],"domain_scores_gemma":[0.99814844,0.0010067888,0.000091314214,0.00029128703,0.00038987937,0.00007226773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015579885,0.001095139,0.0012612363,0.0012181057,0.0005832915,0.000996706,0.0022192663,0.0022372718,0.001923473],"category_scores_gemma":[0.005700961,0.00057676947,0.0010263093,0.0011178672,0.0009793526,0.0016291566,0.0017182727,0.0025085136,0.0008871441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045846586,0.00041556373,0.0016898451,0.0002226206,0.00014523257,0.00021283406,0.00016034486,0.39458713,0.02226427,0.0076755784,0.006593273,0.5655749],"study_design_scores_gemma":[0.000006618597,0.00002367956,0.00015308581,0.0000059723034,0.00000853923,0.000020274261,0.000009357657,0.9937878,0.0028360744,0.002784565,0.0003582149,0.0000057396205],"about_ca_topic_score_codex":0.007888628,"about_ca_topic_score_gemma":0.010736737,"teacher_disagreement_score":0.007888628,"about_ca_system_score_codex":0.0008396895,"about_ca_system_score_gemma":0.0013048154,"threshold_uncertainty_score":0.01568538},"labels":[],"label_agreement":null},{"id":"W4308235812","doi":"10.1109/icip46576.2022.9897572","title":"Interpretable Concept-Based Prototypical Networks for Few-Shot Learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Image Processing (ICIP)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Black box; Computer science; Artificial intelligence; Metric (unit); Set (abstract data type); Task (project management); Machine learning; Shot (pellet)","score_opus":0.0627864200723764,"score_gpt":0.33380520621211196,"score_spread":0.27101878613973557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308235812","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017644096,0.0006381184,0.9769249,0.00030536455,0.000066846784,0.00010775614,0.00051219936,0.0023506663,0.001450097],"genre_scores_gemma":[0.48667854,0.0004792933,0.504199,0.0005531193,0.00014050945,0.00032193007,0.002990354,0.00030767688,0.004329521],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998863,0.00031465237,0.000044473058,0.00045745907,0.00023171095,0.00008884103],"domain_scores_gemma":[0.998285,0.00073641766,0.00014593883,0.00043152252,0.00030367656,0.00009747002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017708458,0.0013098246,0.0010063867,0.0020393198,0.00061672524,0.0011864151,0.002727748,0.0015727894,0.0033474825],"category_scores_gemma":[0.005743912,0.0004395929,0.0009133113,0.0012539524,0.0013268434,0.003258727,0.0019329772,0.0028403683,0.0011464893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035297917,0.00031240864,0.0026918727,0.0003402114,0.00014198577,0.0002645904,0.00047592877,0.20637198,0.015557254,0.04388966,0.010251709,0.7193494],"study_design_scores_gemma":[0.000008768029,0.000042444102,0.0002873593,0.00001736903,0.000008391244,0.00006171407,0.000041375257,0.9574055,0.002556354,0.037560936,0.0019985952,0.000011179456],"about_ca_topic_score_codex":0.0052293525,"about_ca_topic_score_gemma":0.008370022,"teacher_disagreement_score":0.0052293525,"about_ca_system_score_codex":0.0019171662,"about_ca_system_score_gemma":0.0008594045,"threshold_uncertainty_score":0.013910115},"labels":[],"label_agreement":null},{"id":"W4310419110","doi":"10.48550/arxiv.2211.14912","title":"Impact of Strategic Sampling and Supervision Policies on Semi-supervised Learning","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs","keywords":"Representativeness heuristic; Computer science; Context (archaeology); Sample (material); Artificial intelligence; Machine learning; Labelling; Set (abstract data type); Selection (genetic algorithm); Training set; Labeled data; Representation (politics); Process (computing); Quality (philosophy); Supervised learning; Ask price; Data set; Variety (cybernetics); Statistics; Mathematics; Artificial neural network; Psychology","score_opus":0.17218409264989176,"score_gpt":0.2534048193886386,"score_spread":0.08122072673874686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310419110","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":[0.25815523,0.004772053,0.72169715,0.0032784408,0.00028308743,0.00045220074,0.0002702981,0.0031845104,0.0079069715],"genre_scores_gemma":[0.91078126,0.0005644322,0.08533264,0.0011288478,0.00013542156,0.00023875952,0.00036085735,0.00021089391,0.0012469725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9902408,0.006285154,0.00032885635,0.001517504,0.0011637675,0.00046383217],"domain_scores_gemma":[0.9401158,0.04521955,0.0023971475,0.0073105628,0.0032624062,0.0016945231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017191712,0.0017815059,0.0017376661,0.0005946967,0.0013528082,0.0016533674,0.0030038832,0.0027085855,0.0012744415],"category_scores_gemma":[0.069543764,0.0006909822,0.00057839154,0.00054798415,0.0036495014,0.0050088963,0.0031110486,0.003552592,0.00060781575],"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":[0.0025093828,0.001105852,0.016124202,0.00052206666,0.0002751299,0.00024286065,0.00084840134,0.7175513,0.0062885527,0.0384749,0.0070759645,0.20898138],"study_design_scores_gemma":[0.000098128934,0.00026004194,0.00065162784,0.000050918963,0.000026669644,0.000079620695,0.000093774244,0.97406083,0.0030862156,0.020911805,0.0006575697,0.000022647533],"about_ca_topic_score_codex":0.00431752,"about_ca_topic_score_gemma":0.0055530653,"teacher_disagreement_score":0.017191712,"about_ca_system_score_codex":0.0018179319,"about_ca_system_score_gemma":0.0034956166,"threshold_uncertainty_score":0.090919554},"labels":[],"label_agreement":null},{"id":"W4311425960","doi":"10.1016/j.addma.2022.103357","title":"Review of transfer learning in modeling additive manufacturing processes","year":2022,"lang":"en","type":"article","venue":"Additive manufacturing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Quality (philosophy); Process (computing); Computer science; Product (mathematics); Domain (mathematical analysis); Preprocessor; Systems engineering; Notation; Process modeling; Data pre-processing; Data mining; Engineering; Artificial intelligence; Work in process","score_opus":0.022336267380256306,"score_gpt":0.2478184115560235,"score_spread":0.22548214417576717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311425960","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004266973,0.3133844,0.6756247,0.0011844663,0.00064396346,0.00003971296,0.00013619997,0.000510523,0.0042090267],"genre_scores_gemma":[0.2200439,0.52114594,0.2421723,0.0013315052,0.0040908987,0.00017633382,0.00088424835,0.00040639457,0.009748551],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948895,0.00014263672,0.000048540445,0.00015765263,0.00013933762,0.000022989489],"domain_scores_gemma":[0.99872094,0.00083978375,0.00006237033,0.00010552598,0.0002388873,0.000032494852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013338119,0.0010766822,0.0019441167,0.000819522,0.0002534759,0.0012274863,0.0020845344,0.0014966668,0.0017227123],"category_scores_gemma":[0.0032198173,0.00061010075,0.0008886864,0.0016566569,0.0006312468,0.0021918558,0.0009925015,0.0015737952,0.0009550487],"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":[0.00010546902,0.00011084409,0.0005763619,0.0037856621,0.00034847076,0.00010087649,0.00006258978,0.24838053,0.0029969022,0.02706781,0.007959307,0.70850515],"study_design_scores_gemma":[0.000018647737,0.00016006599,0.0011559398,0.00066648953,0.00023070858,0.00029845914,0.00004414578,0.85262525,0.004949769,0.063676484,0.07609253,0.0000815798],"about_ca_topic_score_codex":0.002588974,"about_ca_topic_score_gemma":0.0019004352,"teacher_disagreement_score":0.002588974,"about_ca_system_score_codex":0.0006698045,"about_ca_system_score_gemma":0.0009029679,"threshold_uncertainty_score":0.0070539713},"labels":[],"label_agreement":null},{"id":"W4312050893","doi":"10.1109/jsac.2022.3221991","title":"Deep Learning-Enabled Semantic Communication Systems With Task-Unaware Transmitter and Dynamic Data","year":2022,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":232,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Transmitter; Task (project management); Artificial intelligence; Data transmission; Machine learning; Computer network; Channel (broadcasting)","score_opus":0.02747285909964657,"score_gpt":0.27351984668863916,"score_spread":0.24604698758899257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312050893","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":[0.02814564,0.00023801901,0.9687682,0.00021070952,0.000056938658,0.000031843218,0.000075599695,0.0010805491,0.001392517],"genre_scores_gemma":[0.81600237,0.00026530382,0.1786405,0.00032191898,0.00008120035,0.00012422132,0.00035347187,0.00010681701,0.0041041463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994592,0.00012052006,0.00003842066,0.00016500121,0.00013976893,0.00007715505],"domain_scores_gemma":[0.9989667,0.00041277678,0.00011599603,0.0002246976,0.00022531344,0.00005458735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012036929,0.0007254771,0.0007323491,0.00043122517,0.00047724773,0.00085648726,0.0017305962,0.0010337776,0.0013338703],"category_scores_gemma":[0.0033811838,0.000321159,0.00041416707,0.0006511736,0.0009379074,0.0031252643,0.0021770743,0.0018585224,0.000450874],"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":[0.00067202584,0.000364022,0.0017186436,0.00021830668,0.0001299645,0.00033067417,0.00045004208,0.5389986,0.024052413,0.041885074,0.0052963295,0.38588384],"study_design_scores_gemma":[0.000010078911,0.00003785853,0.0001172007,0.000006372362,0.000009856247,0.000029036251,0.00001986605,0.98341334,0.0047931,0.010913009,0.0006406152,0.000009636985],"about_ca_topic_score_codex":0.003828184,"about_ca_topic_score_gemma":0.0033851662,"teacher_disagreement_score":0.003828184,"about_ca_system_score_codex":0.00091205264,"about_ca_system_score_gemma":0.0012750274,"threshold_uncertainty_score":0.007611811},"labels":[],"label_agreement":null},{"id":"W4312093837","doi":"10.1162/neco_a_01560","title":"Dynamic Consolidation for Continual Learning","year":2022,"lang":"en","type":"article","venue":"Neural Computation","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Consolidation (business); Artificial intelligence; Computer science; Machine learning; Cognitive science; Psychology; Economics","score_opus":0.01977170686320211,"score_gpt":0.28247020650797633,"score_spread":0.2626984996447742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312093837","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":[0.02148233,0.0010252454,0.97380257,0.00039352264,0.00008320164,0.00006630174,0.000068865105,0.0011922177,0.0018857976],"genre_scores_gemma":[0.7858809,0.0008866304,0.20660146,0.0006773091,0.00025174653,0.00035575018,0.00046286712,0.00034370704,0.0045395494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990252,0.00020533685,0.00008005206,0.0003675078,0.00021548731,0.000106448446],"domain_scores_gemma":[0.99726284,0.0011244646,0.00030404626,0.0007320199,0.00038657454,0.00019001632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00249969,0.0013027454,0.00176292,0.0012441096,0.00074193,0.0016214927,0.003865658,0.0015896291,0.0032593263],"category_scores_gemma":[0.008279652,0.00088708795,0.0010152062,0.0010568902,0.0023158742,0.0048229173,0.004498412,0.0036125223,0.0008705076],"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":[0.00030492246,0.00037888513,0.0034633086,0.00032140294,0.00018002611,0.00025244412,0.0003706996,0.5399142,0.008622704,0.05853736,0.005222761,0.38243133],"study_design_scores_gemma":[0.000010415947,0.00004085202,0.00014440488,0.000017221852,0.000012169002,0.000038904574,0.000016255264,0.97676545,0.001240918,0.020654643,0.0010482726,0.000010542437],"about_ca_topic_score_codex":0.0029623415,"about_ca_topic_score_gemma":0.0033018605,"teacher_disagreement_score":0.003865658,"about_ca_system_score_codex":0.0012355408,"about_ca_system_score_gemma":0.0013412993,"threshold_uncertainty_score":0.013219774},"labels":[],"label_agreement":null},{"id":"W4312477916","doi":"10.1109/cvpr52688.2022.01404","title":"Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Consistency (knowledge bases); Computer science; Margin (machine learning); Code (set theory); Sample (material); Point (geometry); Momentum (technical analysis); Algorithm; Artificial intelligence; Machine learning; Chemistry; Mathematics; Programming language","score_opus":0.08498805987629938,"score_gpt":0.2888137516274756,"score_spread":0.2038256917511762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312477916","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":[0.026833808,0.00091661705,0.96506417,0.0010135658,0.00012765624,0.00009088427,0.00009728751,0.0018833535,0.003972684],"genre_scores_gemma":[0.58425874,0.0009511563,0.4030984,0.0018191707,0.00040236834,0.0003354256,0.00042307936,0.0013026444,0.007409101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987165,0.0004412531,0.00006606351,0.00027485596,0.0003231033,0.00017819331],"domain_scores_gemma":[0.9958936,0.0017547653,0.00026371755,0.0011313285,0.00066853565,0.00028813118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002655588,0.0009786506,0.0014163811,0.0006565661,0.0006088876,0.0020595912,0.0022326792,0.0019290476,0.0036087171],"category_scores_gemma":[0.0153490035,0.000455518,0.0006333053,0.00053727714,0.002041996,0.0042348555,0.004290377,0.0036456457,0.0013874526],"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":[0.0012408278,0.0005789463,0.004706006,0.00051719137,0.00012878455,0.00042936023,0.0004835984,0.41705137,0.028020725,0.17798567,0.016720586,0.35213694],"study_design_scores_gemma":[0.00003781042,0.00008044076,0.00020600043,0.00003838066,0.000012468377,0.000074887634,0.000031484837,0.9573208,0.0029156422,0.035971325,0.003295683,0.000015056926],"about_ca_topic_score_codex":0.0024928688,"about_ca_topic_score_gemma":0.0032123346,"teacher_disagreement_score":0.0036087171,"about_ca_system_score_codex":0.0008832445,"about_ca_system_score_gemma":0.0018977363,"threshold_uncertainty_score":0.014044225},"labels":[],"label_agreement":null},{"id":"W4312526008","doi":"10.1109/cvpr52688.2022.00888","title":"Few-shot Learning with Noisy Labels","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":46,"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":"Computer science; Robustness (evolution); Artificial intelligence; Machine learning; Transformer; Single shot; Pattern recognition (psychology); Engineering","score_opus":0.05155474951415761,"score_gpt":0.26713971540313314,"score_spread":0.21558496588897552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312526008","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":[0.06057769,0.0012160816,0.9305399,0.0005949482,0.0001609516,0.00016594092,0.0005752345,0.0039134594,0.0022557538],"genre_scores_gemma":[0.6831478,0.00039204754,0.30416554,0.0010853041,0.00024760392,0.0002413624,0.0041434485,0.00041568678,0.006161263],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973961,0.0007459611,0.00012202485,0.0009213125,0.0006063655,0.00020827343],"domain_scores_gemma":[0.99317425,0.0036445342,0.00035435872,0.0015548737,0.0009181982,0.00035381873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033704964,0.0014825842,0.002664238,0.0014786907,0.0010167215,0.001885244,0.0045723794,0.002482781,0.0025589643],"category_scores_gemma":[0.0149546275,0.0006725438,0.0010537313,0.0011636545,0.0016072264,0.004653179,0.0032844548,0.0034457406,0.001301116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010606132,0.0008869339,0.007210306,0.00067554665,0.00036370964,0.00041623588,0.0004757418,0.33575016,0.010515259,0.0176261,0.022932379,0.602087],"study_design_scores_gemma":[0.000022264556,0.0000732865,0.0004043758,0.000019059637,0.000016630247,0.000077418896,0.000044055745,0.98145294,0.0033027588,0.013440974,0.0011286739,0.000017523196],"about_ca_topic_score_codex":0.0043793335,"about_ca_topic_score_gemma":0.006914747,"teacher_disagreement_score":0.0045723794,"about_ca_system_score_codex":0.0014450006,"about_ca_system_score_gemma":0.0013105209,"threshold_uncertainty_score":0.017825067},"labels":[],"label_agreement":null},{"id":"W4312539501","doi":"10.1109/cvpr52688.2022.00255","title":"Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Hospital; University of Toronto; Sunnybrook Health Science Centre; McGill University","funders":"","keywords":"Computer science; Consistency (knowledge bases); Pixel; Transformer; Artificial intelligence; Class (philosophy); Observable; Image (mathematics); Computer vision; Machine learning; Pattern recognition (psychology); Engineering","score_opus":0.10548477742640627,"score_gpt":0.2952703571092695,"score_spread":0.18978557968286325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312539501","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07058278,0.00041855293,0.9231243,0.0005622872,0.00008665814,0.00007638093,0.00032231415,0.0016317762,0.0031949983],"genre_scores_gemma":[0.9439638,0.00017008401,0.047989685,0.00026748862,0.000082151615,0.00011831152,0.00035997375,0.00013441555,0.0069141467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965036,0.000058440623,0.000014332394,0.00015208156,0.00005723125,0.00006758774],"domain_scores_gemma":[0.99925226,0.00037675214,0.00008654348,0.00008913293,0.00013807628,0.00005716472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086910866,0.0010723982,0.0010815602,0.0005392262,0.0003155873,0.0007582685,0.0024745031,0.0010677832,0.0032839943],"category_scores_gemma":[0.0020917351,0.0006031894,0.00067703327,0.0004466316,0.0009330293,0.0015742427,0.001221962,0.0018820515,0.0004763034],"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":[0.00040839677,0.00012443405,0.002117546,0.00009343344,0.000074544856,0.00015793342,0.00017659439,0.8718704,0.0073045758,0.023966158,0.003548624,0.09015731],"study_design_scores_gemma":[0.0000058641913,0.00001684172,0.00015232278,0.0000016820235,0.0000056153763,0.000011048219,0.0000036387846,0.9947345,0.00039489727,0.0044940603,0.0001767845,0.0000027122762],"about_ca_topic_score_codex":0.012427822,"about_ca_topic_score_gemma":0.01475358,"teacher_disagreement_score":0.012427822,"about_ca_system_score_codex":0.001468875,"about_ca_system_score_gemma":0.0009845438,"threshold_uncertainty_score":0.024710953},"labels":[],"label_agreement":null},{"id":"W4312745891","doi":"10.1109/icpr56361.2022.9956614","title":"Attentive Task Interaction Network for Multi-Task Learning","year":2022,"lang":"en","type":"article","venue":"2022 26th International Conference on Pattern Recognition (ICPR)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Task (project management); Multi-task learning; Artificial intelligence; Machine learning; Feature (linguistics); Task analysis; Popularity","score_opus":0.12402203218412317,"score_gpt":0.3269272616921161,"score_spread":0.20290522950799295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312745891","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":[0.026520161,0.001432176,0.96322155,0.00074095494,0.00019762736,0.000099870165,0.00036120974,0.0028422254,0.0045841807],"genre_scores_gemma":[0.7924014,0.0008780259,0.19264917,0.00087554246,0.00027439697,0.00045680936,0.0014108254,0.00029115152,0.0107628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945337,0.00017939837,0.000022525484,0.00018123873,0.0000887327,0.00007473888],"domain_scores_gemma":[0.99890625,0.0005717856,0.000092402035,0.00018673664,0.00014772042,0.00009498942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013384892,0.0012161974,0.00084400555,0.00053364196,0.0005132973,0.00081757596,0.0022945767,0.0014542949,0.004399626],"category_scores_gemma":[0.0039244355,0.00045314897,0.0006965801,0.0006525795,0.0008090189,0.0030476856,0.0023651,0.002832732,0.00131362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070980017,0.00054707634,0.001708604,0.0003521011,0.0001840524,0.00026588535,0.00024575065,0.451944,0.017905526,0.03604014,0.015739504,0.47435755],"study_design_scores_gemma":[0.00001760185,0.000099387646,0.00027160122,0.000011732115,0.000024202982,0.00004266382,0.0000164926,0.9624993,0.0028524545,0.031460855,0.0026891346,0.0000145999065],"about_ca_topic_score_codex":0.0032380694,"about_ca_topic_score_gemma":0.0051759207,"teacher_disagreement_score":0.004399626,"about_ca_system_score_codex":0.0010698192,"about_ca_system_score_gemma":0.0010418305,"threshold_uncertainty_score":0.014718235},"labels":[],"label_agreement":null},{"id":"W4312896061","doi":"10.1109/ijcnn55064.2022.9892722","title":"Meta-free few-shot learning via representation learning with weight averaging","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Machine learning; Computer science; Probabilistic logic; Meta learning (computer science); Benchmark (surveying); Representation (politics); Transfer of learning; Feature learning; Range (aeronautics); Task (project management)","score_opus":0.07457992658008096,"score_gpt":0.26766154343015214,"score_spread":0.1930816168500712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312896061","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":[0.015002108,0.00058639,0.9823497,0.00017677722,0.000058751146,0.000055650937,0.0000862344,0.0009917964,0.0006926661],"genre_scores_gemma":[0.64181685,0.0006654258,0.34984392,0.00062425504,0.00026475536,0.0003371077,0.0013348631,0.0004042685,0.004708531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986143,0.0003856201,0.00008459503,0.0005253657,0.0002588114,0.00013125333],"domain_scores_gemma":[0.99688834,0.0015366167,0.0002844654,0.0007272074,0.00040236057,0.00016100817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024359098,0.0019036534,0.0026883485,0.0014799034,0.00081191544,0.0016896512,0.004404592,0.0022296591,0.0021163016],"category_scores_gemma":[0.008747468,0.00085632567,0.0016721141,0.0015377356,0.0014224531,0.005005335,0.0027580408,0.0035852424,0.00093045033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027826714,0.00035176327,0.0022013204,0.00031145228,0.0004079735,0.00016826052,0.000327515,0.48127836,0.008056238,0.017321773,0.0052337823,0.48406336],"study_design_scores_gemma":[0.0000083164905,0.00004832665,0.00014750558,0.000013017216,0.000020827732,0.00003106711,0.000015898608,0.9839983,0.001618037,0.01370553,0.000378696,0.000014451473],"about_ca_topic_score_codex":0.0036276581,"about_ca_topic_score_gemma":0.00426031,"teacher_disagreement_score":0.004404592,"about_ca_system_score_codex":0.0012545318,"about_ca_system_score_gemma":0.001196795,"threshold_uncertainty_score":0.012882471},"labels":[],"label_agreement":null},{"id":"W4313184971","doi":"10.1109/cvpr52688.2022.01642","title":"MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea","keywords":"Computer science; Modal; Modality (human–computer interaction); Segmentation; Adaptation (eye); Modalities; Domain adaptation; Consistency (knowledge bases); Test data; Scheme (mathematics); Artificial intelligence; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.0619686168823289,"score_gpt":0.2893112902145141,"score_spread":0.22734267333218522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313184971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010438629,0.00012825512,0.9858256,0.00006487862,0.00006046413,0.000044471344,0.00008024815,0.002436952,0.00092048355],"genre_scores_gemma":[0.3935646,0.00022110036,0.59999096,0.0003658115,0.00008658097,0.0002719433,0.00084324734,0.0010071547,0.0036486154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940753,0.00014405663,0.000024003852,0.00018513351,0.00018369715,0.00005567666],"domain_scores_gemma":[0.9991074,0.00030216353,0.00006775413,0.00024861813,0.00020584669,0.000068193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010052186,0.00092767307,0.0006332527,0.0007002375,0.00037355424,0.00074106053,0.0016775269,0.0010609506,0.003023011],"category_scores_gemma":[0.0030257772,0.0003658578,0.00085221464,0.0006619081,0.00073786866,0.0013089229,0.0017765725,0.0015991072,0.0011660546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005424365,0.00022944911,0.0020724859,0.00016289896,0.00015419668,0.00014689114,0.00029394298,0.16703358,0.1284888,0.005175553,0.006799968,0.6888998],"study_design_scores_gemma":[0.000011431854,0.00007713701,0.0010210343,0.000009365942,0.00001797454,0.000095175776,0.00003958816,0.96739334,0.022979734,0.0051842467,0.0031432833,0.000027611142],"about_ca_topic_score_codex":0.0031668546,"about_ca_topic_score_gemma":0.004790386,"teacher_disagreement_score":0.0031668546,"about_ca_system_score_codex":0.00046873966,"about_ca_system_score_gemma":0.0007071903,"threshold_uncertainty_score":0.010112941},"labels":[],"label_agreement":null},{"id":"W4313316212","doi":"10.1109/tmm.2022.3233306","title":"Cycle Consistency Based Pseudo Label and Fine Alignment for Unsupervised Domain Adaptation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"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 Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Consistency (knowledge bases); Data mining; Machine learning","score_opus":0.029257884249003458,"score_gpt":0.2524013322969264,"score_spread":0.22314344804792294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313316212","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":[0.008719738,0.00015991017,0.989368,0.000042191168,0.00003187151,0.000037592727,0.00003903013,0.0008163663,0.00078533933],"genre_scores_gemma":[0.41491848,0.0003198329,0.57795286,0.00031573436,0.00009825009,0.00030671415,0.0007420346,0.0005452389,0.004800826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988605,0.00027495745,0.000057175996,0.0004326509,0.00028638262,0.00008835767],"domain_scores_gemma":[0.9984584,0.00044517856,0.00014773053,0.00046131157,0.00039851468,0.00008891149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010420465,0.00081865524,0.00094620895,0.0010837823,0.0006789293,0.0009232673,0.0016557107,0.0009144469,0.001954865],"category_scores_gemma":[0.0039406777,0.00034671722,0.0007421595,0.0011692719,0.0010089759,0.0023066015,0.0017942034,0.0014629491,0.0011253956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030065211,0.0002482189,0.0030099354,0.00017008097,0.000085240754,0.00013944603,0.0003815041,0.12204753,0.045678206,0.01863732,0.0055535184,0.8037483],"study_design_scores_gemma":[0.000020100973,0.0000910471,0.00096875336,0.000014042702,0.00002132349,0.00015035471,0.000062148676,0.96097815,0.016166799,0.016381152,0.005110725,0.000035501398],"about_ca_topic_score_codex":0.0026484393,"about_ca_topic_score_gemma":0.0030710935,"teacher_disagreement_score":0.0026484393,"about_ca_system_score_codex":0.00068113935,"about_ca_system_score_gemma":0.001197595,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4313532401","doi":"10.48550/arxiv.2301.01047","title":"A Theory of Human-Like Few-Shot Learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Artificial intelligence; Autoencoder; Computer science; Generative grammar; Deep learning; Machine learning; Generative model","score_opus":0.17919300503171298,"score_gpt":0.2274187735958758,"score_spread":0.04822576856416283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313532401","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008302796,0.00039200988,0.9870122,0.0008234817,0.000050179486,0.000031831725,0.00009954963,0.00016838146,0.003119579],"genre_scores_gemma":[0.6719194,0.0013065033,0.31282356,0.001777274,0.00048647219,0.00039345745,0.00062256475,0.0002668097,0.0104040075],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998026,0.00071830384,0.000072327544,0.0006383367,0.00042438207,0.000120627614],"domain_scores_gemma":[0.9918651,0.0057629803,0.00046401733,0.0010761279,0.0005292522,0.00030251397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033659409,0.00077836,0.0015037734,0.0011407726,0.0009619293,0.002131638,0.0031121888,0.0023916445,0.0044257506],"category_scores_gemma":[0.016865734,0.000705461,0.0011831643,0.00092540827,0.005117037,0.007115848,0.0029870153,0.0035696186,0.0007265436],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052052,0.00008207052,0.0011247046,0.00027994934,0.00010851673,0.000116057185,0.0003654551,0.104130976,0.0019592796,0.8534409,0.0031523586,0.035187677],"study_design_scores_gemma":[0.0000068083796,0.00004006337,0.00025771183,0.000028250213,0.000010493371,0.00008580098,0.00002901363,0.32982555,0.00061191473,0.6675705,0.0015144731,0.000019456664],"about_ca_topic_score_codex":0.0020202075,"about_ca_topic_score_gemma":0.0021098978,"teacher_disagreement_score":0.0044257506,"about_ca_system_score_codex":0.0014558564,"about_ca_system_score_gemma":0.0010634961,"threshold_uncertainty_score":0.017800987},"labels":[],"label_agreement":null},{"id":"W4315630116","doi":"10.1109/globecom48099.2022.10000610","title":"Transfer Learning with Input Reconstruction Loss","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Transfer of learning; Artificial intelligence; Artificial neural network; Machine learning; Exploit; Wireless; Deep learning; Feature (linguistics); Task (project management); Wireless network; Distributed computing; Engineering","score_opus":0.031227157364804945,"score_gpt":0.26212370735637963,"score_spread":0.2308965499915747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630116","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":[0.011997122,0.00036843258,0.9846853,0.0003266785,0.000053467313,0.000052619707,0.00006152049,0.0007366758,0.0017181019],"genre_scores_gemma":[0.7940658,0.0006313042,0.19442208,0.00061882305,0.00018228142,0.0004944849,0.00061606173,0.00027028332,0.008698735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896824,0.0003759462,0.00006196325,0.0002471866,0.00023235499,0.000114375944],"domain_scores_gemma":[0.9976023,0.0014483547,0.00016188572,0.0003591657,0.0003553398,0.00007307081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002577266,0.0018618336,0.0015495303,0.00057156896,0.0004383142,0.0011285874,0.0021514436,0.0023425682,0.0031146323],"category_scores_gemma":[0.008705625,0.0005325079,0.00097306294,0.0008659864,0.0014264338,0.0029283464,0.002518072,0.0031860222,0.00089799927],"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":[0.00015039057,0.0001356753,0.00068991684,0.00012212532,0.00006771068,0.00011523425,0.000054924945,0.8740865,0.0017834362,0.014661034,0.0027648597,0.10536827],"study_design_scores_gemma":[0.000006043417,0.000028440276,0.000046710265,0.0000049503915,0.0000051212783,0.000012260168,0.0000036130177,0.9940361,0.0006745338,0.004949429,0.00022930221,0.0000035644127],"about_ca_topic_score_codex":0.0021554786,"about_ca_topic_score_gemma":0.0013190372,"teacher_disagreement_score":0.0031146323,"about_ca_system_score_codex":0.0011886616,"about_ca_system_score_gemma":0.0011791661,"threshold_uncertainty_score":0.013630033},"labels":[],"label_agreement":null},{"id":"W4316040405","doi":"10.20944/preprints202212.0049.v2","title":"TransLearn-YOLOx: Improved-YOLO with Transfer Learning for Fast and Accurate Multiclass UAV Detection","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Higher Education Commision, Pakistan; Higher Education Commission, Pakistan","keywords":"Computer science; Transfer of learning; Artificial intelligence; Code (set theory); Object detection; Precision and recall; F1 score; Deep learning; Class (philosophy); Transfer (computing); Rotor (electric); Machine learning; Real-time computing; Pattern recognition (psychology); Engineering","score_opus":0.1185859487311561,"score_gpt":0.3263680711943518,"score_spread":0.2077821224631957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316040405","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12832856,0.0049113478,0.7722091,0.0008280248,0.0011862193,0.00059361017,0.006152035,0.07848052,0.007310559],"genre_scores_gemma":[0.424343,0.0009073171,0.5178944,0.00113302,0.00028528666,0.00083818904,0.036360417,0.0022907143,0.0159476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919504,0.0001479226,0.000040086605,0.0003733674,0.00012403415,0.00011952229],"domain_scores_gemma":[0.99917835,0.00024493632,0.000053803265,0.00026293725,0.00020392041,0.000055998484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015871335,0.0022034214,0.0012023808,0.0014790983,0.0006865328,0.001157079,0.0032114172,0.0018808382,0.005182898],"category_scores_gemma":[0.0036754343,0.0006126045,0.0012670108,0.0010128291,0.0005830181,0.0022109163,0.0024596106,0.0026556738,0.004301736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094864744,0.0008811211,0.0046243505,0.00048951735,0.00030562523,0.00022783632,0.00016858627,0.10545187,0.01899332,0.00287015,0.07937518,0.7856637],"study_design_scores_gemma":[0.00007751922,0.00019540831,0.0010680095,0.00004219763,0.000029422223,0.00009376715,0.000063910964,0.97693324,0.011054673,0.0034853774,0.006927364,0.000029079609],"about_ca_topic_score_codex":0.008885874,"about_ca_topic_score_gemma":0.011362765,"teacher_disagreement_score":0.008885874,"about_ca_system_score_codex":0.000909727,"about_ca_system_score_gemma":0.0011643736,"threshold_uncertainty_score":0.017668307},"labels":[],"label_agreement":null},{"id":"W4316660917","doi":"10.1109/access.2023.3237025","title":"Domain Adaptation: Challenges, Methods, Datasets, and Applications","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":181,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Domain (mathematical analysis); Domain adaptation; Adaptation (eye); Machine learning; Artificial intelligence; Data science; Taxonomy (biology); Data mining","score_opus":0.13391293187276632,"score_gpt":0.39923017636195895,"score_spread":0.26531724448919264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316660917","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029523278,0.13395372,0.7956355,0.013974034,0.002124224,0.0007560918,0.0061441907,0.0033739512,0.014514957],"genre_scores_gemma":[0.1673698,0.10856065,0.6862684,0.004383321,0.0031080812,0.0016646629,0.021475265,0.0007461647,0.006423701],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99362165,0.002221089,0.0005934828,0.0014729432,0.001899693,0.00019115438],"domain_scores_gemma":[0.987688,0.0070699546,0.00045405613,0.0020401585,0.0023544005,0.00039357503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008997372,0.0013550776,0.0012776442,0.0032995606,0.000888363,0.003102449,0.0026285495,0.0023884918,0.0016045666],"category_scores_gemma":[0.021754915,0.00067059044,0.0010930732,0.00561945,0.0012659873,0.0044438923,0.0024862364,0.0044078864,0.0014118575],"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":[0.00019650826,0.00027800095,0.008257613,0.0033126224,0.00020418139,0.00018595679,0.00026660287,0.034143686,0.003955388,0.02104206,0.06384943,0.864308],"study_design_scores_gemma":[0.00006432962,0.00026226605,0.012829328,0.0024644514,0.00019138634,0.0015246896,0.001679357,0.48202595,0.014127464,0.14592792,0.33865634,0.00024658043],"about_ca_topic_score_codex":0.0047013992,"about_ca_topic_score_gemma":0.0040508676,"teacher_disagreement_score":0.008997372,"about_ca_system_score_codex":0.0012979012,"about_ca_system_score_gemma":0.0019499674,"threshold_uncertainty_score":0.047583163},"labels":[],"label_agreement":null},{"id":"W4317036100","doi":"10.1145/3576045","title":"Distilled Meta-learning for Multi-Class Incremental Learning","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"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 Ottawa","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Forgetting; Meta learning (computer science); Computer science; Artificial intelligence; Machine learning; Incremental learning; Benchmark (surveying); Task (project management); Class (philosophy); Active learning (machine learning); Engineering; Psychology","score_opus":0.1055085410575087,"score_gpt":0.3442216972377931,"score_spread":0.2387131561802844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317036100","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":[0.011580817,0.0011298689,0.9847227,0.0002162916,0.000077250486,0.000084502935,0.00008438134,0.0012271133,0.0008771343],"genre_scores_gemma":[0.6326296,0.00086775067,0.36188906,0.0005743024,0.0002426257,0.00045796507,0.0006172657,0.00028600593,0.002435565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989624,0.00030639954,0.00007667443,0.00031249458,0.00023282187,0.00010917137],"domain_scores_gemma":[0.9970074,0.00159403,0.00020460734,0.0006080157,0.00044207493,0.00014374549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026058063,0.0013883971,0.0019849185,0.0014443252,0.00057210197,0.0013179592,0.004487699,0.001637295,0.0021246355],"category_scores_gemma":[0.007427581,0.0007544204,0.0012837816,0.0013080692,0.0012126386,0.0037874323,0.0023983743,0.0031258913,0.00071243074],"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":[0.00021761682,0.00034200816,0.0020923254,0.00037688698,0.00029109197,0.00019351473,0.00025277707,0.52856517,0.00508192,0.02126365,0.0053031337,0.43602],"study_design_scores_gemma":[0.000014335726,0.000048119316,0.000091788155,0.000015080731,0.00002729921,0.000029905643,0.000010162431,0.9850825,0.0011809974,0.012695561,0.0007931866,0.0000111315885],"about_ca_topic_score_codex":0.002467308,"about_ca_topic_score_gemma":0.00411598,"teacher_disagreement_score":0.004487699,"about_ca_system_score_codex":0.0011037944,"about_ca_system_score_gemma":0.0013318877,"threshold_uncertainty_score":0.013781011},"labels":[],"label_agreement":null},{"id":"W4317384342","doi":"10.1109/access.2023.3237966","title":"A Cross-Modal Alignment for Zero-Shot Image Classification","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Natural Resources","keywords":"Computer science; Matching (statistics); Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Embedding; Metric (unit); Modal; Feature extraction; Zero (linguistics); Image (mathematics); Encoder; Contextual image classification; Measure (data warehouse); Key (lock); Data mining; Mathematics","score_opus":0.1308000278195402,"score_gpt":0.4054642342831136,"score_spread":0.2746642064635734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317384342","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":[0.033053488,0.0007269146,0.95415235,0.00015680652,0.00016610615,0.00014680935,0.00044170994,0.008654283,0.0025014994],"genre_scores_gemma":[0.50735664,0.0004321077,0.4742062,0.00059039815,0.00022285165,0.00039649443,0.0057048257,0.0007688365,0.010321636],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99820065,0.0002675489,0.00007198674,0.00092686847,0.00032804135,0.00020480104],"domain_scores_gemma":[0.9988123,0.00024770774,0.00009674423,0.00040686078,0.00033786302,0.00009841178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013311013,0.0011432578,0.0014666896,0.0016489491,0.00086280424,0.0010170266,0.0023563246,0.0015114242,0.004485541],"category_scores_gemma":[0.0029383025,0.00040211497,0.001063414,0.0017508093,0.00086839264,0.0025065492,0.0026562067,0.0023065172,0.0029323339],"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":[0.0005040472,0.0007205148,0.0023365882,0.00018520221,0.0001194341,0.00010056343,0.000213933,0.0166755,0.051339447,0.0045635826,0.012317665,0.9109235],"study_design_scores_gemma":[0.000041635867,0.0003529377,0.0029101914,0.000024781473,0.00006237701,0.00031869684,0.00015501544,0.9376742,0.03999073,0.011671095,0.0067395777,0.000058846934],"about_ca_topic_score_codex":0.0039721094,"about_ca_topic_score_gemma":0.005954661,"teacher_disagreement_score":0.004485541,"about_ca_system_score_codex":0.0006879062,"about_ca_system_score_gemma":0.0010068264,"threshold_uncertainty_score":0.015005648},"labels":[],"label_agreement":null},{"id":"W4317438807","doi":"10.1371/journal.pcbi.1010808","title":"Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals","year":2023,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Global Health Research; Canadian Institute for Advanced Research","funders":"Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; Wellcome; Canadian Institute for Advanced Research; Medical Research Council; University of Oxford; Wellcome Trust","keywords":"Hebbian theory; Computer science; Artificial intelligence; Context (archaeology); Artificial neural network; Task (project management); Gating; Forgetting; Heuristics; Machine learning; Pattern recognition (psychology)","score_opus":0.0408393312688971,"score_gpt":0.2604761208785358,"score_spread":0.21963678960963867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317438807","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":[0.34300312,0.00020082704,0.65334696,0.0005704712,0.00003911598,0.0000345827,0.00008605471,0.00032350558,0.0023952832],"genre_scores_gemma":[0.9694044,0.00007467872,0.028808825,0.000058408637,0.000009278422,0.000039308172,0.000031817304,0.000021865859,0.0015513796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997948,0.00007164909,0.000007119101,0.00006994057,0.000021727183,0.00003475824],"domain_scores_gemma":[0.9991062,0.00054257934,0.000104280596,0.00010007596,0.00007438517,0.00007231432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070720783,0.00035697233,0.00037830631,0.00021499349,0.00017555247,0.0006175287,0.0008167739,0.0008355953,0.0012862165],"category_scores_gemma":[0.0029897061,0.00037377034,0.00035144147,0.00017401573,0.0008842175,0.00097042293,0.000557044,0.0011106092,0.00019774835],"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":[0.00014447003,0.000071302515,0.002042715,0.00003652207,0.00003642113,0.000072537274,0.0001400854,0.9535875,0.0063389023,0.015939204,0.00030976062,0.021280544],"study_design_scores_gemma":[0.0000058038086,0.000014257713,0.00034302205,0.0000017386834,0.0000026442822,0.000011418088,0.0000032674675,0.98876846,0.00037785017,0.010377002,0.000091452916,0.000003051304],"about_ca_topic_score_codex":0.004590873,"about_ca_topic_score_gemma":0.0070799473,"teacher_disagreement_score":0.004590873,"about_ca_system_score_codex":0.0005477974,"about_ca_system_score_gemma":0.00056882255,"threshold_uncertainty_score":0.0091282725},"labels":[],"label_agreement":null},{"id":"W4318067874","doi":"10.1007/s10489-022-04414-2","title":"Center transfer for supervised domain adaptation","year":2023,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"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 Alberta","funders":"Hong Kong Polytechnic University","keywords":"Computer science; Discriminative model; Domain adaptation; Domain (mathematical analysis); Transfer of learning; Artificial intelligence; Field (mathematics); Feature (linguistics); CTL*; Adaptation (eye); Labeled data; Pattern recognition (psychology); Machine learning; Data mining","score_opus":0.0532276786534139,"score_gpt":0.27338108622513513,"score_spread":0.22015340757172122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318067874","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0096278405,0.0008073031,0.9854026,0.0001987106,0.00010249065,0.00006975517,0.00015483142,0.0020327813,0.0016036289],"genre_scores_gemma":[0.56335264,0.0011831495,0.40894634,0.0009831721,0.00039351248,0.0005241684,0.002798175,0.00089849834,0.02092037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991486,0.00024363284,0.000033397133,0.00031628687,0.00017061047,0.00008750237],"domain_scores_gemma":[0.9980718,0.0008904266,0.000084675194,0.00051934185,0.00034772954,0.000085963526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017283264,0.0010371296,0.0016128923,0.0012046456,0.00078465557,0.00082004414,0.0024653957,0.001791054,0.00446297],"category_scores_gemma":[0.004791034,0.0004406993,0.0010202286,0.0012839957,0.0010094225,0.0022679812,0.0026861452,0.0025711285,0.0022808546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049451174,0.00052797794,0.0010604862,0.0002721395,0.0002972137,0.00015527497,0.00016297326,0.18857047,0.020413518,0.033082277,0.025804028,0.7291592],"study_design_scores_gemma":[0.000015214251,0.000038942053,0.00029091066,0.00000901406,0.000022776609,0.00004864513,0.000019038956,0.96702015,0.005139614,0.025407758,0.0019723568,0.00001570391],"about_ca_topic_score_codex":0.005004459,"about_ca_topic_score_gemma":0.0056040147,"teacher_disagreement_score":0.005004459,"about_ca_system_score_codex":0.0009137235,"about_ca_system_score_gemma":0.0013340223,"threshold_uncertainty_score":0.014930069},"labels":[],"label_agreement":null},{"id":"W4319300892","doi":"10.1109/wacv56688.2023.00278","title":"TeST: Test-time Self-Training under Distribution Shift","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Test data; Artificial intelligence; Test (biology); Machine learning; Inference; Adaptation (eye); Data mining; Pattern recognition (psychology)","score_opus":0.02988191807964998,"score_gpt":0.2922269165930714,"score_spread":0.2623449985134214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300892","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17959797,0.0016667745,0.75586975,0.0007852769,0.0007251613,0.00046211615,0.0014518455,0.05337493,0.006066104],"genre_scores_gemma":[0.7022183,0.00029863074,0.27759567,0.0013857291,0.000150105,0.00048711628,0.007940505,0.002461703,0.007462228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872226,0.00033367562,0.0000692882,0.0004620901,0.000273072,0.00013949123],"domain_scores_gemma":[0.99630475,0.0013706976,0.00020099818,0.0013390279,0.0005978643,0.00018673408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027355028,0.0018457965,0.0010627046,0.00070352276,0.00051177666,0.00088020944,0.0035514864,0.0019169489,0.004324446],"category_scores_gemma":[0.010975115,0.0005877138,0.0009554469,0.0006698301,0.00088392256,0.0027062332,0.0025012873,0.0033402136,0.0025589995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011985736,0.000864257,0.009296238,0.00027432456,0.0004726443,0.0002887093,0.00017867246,0.277906,0.022040429,0.0027947486,0.037926868,0.64675844],"study_design_scores_gemma":[0.00006489519,0.00022400319,0.0011977982,0.000018199255,0.000030382163,0.00016653792,0.000046552785,0.9809327,0.011726691,0.0028242762,0.0027434365,0.000024490195],"about_ca_topic_score_codex":0.005789763,"about_ca_topic_score_gemma":0.0071300347,"teacher_disagreement_score":0.005789763,"about_ca_system_score_codex":0.00084411696,"about_ca_system_score_gemma":0.0013917397,"threshold_uncertainty_score":0.014466882},"labels":[],"label_agreement":null},{"id":"W4319778765","doi":"10.1109/tnnls.2023.3238729","title":"Automatic Metric Search for Few-Shot Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Western University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Metric (unit); Artificial intelligence; Task (project management); Class (philosophy); Machine learning; Domain (mathematical analysis); Sample (material); Shot (pellet); Space (punctuation); Function (biology); Data mining; Mathematics","score_opus":0.0439409307917825,"score_gpt":0.2830359300858012,"score_spread":0.2390949992940187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319778765","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028055895,0.0009867362,0.9669662,0.00021644402,0.000041752897,0.00009937979,0.00014991294,0.002227365,0.001256153],"genre_scores_gemma":[0.69017875,0.0004264992,0.30294904,0.00059454545,0.00008023043,0.00033627142,0.0014438,0.00043361614,0.00355729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888176,0.0003316169,0.00007117284,0.00042535056,0.00020105239,0.00008904798],"domain_scores_gemma":[0.9982665,0.0009383333,0.0001359828,0.00028092926,0.00024685098,0.0001314699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001612351,0.0014619193,0.002586889,0.001463296,0.000723958,0.0011305866,0.003949329,0.0022122767,0.0029700666],"category_scores_gemma":[0.0069305534,0.0007400682,0.00080582284,0.0012006024,0.0013096281,0.00372327,0.0025984119,0.0022389074,0.0010115568],"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":[0.00028886096,0.0003737889,0.0021419474,0.00038557185,0.00014068268,0.00017693949,0.00023330614,0.4987471,0.008408707,0.019770995,0.008962262,0.46036988],"study_design_scores_gemma":[0.000010994748,0.000042327752,0.000085240456,0.000006959549,0.00000537661,0.000037177517,0.000014926199,0.98920864,0.00100317,0.009179157,0.0003974793,0.000008507296],"about_ca_topic_score_codex":0.004558067,"about_ca_topic_score_gemma":0.005550338,"teacher_disagreement_score":0.004558067,"about_ca_system_score_codex":0.0014267273,"about_ca_system_score_gemma":0.0014674222,"threshold_uncertainty_score":0.010351658},"labels":[],"label_agreement":null},{"id":"W4320014598","doi":"10.2139/ssrn.4331203","title":"Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"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 British Columbia; University of British Columbia Hospital","funders":"","keywords":"Simple (philosophy); Computer science; Active learning (machine learning); Domain (mathematical analysis); Shot (pellet); One shot; Artificial intelligence; Machine learning; Engineering; Mathematics; Materials science","score_opus":0.06906769261902579,"score_gpt":0.31324693607349124,"score_spread":0.24417924345446546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320014598","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":[0.012594817,0.0007125005,0.9848481,0.00039825126,0.00006433916,0.00004383698,0.0000882063,0.0005356814,0.0007143685],"genre_scores_gemma":[0.6908997,0.0009284528,0.29873988,0.00071687996,0.0003760684,0.00029582682,0.00074780936,0.00040951022,0.006885872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894136,0.0004394019,0.000061249164,0.00032208656,0.00013857264,0.000097307326],"domain_scores_gemma":[0.99387795,0.0042390814,0.00030447918,0.0008166567,0.00041093305,0.00035094537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003913156,0.0013724621,0.0025308933,0.0011778348,0.00077719375,0.0024331906,0.0045886463,0.0035702323,0.0027809672],"category_scores_gemma":[0.013151283,0.0010135005,0.0014900289,0.0012204818,0.0016537391,0.005869363,0.0037905986,0.005388047,0.00097589474],"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":[0.00039704388,0.00039759369,0.0017484867,0.0003139124,0.00036899207,0.00012887534,0.00026020978,0.75630975,0.0037460274,0.051065095,0.0044255997,0.1808383],"study_design_scores_gemma":[0.000004591566,0.00001748336,0.00005607195,0.00000755276,0.000009126726,0.000010971002,0.0000054510483,0.9828143,0.00024695153,0.016646296,0.00017500717,0.0000061675237],"about_ca_topic_score_codex":0.0034443066,"about_ca_topic_score_gemma":0.0047825086,"teacher_disagreement_score":0.0045886463,"about_ca_system_score_codex":0.0010917651,"about_ca_system_score_gemma":0.0010629796,"threshold_uncertainty_score":0.020694971},"labels":[],"label_agreement":null},{"id":"W4321789461","doi":"10.3390/info14030148","title":"Multi-Dimensional Information Alignment in Different Modalities for Generalized Zero-Shot and Few-Shot Learning","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Discriminative model; Embedding; Artificial intelligence; Computer science; Benchmark (surveying); Pattern recognition (psychology); Space (punctuation); Visual space; Shot (pellet); Feature (linguistics); Feature vector; Class (philosophy); Modalities; Zero (linguistics); Machine learning; Geography","score_opus":0.044955854781808524,"score_gpt":0.2758490148283173,"score_spread":0.23089316004650878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321789461","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02652761,0.00093178137,0.96895546,0.00031483738,0.00009088301,0.00012276306,0.00026159247,0.001373372,0.0014216902],"genre_scores_gemma":[0.73130983,0.00070599944,0.2574488,0.00090750394,0.00025569974,0.00043439612,0.0023739152,0.0002455683,0.0063182022],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986848,0.00030626298,0.000060986797,0.00058731786,0.00022243803,0.00013820028],"domain_scores_gemma":[0.9986407,0.0005952493,0.00009802744,0.0003420065,0.0002208876,0.00010321051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015518308,0.0012650809,0.0020474868,0.0013064152,0.00071960705,0.0011682637,0.0033390676,0.0019505231,0.0031071422],"category_scores_gemma":[0.005049734,0.00055516156,0.0014244677,0.0014303324,0.0015800361,0.0035328583,0.002712317,0.002919959,0.001010029],"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":[0.00044171375,0.0006476785,0.0030833257,0.0003709698,0.0002497409,0.00023826126,0.0003619487,0.22679229,0.011973407,0.0204326,0.009638577,0.72576946],"study_design_scores_gemma":[0.000017106076,0.00008954416,0.00044617357,0.00002028936,0.000024794288,0.000094046365,0.000049631228,0.9735979,0.0025110778,0.02198813,0.0011362636,0.000024921354],"about_ca_topic_score_codex":0.005012614,"about_ca_topic_score_gemma":0.005685182,"teacher_disagreement_score":0.005012614,"about_ca_system_score_codex":0.0010818524,"about_ca_system_score_gemma":0.0012616562,"threshold_uncertainty_score":0.010394394},"labels":[],"label_agreement":null},{"id":"W4321792452","doi":"10.1016/j.neunet.2023.02.023","title":"Episodic task agnostic contrastive training for multi-task learning","year":2023,"lang":"en","type":"article","venue":"Neural Networks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Vector Institute; Western University","funders":"National Key Research and Development Program of China; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Multi-task learning; Machine learning; Task (project management); Embedding; Feature learning; Transfer of learning","score_opus":0.06642584105792725,"score_gpt":0.2973511286292233,"score_spread":0.23092528757129605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321792452","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023483563,0.00065504224,0.9732289,0.00019180475,0.00008975512,0.000042888045,0.00009423388,0.00094583986,0.0012681092],"genre_scores_gemma":[0.73844856,0.00040135474,0.2530618,0.000432035,0.00015011914,0.00017839657,0.00060900475,0.00024198847,0.0064767413],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995431,0.00013740125,0.000020559684,0.00017098623,0.000059097176,0.000068892696],"domain_scores_gemma":[0.9986406,0.00084812136,0.00006617009,0.00022789887,0.00013137638,0.0000857982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016467199,0.0010080972,0.0012385926,0.000503408,0.00041338746,0.0006816568,0.002372307,0.0020529022,0.0023492598],"category_scores_gemma":[0.0033406646,0.0005874878,0.0006285632,0.00060926925,0.0007552512,0.0018072271,0.0018803262,0.0030051386,0.0006561255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066584116,0.00057219533,0.001254294,0.00025221147,0.00025964912,0.0001726969,0.00010715838,0.3349795,0.03489126,0.016639717,0.0070856903,0.60311973],"study_design_scores_gemma":[0.000011661726,0.00006279159,0.00022184975,0.0000073650517,0.000013939486,0.000032441665,0.000006771416,0.9871432,0.0040767253,0.007902313,0.0005120933,0.00000884221],"about_ca_topic_score_codex":0.0024983801,"about_ca_topic_score_gemma":0.0047598258,"teacher_disagreement_score":0.0024983801,"about_ca_system_score_codex":0.0006221436,"about_ca_system_score_gemma":0.0006727746,"threshold_uncertainty_score":0.008708775},"labels":[],"label_agreement":null},{"id":"W4322753738","doi":"10.1016/j.patcog.2023.109474","title":"Fourier-based augmentation with applications to domain generalization","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"National Major Science and Technology Projects of China","keywords":"Generalization; Fourier transform; Computer science; Domain (mathematical analysis); Frequency domain; Fourier series; Artificial intelligence; Fourier domain; Semantics (computer science); Algorithm; Mathematics; Computer vision; Mathematical analysis","score_opus":0.04440216646547513,"score_gpt":0.2817551091962559,"score_spread":0.23735294273078078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322753738","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":[0.0047645126,0.00026115714,0.9934754,0.000099528166,0.00004219822,0.00001644899,0.00004688098,0.00051316695,0.0007807661],"genre_scores_gemma":[0.2531679,0.001081951,0.7369416,0.0002270223,0.00020861784,0.000141889,0.0005555012,0.00029160723,0.007383991],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995665,0.00013174422,0.000025403917,0.00012982223,0.00010897238,0.00003753188],"domain_scores_gemma":[0.9986002,0.00064265,0.00007617102,0.0004205137,0.00019938326,0.000061033297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009986679,0.0006154962,0.001035622,0.00080084556,0.00037978875,0.0007419834,0.0013827552,0.0010477124,0.0030287276],"category_scores_gemma":[0.0035029058,0.00040807074,0.0009020405,0.0010533866,0.00096406013,0.0017740441,0.0021491917,0.0019481027,0.0010726056],"study_design_candidate":"bench_or_experimental","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":[0.00023211498,0.00022594756,0.00065282494,0.00019560344,0.00008935296,0.00011269438,0.00012018195,0.20705253,0.036025576,0.05516216,0.0055296742,0.69460136],"study_design_scores_gemma":[0.000006128457,0.000030679625,0.00018172544,0.000009373387,0.000010374117,0.00007057825,0.000010295241,0.96892345,0.0054776547,0.023395695,0.0018720191,0.000011944373],"about_ca_topic_score_codex":0.0017129716,"about_ca_topic_score_gemma":0.0022451987,"teacher_disagreement_score":0.0030287276,"about_ca_system_score_codex":0.00030881967,"about_ca_system_score_gemma":0.0006404396,"threshold_uncertainty_score":0.010132074},"labels":[],"label_agreement":null},{"id":"W4323645989","doi":"10.1109/fnwf55208.2022.00062","title":"Efficient Transfer Learning in 6G","year":2022,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Transfer of learning; Artificial neural network; Big data; Artificial intelligence; Transfer (computing); Power (physics); Machine learning; Data mining","score_opus":0.015647196825994615,"score_gpt":0.22846752242591725,"score_spread":0.21282032559992264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323645989","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":[0.12231223,0.0006702778,0.86138755,0.0011870844,0.00018976719,0.00016520076,0.0001953982,0.0040932056,0.009799332],"genre_scores_gemma":[0.9326518,0.00015251459,0.06341896,0.0002757335,0.000050829203,0.00007367279,0.0001884328,0.0000700228,0.0031180168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918896,0.00019110022,0.000029767503,0.00015594765,0.00023673644,0.00019746427],"domain_scores_gemma":[0.99870443,0.00058148697,0.000087821536,0.0003067399,0.00024300949,0.00007653323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012287017,0.00069886446,0.0007064429,0.0005209429,0.0006559251,0.0010913435,0.0015269832,0.0012897784,0.0033334326],"category_scores_gemma":[0.004498448,0.00026134402,0.0003083275,0.000805829,0.0009975734,0.0026169852,0.0018539161,0.0014357215,0.0009650519],"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":[0.00031867292,0.00022915716,0.00196339,0.000090732734,0.00004195601,0.00028921195,0.00015792546,0.751623,0.00767658,0.016974362,0.0059751063,0.21465991],"study_design_scores_gemma":[0.000009881473,0.000050302715,0.00022933377,0.000005215241,0.0000037208572,0.0000504026,0.00003149671,0.98562753,0.0022880393,0.010701711,0.0009945587,0.00000768488],"about_ca_topic_score_codex":0.0053151436,"about_ca_topic_score_gemma":0.004545708,"teacher_disagreement_score":0.0053151436,"about_ca_system_score_codex":0.0011900052,"about_ca_system_score_gemma":0.00085872505,"threshold_uncertainty_score":0.011151373},"labels":[],"label_agreement":null},{"id":"W4323817659","doi":"10.1101/2023.03.08.531734","title":"Class-imbalanced Unsupervised and Semi-Supervised Domain Adaptation for Histopathology Images","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Waterloo","funders":"","keywords":"Computer science; Leverage (statistics); Domain adaptation; Artificial intelligence; Transfer of learning; Labeled data; Domain (mathematical analysis); Machine learning; Adaptation (eye); Pattern recognition (psychology); Class (philosophy); Supervised learning; Classifier (UML); Mathematics; Artificial neural network","score_opus":0.027109451605227333,"score_gpt":0.2364663121081186,"score_spread":0.20935686050289126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323817659","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":[0.12514284,0.0005179636,0.8711819,0.00032077468,0.00007481633,0.00011302931,0.00020683116,0.0014761604,0.0009655627],"genre_scores_gemma":[0.78911054,0.00025924493,0.20627299,0.00027141624,0.000101062935,0.00016478657,0.0010511037,0.00015046545,0.002618532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989795,0.00039206067,0.000041545674,0.00032735066,0.00017734058,0.00008211644],"domain_scores_gemma":[0.99794096,0.00085581694,0.00021996748,0.00045157972,0.00040472354,0.00012696332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002705456,0.00066723657,0.0007647233,0.0012092832,0.00037382214,0.00069665676,0.0013260841,0.0012239908,0.0005585047],"category_scores_gemma":[0.0037558957,0.00030475447,0.0008887067,0.00083347847,0.00095132395,0.0010800266,0.0010611927,0.0013097591,0.00039630858],"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":[0.00047283826,0.00042539844,0.007686514,0.000177535,0.0001896458,0.0002730083,0.00025858646,0.57438546,0.04712758,0.00377772,0.0045957314,0.3606299],"study_design_scores_gemma":[0.0000064006526,0.000032688953,0.0011927981,0.000005729487,0.0000074052914,0.00006243489,0.000020167236,0.9883315,0.007505525,0.002387966,0.00043721634,0.000010111963],"about_ca_topic_score_codex":0.001881678,"about_ca_topic_score_gemma":0.0021632193,"teacher_disagreement_score":0.002705456,"about_ca_system_score_codex":0.0007066786,"about_ca_system_score_gemma":0.00057670876,"threshold_uncertainty_score":0.014307976},"labels":[],"label_agreement":null},{"id":"W4327664521","doi":"10.1109/tmm.2023.3257566","title":"Federated Adversarial Domain Hallucination for Privacy-Preserving Domain Generalization","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"Science and Technology Commission of Shanghai Municipality","keywords":"Computer science; Domain (mathematical analysis); Artificial intelligence; Entropy (arrow of time); Generalization; Segmentation; Machine learning; Deep learning; Pattern recognition (psychology); Mathematics","score_opus":0.030672194975276946,"score_gpt":0.2805723767357126,"score_spread":0.24990018176043566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327664521","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022332812,0.00025872834,0.9755786,0.0002417165,0.000026465395,0.00003118386,0.0000651008,0.0005672434,0.0008981525],"genre_scores_gemma":[0.89248526,0.00030106193,0.10339385,0.00045421624,0.00006450523,0.00010165241,0.000289945,0.000103624414,0.0028060146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988393,0.000440356,0.000048704045,0.00027402784,0.00026958602,0.00012804878],"domain_scores_gemma":[0.9969963,0.0013661452,0.00028300734,0.0009876194,0.00023027278,0.00013678307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024084179,0.0009894529,0.0012649887,0.00052424346,0.00043994296,0.00083904207,0.0016215947,0.0012228069,0.0013282943],"category_scores_gemma":[0.0061204685,0.00034948858,0.0009455826,0.0005841127,0.001773829,0.0025417372,0.003001542,0.0023964162,0.00037935292],"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":[0.00030094807,0.00011438271,0.0012424436,0.000082898594,0.000109241475,0.00025506018,0.00013082453,0.8607269,0.0075130933,0.019905077,0.002567828,0.10705124],"study_design_scores_gemma":[0.000008205966,0.00003836071,0.000098240795,0.000005897748,0.0000071948252,0.00007460206,0.000014908835,0.9848208,0.002784056,0.011686794,0.0004535641,0.0000074193495],"about_ca_topic_score_codex":0.0013251436,"about_ca_topic_score_gemma":0.001187974,"teacher_disagreement_score":0.0024084179,"about_ca_system_score_codex":0.00082199817,"about_ca_system_score_gemma":0.00083733926,"threshold_uncertainty_score":0.012737095},"labels":[],"label_agreement":null},{"id":"W4360595145","doi":"10.1109/tnnls.2022.3227267","title":"Tensor-Empowered Adaptive Learning for Few-Shot Streaming Tasks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Tensor (intrinsic definition); Task (project management); Artificial intelligence; Streaming algorithm; Adaptation (eye); Scratch; Dependency (UML); Machine learning; Mathematics; Upper and lower bounds","score_opus":0.03811174194860487,"score_gpt":0.2646603826212851,"score_spread":0.22654864067268024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360595145","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":[0.022167157,0.0005343164,0.9751765,0.000174363,0.00007855342,0.00005910576,0.00009084252,0.0009965963,0.00072262017],"genre_scores_gemma":[0.5901713,0.00066167966,0.40361163,0.00033212488,0.00021855888,0.00017970448,0.00081644213,0.0002471902,0.0037613437],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943095,0.00013773111,0.000038729177,0.00019842625,0.00012320459,0.00007098044],"domain_scores_gemma":[0.9986278,0.0004907901,0.00013390364,0.00026507938,0.00034831386,0.00013414724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014972142,0.0011479701,0.0011025817,0.0007113266,0.00047702805,0.00074076257,0.0016969007,0.0010696464,0.0015451161],"category_scores_gemma":[0.00463619,0.00037722307,0.0007589119,0.0009318388,0.00077038823,0.0026001057,0.0014981555,0.0024245773,0.00053612306],"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":[0.00030352737,0.0002967315,0.002398846,0.0002578042,0.00012005271,0.0001261145,0.00026206637,0.48912898,0.023956915,0.010811276,0.005773871,0.46656376],"study_design_scores_gemma":[0.000004142326,0.000027493983,0.00017905621,0.0000031368959,0.000005178461,0.000013298506,0.0000103223265,0.99482334,0.0015951521,0.0029676037,0.0003649703,0.0000061730484],"about_ca_topic_score_codex":0.006313772,"about_ca_topic_score_gemma":0.006836188,"teacher_disagreement_score":0.006313772,"about_ca_system_score_codex":0.00080379023,"about_ca_system_score_gemma":0.0011954079,"threshold_uncertainty_score":0.0125540495},"labels":[],"label_agreement":null},{"id":"W4361228676","doi":"10.1016/j.neunet.2023.03.023","title":"On the value of label and semantic information in domain generalization","year":2023,"lang":"en","type":"article","venue":"Neural Networks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Vector Institute; Western University","funders":"National Key Research and Development Program of China; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Generalization; Conditional probability distribution; Domain (mathematical analysis); Artificial intelligence; Feature (linguistics); Marginal distribution; Machine learning; Baseline (sea); Process (computing); Data mining; Mathematics; Random variable","score_opus":0.016292533781112273,"score_gpt":0.2369832622420537,"score_spread":0.22069072846094143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361228676","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":[0.09787005,0.0040605417,0.89045084,0.0036203715,0.00013396435,0.000072809664,0.00021222763,0.0003709254,0.0032083015],"genre_scores_gemma":[0.87467086,0.0018742598,0.11822972,0.00097096007,0.00041964496,0.00009547213,0.00037156296,0.00014977535,0.003217714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962924,0.0022622843,0.00017141076,0.0007104338,0.00038902694,0.00017441854],"domain_scores_gemma":[0.945478,0.04828491,0.00097031647,0.003139414,0.0013292645,0.0007980944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011453325,0.0012141302,0.0026662888,0.0017164774,0.0010610375,0.0019039668,0.003099041,0.00438611,0.0016075331],"category_scores_gemma":[0.038971722,0.000940364,0.0011074666,0.0013764245,0.0050668134,0.010421216,0.0041726967,0.0059482376,0.00019982665],"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":[0.0009666954,0.0005141654,0.005592929,0.00038181822,0.00050762465,0.0001865494,0.00036888837,0.63450766,0.003641263,0.12815309,0.0050877593,0.22009154],"study_design_scores_gemma":[0.000018406363,0.000037664788,0.00044899934,0.000023773102,0.00003367015,0.0000216685,0.000024000952,0.9050915,0.0005012452,0.093606144,0.00017659672,0.000016406875],"about_ca_topic_score_codex":0.00597164,"about_ca_topic_score_gemma":0.005221859,"teacher_disagreement_score":0.011453325,"about_ca_system_score_codex":0.0016952899,"about_ca_system_score_gemma":0.0009487506,"threshold_uncertainty_score":0.06057173},"labels":[],"label_agreement":null},{"id":"W4361988503","doi":"10.1007/978-3-031-30047-9_10","title":"Diffusion Transport Alignment","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Domain (mathematical analysis); Domain adaptation; Process (computing); Diffusion map; Data mining; Manifold (fluid mechanics); Exploit; Diffusion; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Algorithm; Nonlinear dimensionality reduction; Dimensionality reduction; Mathematics","score_opus":0.022354449406243863,"score_gpt":0.24346329672274458,"score_spread":0.2211088473165007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361988503","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066218474,0.0011411787,0.91952664,0.000835492,0.001059106,0.00013143501,0.0014378856,0.005849358,0.063397124],"genre_scores_gemma":[0.21991271,0.0018239482,0.5493055,0.00076441537,0.00059921766,0.00036611577,0.008351528,0.0069587803,0.21191777],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995029,0.000068428635,0.000022958786,0.00023251769,0.0001222481,0.000050846505],"domain_scores_gemma":[0.9993954,0.00011810416,0.000031832136,0.00024970766,0.0001506047,0.00005436416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067563006,0.0011270762,0.001258628,0.001183286,0.0012416779,0.0022335972,0.0012621507,0.0019341275,0.038858894],"category_scores_gemma":[0.0021083772,0.000649871,0.00081271084,0.0015098815,0.00073027296,0.0029201491,0.0023590513,0.002346117,0.023895578],"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":[0.00032192332,0.00014080765,0.00044960988,0.0003163354,0.00010343488,0.00018921061,0.00014170728,0.063506,0.033550486,0.3587202,0.0790399,0.46352038],"study_design_scores_gemma":[0.00003991279,0.000076383054,0.0004991562,0.00007914619,0.000048411373,0.00033724483,0.00009927611,0.57460237,0.037161995,0.24419683,0.14278696,0.00007233692],"about_ca_topic_score_codex":0.0021111679,"about_ca_topic_score_gemma":0.0024412575,"teacher_disagreement_score":0.038858894,"about_ca_system_score_codex":0.00090803276,"about_ca_system_score_gemma":0.0010517591,"threshold_uncertainty_score":0.12999594},"labels":[],"label_agreement":null},{"id":"W4365420502","doi":"10.1007/978-3-031-30105-6_10","title":"An Effective Ensemble Model Related to Incremental Learning in Neural Machine Translation","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 British Columbia","funders":"","keywords":"Computer science; Forgetting; Machine translation; Artificial intelligence; Machine learning; Artificial neural network; Task (project management); Translation (biology); Incremental learning; Ensemble learning; Field (mathematics); Deep learning; Engineering","score_opus":0.021974915583850233,"score_gpt":0.27290528381608536,"score_spread":0.25093036823223513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365420502","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":[0.01200498,0.0008995044,0.9839348,0.00023013118,0.00014824318,0.000030197783,0.00008309141,0.00041527604,0.0022538106],"genre_scores_gemma":[0.45284924,0.0016733819,0.52623004,0.00041860668,0.0006087689,0.00029119494,0.0011463431,0.00044260928,0.016339835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993655,0.00025002498,0.00004089779,0.00015028684,0.00013615536,0.000057179597],"domain_scores_gemma":[0.9981012,0.0011110505,0.00007493397,0.0002788926,0.00038043776,0.00005352067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013988288,0.00059350365,0.0012943649,0.00068393245,0.0005760358,0.0008795296,0.0016600768,0.0013159704,0.0034098981],"category_scores_gemma":[0.00418741,0.00037723366,0.0007810653,0.0014855915,0.00047563348,0.0027411517,0.0013955168,0.0019572666,0.0009947328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016519593,0.0002113663,0.0009883896,0.00014946406,0.00017571486,0.00012690926,0.00012980365,0.4286404,0.0055916198,0.08591365,0.0100858705,0.4678216],"study_design_scores_gemma":[0.000002610754,0.00002188755,0.00007602664,0.0000041093263,0.000013961352,0.000024434845,0.000004200194,0.982357,0.0005419466,0.016185472,0.0007637397,0.000004572103],"about_ca_topic_score_codex":0.0019690637,"about_ca_topic_score_gemma":0.002782126,"teacher_disagreement_score":0.0034098981,"about_ca_system_score_codex":0.00040376125,"about_ca_system_score_gemma":0.0006398851,"threshold_uncertainty_score":0.0114071965},"labels":[],"label_agreement":null},{"id":"W4365441057","doi":"10.48550/arxiv.2304.04858","title":"Simulated Annealing in Early Layers Leads to Better Generalization","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Initialization; Computer science; Margin (machine learning); Artificial intelligence; Forgetting; Benchmark (surveying); Gradient descent; Transfer of learning; Machine learning; Generalization; Artificial neural network; Mathematics","score_opus":0.10599785397843371,"score_gpt":0.2160249857094276,"score_spread":0.11002713173099389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365441057","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":[0.09777579,0.0011570185,0.88993835,0.00078005745,0.00019073382,0.0001129078,0.00013005057,0.005017761,0.0048973337],"genre_scores_gemma":[0.77956885,0.00045368643,0.21342017,0.00062704505,0.00009156535,0.00018148226,0.00047905283,0.00068877474,0.0044894144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924266,0.00018915582,0.000051530078,0.00029103737,0.00012026434,0.000105268526],"domain_scores_gemma":[0.9970714,0.001446002,0.00017427946,0.0008450658,0.0003498352,0.000113507835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001961178,0.0017452203,0.0017298572,0.0007806713,0.0007563395,0.0011819262,0.001831484,0.0018751357,0.0038160125],"category_scores_gemma":[0.009073368,0.0008093497,0.001344521,0.00046280006,0.0010695191,0.0033881147,0.0014553949,0.0030887623,0.0012314797],"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":[0.00023577672,0.00020963876,0.002989829,0.00018770546,0.00016858963,0.00013645669,0.00020720348,0.81182325,0.012895758,0.007977076,0.004532788,0.15863593],"study_design_scores_gemma":[0.00000983446,0.00003990804,0.00023487536,0.0000107306805,0.000012187724,0.00002569734,0.0000113237775,0.9907857,0.0032076743,0.005080847,0.00057278323,0.000008369864],"about_ca_topic_score_codex":0.0061031156,"about_ca_topic_score_gemma":0.010031044,"teacher_disagreement_score":0.0061031156,"about_ca_system_score_codex":0.0011030345,"about_ca_system_score_gemma":0.0013101277,"threshold_uncertainty_score":0.012765825},"labels":[],"label_agreement":null},{"id":"W4366307908","doi":"10.1109/ictai56018.2022.00091","title":"On Domain Generalization for Batched Prediction: the Benefit of Contextual Adversarial Training","year":2022,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Key Research and Development Program of China","keywords":"Computer science; Generalization; Adversarial system; Domain (mathematical analysis); Context (archaeology); Machine learning; Artificial intelligence; Training set; Mathematics","score_opus":0.03438473081869904,"score_gpt":0.24771332209967492,"score_spread":0.21332859128097587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366307908","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":[0.023720779,0.00092244917,0.972669,0.00046774783,0.000058203357,0.00004758409,0.00005695819,0.00048461303,0.0015727066],"genre_scores_gemma":[0.7605143,0.0011194854,0.23264173,0.0008963778,0.00030525302,0.00016774969,0.00036886655,0.00023504705,0.0037512907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853766,0.0007178377,0.000047597074,0.00037872733,0.00020675916,0.00011140212],"domain_scores_gemma":[0.9931392,0.004745345,0.00026031223,0.0013045463,0.0003607583,0.0001897907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040237163,0.0015764672,0.0017615325,0.00047795245,0.00060177397,0.00083233323,0.002311444,0.0018927924,0.0015354705],"category_scores_gemma":[0.013084472,0.00053207227,0.00070692686,0.00064847706,0.001779002,0.0033489957,0.003168383,0.0035536503,0.0004269429],"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":[0.00036496655,0.00013052125,0.0014188236,0.00009557237,0.000095764604,0.00012807512,0.00017273406,0.8659108,0.0052713254,0.02281224,0.002479622,0.10111955],"study_design_scores_gemma":[0.0000061737765,0.000042950247,0.00012367024,0.000008202906,0.000009306579,0.000025720048,0.00001022548,0.98983103,0.00078585744,0.008803771,0.0003457158,0.0000073421274],"about_ca_topic_score_codex":0.003925187,"about_ca_topic_score_gemma":0.0034613097,"teacher_disagreement_score":0.0040237163,"about_ca_system_score_codex":0.0007899334,"about_ca_system_score_gemma":0.00097406283,"threshold_uncertainty_score":0.021279693},"labels":[],"label_agreement":null},{"id":"W4366677440","doi":"10.1109/iccicc57084.2022.10101488","title":"Stochastic Sensitivity Regularized Autoencoder for Robust Feature Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Autoencoder; Regularization (linguistics); Encoder; Artificial intelligence; Computer science; Covariance; Robustness (evolution); Pattern recognition (psychology); Algorithm; Mathematics; Mathematical optimization; Deep learning","score_opus":0.02153710793094147,"score_gpt":0.23404845639898464,"score_spread":0.21251134846804318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366677440","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004789658,0.00011809758,0.99424356,0.000057728117,0.000018804973,0.00001220487,0.000031390395,0.0002520826,0.0004764226],"genre_scores_gemma":[0.51921153,0.00044933642,0.4745484,0.00033720373,0.00009297968,0.00016095108,0.0004354692,0.00019703801,0.004567022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950993,0.00013182762,0.00002532935,0.00012209702,0.00017224615,0.000038504848],"domain_scores_gemma":[0.99938905,0.00028467385,0.000055957542,0.00010990825,0.00013677415,0.00002362487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009691632,0.0007616179,0.0008093366,0.00043073343,0.00020803517,0.00047545327,0.0007990272,0.00092011853,0.0010764981],"category_scores_gemma":[0.0025206807,0.00038328374,0.0007127642,0.00043105503,0.00074104744,0.00094073097,0.0008644274,0.001399447,0.0004222053],"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":[0.000067070934,0.0000459878,0.0003398346,0.00007769702,0.00009668803,0.00008405242,0.000042008036,0.86271316,0.021281565,0.0197613,0.0014412907,0.09404937],"study_design_scores_gemma":[0.0000014830574,0.000011869965,0.00005263015,0.0000022557515,0.0000033995668,0.000013287356,9.719397e-7,0.99478674,0.0019196714,0.0029072533,0.00029625968,0.0000042065203],"about_ca_topic_score_codex":0.0017702837,"about_ca_topic_score_gemma":0.0018369718,"teacher_disagreement_score":0.0017702837,"about_ca_system_score_codex":0.0005580742,"about_ca_system_score_gemma":0.00072216266,"threshold_uncertainty_score":0.005125463},"labels":[],"label_agreement":null},{"id":"W4366850523","doi":"10.1145/3543507.3583457","title":"CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Computer science; Artificial intelligence; Conceptual clustering; Document clustering; Correlation clustering; Canopy clustering algorithm; CURE data clustering algorithm; Brown clustering; Representation (politics); Machine learning; Feature learning; Data mining; Pattern recognition (psychology)","score_opus":0.05283188345815129,"score_gpt":0.32285545194671716,"score_spread":0.2700235684885659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366850523","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":[0.0025614132,0.00020081126,0.99467695,0.00008874716,0.00002778874,0.000093073206,0.00014058381,0.0016637149,0.0005468602],"genre_scores_gemma":[0.093705356,0.00031462646,0.89542973,0.0004385472,0.00013009824,0.00070581003,0.002553212,0.00067610684,0.0060464963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99737334,0.00063102355,0.0001490048,0.0008753842,0.000713898,0.0002574766],"domain_scores_gemma":[0.9972052,0.00092981645,0.00025034416,0.00047248983,0.0009836435,0.00015842979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023510677,0.0017362126,0.0017819935,0.003574763,0.0012759519,0.001997995,0.0059750997,0.0026860994,0.0028461707],"category_scores_gemma":[0.00766028,0.00073906075,0.0017671612,0.0033275778,0.0015723071,0.0033334517,0.0029634964,0.0033949292,0.002989473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036016168,0.0003738739,0.0023887071,0.0004400902,0.00021093196,0.0002499076,0.00075833144,0.34703892,0.019614061,0.031098867,0.018897494,0.5785686],"study_design_scores_gemma":[0.0000195343,0.00005113184,0.00022359769,0.000015157284,0.000015542528,0.000059989736,0.000044486053,0.9773032,0.0040680184,0.014338052,0.0038335426,0.00002775423],"about_ca_topic_score_codex":0.00943477,"about_ca_topic_score_gemma":0.017497543,"teacher_disagreement_score":0.00943477,"about_ca_system_score_codex":0.0022931516,"about_ca_system_score_gemma":0.0030416918,"threshold_uncertainty_score":0.018759668},"labels":[],"label_agreement":null},{"id":"W4372260056","doi":"10.1109/icassp49357.2023.10095934","title":"On Weighted Cross-Entropy for Label-Imbalanced Separable Data: An Algorithmic-Stability Study","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Early stopping; Generalization error; Logarithm; Cross entropy; Gradient descent; Conjecture; Mathematics; Algorithm; Regularization (linguistics); Applied mathematics; Stability (learning theory); Artificial intelligence; Machine learning; Pattern recognition (psychology); Artificial neural network; Discrete mathematics","score_opus":0.10968118709862464,"score_gpt":0.38560516618731633,"score_spread":0.2759239790886917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4372260056","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":[0.06383059,0.0011523452,0.93123657,0.0013269447,0.000050184528,0.0001053594,0.00013875222,0.00028254022,0.0018766166],"genre_scores_gemma":[0.8301689,0.0013929219,0.16125298,0.0009643231,0.0003667707,0.00038415802,0.0008961566,0.0004706169,0.004103105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961964,0.0015108023,0.00020599287,0.0009223472,0.00086094014,0.00030351785],"domain_scores_gemma":[0.9400509,0.047713216,0.003149307,0.0044691754,0.003494683,0.0011227862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016455537,0.0018822154,0.0018407921,0.002768454,0.0011472163,0.002629002,0.00216204,0.002336346,0.001999808],"category_scores_gemma":[0.075081274,0.0007560095,0.0012890538,0.0014353393,0.00562247,0.007940386,0.0053849965,0.004831033,0.00050979387],"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":[0.0005432348,0.0002724711,0.01376546,0.0003697925,0.00022700081,0.00035156863,0.00069429,0.66417485,0.006931398,0.22874495,0.0028128466,0.08111218],"study_design_scores_gemma":[0.0000129011205,0.00009187106,0.0010144411,0.000046853384,0.000017139328,0.000072666284,0.000036273574,0.9125006,0.0015952217,0.08418962,0.00039927312,0.00002311069],"about_ca_topic_score_codex":0.0018933807,"about_ca_topic_score_gemma":0.0016191016,"teacher_disagreement_score":0.016455537,"about_ca_system_score_codex":0.0024645997,"about_ca_system_score_gemma":0.0015312749,"threshold_uncertainty_score":0.08702618},"labels":[],"label_agreement":null},{"id":"W4375957737","doi":"10.48550/arxiv.2305.04106","title":"On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Robustness (evolution); Encoder; Software; Artificial intelligence; Machine learning; Code (set theory); Language model; Downstream (manufacturing); Source code; Programming language","score_opus":0.11166014822199442,"score_gpt":0.23619844507556412,"score_spread":0.1245382968535697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375957737","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":[0.061390348,0.0014854403,0.9308988,0.0014371332,0.0001222971,0.00009754887,0.000118970216,0.0022603339,0.0021891405],"genre_scores_gemma":[0.82270455,0.001581395,0.16758363,0.0013791549,0.00020223853,0.00029009944,0.0005694717,0.0005076122,0.005181841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983553,0.0004880661,0.00010732024,0.0005805776,0.0003108967,0.00015774873],"domain_scores_gemma":[0.984574,0.011412704,0.00068148656,0.0019331727,0.0010647848,0.00033388243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005131579,0.0023333358,0.0012054472,0.0008680294,0.00074437645,0.0014591384,0.003032609,0.0020524168,0.0017326996],"category_scores_gemma":[0.025582734,0.0011607065,0.0011685397,0.0007722221,0.0026112662,0.0045131957,0.0027199257,0.006475526,0.0009123954],"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":[0.00029295226,0.00020527102,0.0044339155,0.00017985687,0.00017912971,0.00027232568,0.00037585676,0.74595946,0.007088678,0.013037217,0.0028785144,0.2250968],"study_design_scores_gemma":[0.000010497709,0.000063713516,0.0003025131,0.000022949076,0.00001493852,0.000043713884,0.000015726528,0.99134594,0.0020391748,0.005741098,0.00038626188,0.000013482487],"about_ca_topic_score_codex":0.012331308,"about_ca_topic_score_gemma":0.015244548,"teacher_disagreement_score":0.012331308,"about_ca_system_score_codex":0.0015505295,"about_ca_system_score_gemma":0.002220168,"threshold_uncertainty_score":0.02713865},"labels":[],"label_agreement":null},{"id":"W4380993971","doi":"10.48550/arxiv.2306.08838","title":"Differentially Private Domain Adaptation with Theoretical Guarantees","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; National Science Foundation","keywords":"Leverage (statistics); Domain adaptation; Computer science; Domain (mathematical analysis); Adaptation (eye); Lipschitz continuity; Convex optimization; Regular polygon; Sample (material); Mathematical optimization; Optimization problem; Artificial intelligence; Machine learning; Algorithm; Mathematics; Classifier (UML)","score_opus":0.0669261931874121,"score_gpt":0.18712118157378657,"score_spread":0.12019498838637448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380993971","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":[0.0124705415,0.0003013941,0.98327106,0.00084248354,0.000044874596,0.00008760217,0.0001441701,0.0008448663,0.00199303],"genre_scores_gemma":[0.6705268,0.0007635683,0.31784067,0.0010542766,0.0003141633,0.0005956742,0.0008205594,0.00040568027,0.0076785707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99501497,0.0020242683,0.00019564688,0.0012191868,0.001191868,0.00035414944],"domain_scores_gemma":[0.97484654,0.014504081,0.0011120659,0.007899185,0.0011320312,0.00050614146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070552584,0.0013855215,0.0020974292,0.0008125513,0.0012996899,0.002489538,0.00343674,0.0028961417,0.0034046797],"category_scores_gemma":[0.035090525,0.0008143228,0.0012225856,0.0015529749,0.002860337,0.007065585,0.007422254,0.005856542,0.0019411488],"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":[0.0010733041,0.0005120469,0.0025302947,0.00033613716,0.00015827446,0.0002440798,0.0004401434,0.544107,0.008764436,0.2303057,0.011102467,0.20042616],"study_design_scores_gemma":[0.00005387228,0.000058047077,0.00025108657,0.000016191289,0.000014689047,0.00012201405,0.000036009267,0.8393017,0.0032155619,0.15526451,0.0016459154,0.000020394014],"about_ca_topic_score_codex":0.00095180987,"about_ca_topic_score_gemma":0.0010011175,"teacher_disagreement_score":0.0070552584,"about_ca_system_score_codex":0.002241865,"about_ca_system_score_gemma":0.0026042936,"threshold_uncertainty_score":0.03731221},"labels":[],"label_agreement":null},{"id":"W4381952341","doi":"10.31234/osf.io/ps5aq","title":"Novel Concentric-Circle Technique Interrogates Implicit Category Learning","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leverage (statistics); Boundary (topology); Representation (politics); Computer science; Quadratic equation; Concentric; Artificial intelligence; Mathematics; Geometry; Mathematical analysis","score_opus":0.044619687136768595,"score_gpt":0.2917461303530237,"score_spread":0.24712644321625513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381952341","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":[0.11877276,0.00011913298,0.8708762,0.00065240316,0.00004421102,0.00007981203,0.00014364507,0.00056319346,0.008748664],"genre_scores_gemma":[0.7816396,0.000065367174,0.21607423,0.00011881649,0.000023688674,0.000107464046,0.00018253944,0.00012116525,0.0016672565],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909306,0.00041597162,0.000028401026,0.00025681802,0.0001632863,0.000042489355],"domain_scores_gemma":[0.991651,0.00492904,0.00055435067,0.0021945697,0.00044131893,0.00022975182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019824395,0.000305406,0.00039422783,0.00066679856,0.0006119553,0.0008598435,0.0017936486,0.0010413652,0.004277654],"category_scores_gemma":[0.014571839,0.00026089465,0.0005048306,0.00061543577,0.0026938848,0.0040245764,0.0022644007,0.0017497495,0.0005279278],"study_design_candidate":"bench_or_experimental","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":[0.00043018904,0.00024328221,0.0067017335,0.00030432633,0.00008659121,0.00027620446,0.0036112377,0.024647802,0.03379574,0.7320936,0.0035225726,0.1942867],"study_design_scores_gemma":[0.00003704181,0.00021888656,0.0027185348,0.0000296426,0.000028088481,0.00041630113,0.00043498256,0.42175424,0.016210292,0.5508624,0.007245303,0.000044369655],"about_ca_topic_score_codex":0.00062797725,"about_ca_topic_score_gemma":0.00083074294,"teacher_disagreement_score":0.004277654,"about_ca_system_score_codex":0.00070331333,"about_ca_system_score_gemma":0.0004304752,"threshold_uncertainty_score":0.014310181},"labels":[],"label_agreement":null},{"id":"W4382141764","doi":"10.48550/arxiv.2306.13275","title":"On The Relationship Between Continual Learning and Long-Tailed Recognition","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MNIST database; Computer science; Artificial intelligence; Set (abstract data type); Machine learning; Forgetting; Context (archaeology); Function (biology); Pattern recognition (psychology); Deep learning","score_opus":0.2225420793896809,"score_gpt":0.23009479280232226,"score_spread":0.007552713412641354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382141764","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":[0.044783805,0.0022314987,0.9461417,0.0025379702,0.000092768314,0.00006788181,0.00011245743,0.00077459135,0.0032573158],"genre_scores_gemma":[0.8402488,0.0018970281,0.15044278,0.0015533649,0.00046397606,0.00026624117,0.00043938722,0.00032642458,0.0043620304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968663,0.0011268444,0.00014018064,0.0011713775,0.0004947575,0.00020048987],"domain_scores_gemma":[0.9623926,0.028093357,0.0019476723,0.004318126,0.0020999513,0.0011482445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010416112,0.0014547256,0.0014234505,0.0014391526,0.0013367865,0.0025220178,0.0030434725,0.002644046,0.0023284038],"category_scores_gemma":[0.045925345,0.00093439216,0.0006982213,0.0012666362,0.0069418955,0.009393242,0.0063387984,0.006777653,0.0007735596],"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":[0.0006693529,0.0004187561,0.016106978,0.0005049913,0.00022272191,0.00036092682,0.0010440596,0.45532608,0.0069645448,0.25594658,0.008413949,0.25402108],"study_design_scores_gemma":[0.00002013876,0.00015212636,0.0012472593,0.000049940543,0.000018645515,0.00013321976,0.000077533914,0.816624,0.0018085992,0.17880484,0.001030578,0.00003310351],"about_ca_topic_score_codex":0.0028405439,"about_ca_topic_score_gemma":0.002847087,"teacher_disagreement_score":0.010416112,"about_ca_system_score_codex":0.0021113688,"about_ca_system_score_gemma":0.0016890173,"threshold_uncertainty_score":0.055086315},"labels":[],"label_agreement":null},{"id":"W4382240129","doi":"10.1609/aaai.v37i3.25407","title":"Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Discriminator; Mutual information; Pattern recognition (psychology); Conditional probability distribution; Computer science; Artificial intelligence; Classifier (UML); Domain (mathematical analysis); Feature (linguistics); Mathematics; Machine learning; Statistics","score_opus":0.08402993613928378,"score_gpt":0.2946167539703175,"score_spread":0.21058681783103372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382240129","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":[0.040171638,0.00043239177,0.95356506,0.00022439496,0.00009507363,0.000058608806,0.00016751075,0.0028581845,0.0024271745],"genre_scores_gemma":[0.75000274,0.00021417771,0.24076767,0.00069595914,0.00011023956,0.00017490346,0.0010117535,0.0004722506,0.006550366],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987978,0.00033804192,0.000042247288,0.00043524825,0.00027740805,0.0001092668],"domain_scores_gemma":[0.99861205,0.00045779382,0.000109017834,0.00042896526,0.0002912148,0.00010098688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013165365,0.0009262806,0.0009847883,0.0007107276,0.0005450755,0.0009863648,0.0021061,0.0011374626,0.002267677],"category_scores_gemma":[0.0038203457,0.00039686318,0.00090548623,0.0008496301,0.00084425975,0.0023426064,0.0028267254,0.0027554438,0.0012515825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005628198,0.00042830218,0.004608235,0.00015387239,0.00023453319,0.00024055084,0.0004239984,0.2039925,0.032192778,0.01563397,0.011528599,0.72999984],"study_design_scores_gemma":[0.000015341684,0.00004608081,0.00069675397,0.00000828729,0.000017573784,0.000089784095,0.000049573464,0.9793108,0.0064756456,0.010828547,0.0024418922,0.000019694568],"about_ca_topic_score_codex":0.0022366832,"about_ca_topic_score_gemma":0.0028219447,"teacher_disagreement_score":0.002267677,"about_ca_system_score_codex":0.0006811325,"about_ca_system_score_gemma":0.00073673594,"threshold_uncertainty_score":0.007586181},"labels":[],"label_agreement":null},{"id":"W4382318976","doi":"10.1609/aaai.v37i9.26238","title":"MetaZSCIL: A Meta-Learning Approach for Generalized Zero-Shot Class Incremental Learning","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Meta learning (computer science); Forgetting; Computer science; Artificial intelligence; Machine learning; Class (philosophy); Task (project management); Feature (linguistics); Process (computing); Generative grammar; Sample (material); Adaptation (eye); Engineering","score_opus":0.230948496481997,"score_gpt":0.3343297526525874,"score_spread":0.10338125617059041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382318976","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":[0.019769,0.00081809936,0.9712407,0.00030376125,0.00009362213,0.00017806,0.0002804886,0.005414046,0.0019020629],"genre_scores_gemma":[0.59984964,0.00048306142,0.38982254,0.00093834574,0.00016821222,0.0005541338,0.0020504722,0.00071824464,0.0054154308],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988147,0.00023942246,0.000054661505,0.00045810727,0.00027437933,0.0001587966],"domain_scores_gemma":[0.99814343,0.0007796544,0.00014654372,0.00045682443,0.00033539557,0.00013806559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018799503,0.0019523556,0.0020759068,0.0015759966,0.0006625215,0.001525052,0.007964134,0.0023703463,0.003220102],"category_scores_gemma":[0.005078095,0.0011013189,0.0015086146,0.0013021388,0.0013672997,0.0034878517,0.0032523908,0.0030176148,0.0011468497],"study_design_candidate":"bench_or_experimental","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":[0.00029313666,0.00042507506,0.003232199,0.00031431793,0.00027581968,0.00024451595,0.0002814998,0.4518659,0.008857517,0.009248217,0.008903802,0.5160579],"study_design_scores_gemma":[0.000012774751,0.000051486957,0.00014020293,0.000009934725,0.000020858335,0.000029152929,0.000013541011,0.99162185,0.0016580577,0.0057232403,0.00070794916,0.0000110025085],"about_ca_topic_score_codex":0.006703641,"about_ca_topic_score_gemma":0.011794688,"teacher_disagreement_score":0.007964134,"about_ca_system_score_codex":0.001921761,"about_ca_system_score_gemma":0.0017990926,"threshold_uncertainty_score":0.013943374},"labels":[],"label_agreement":null},{"id":"W4382319506","doi":"10.48550/arxiv.2306.13812","title":"Maintaining Plasticity in Deep Continual Learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; DeepMind; Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"MNIST database; Backpropagation; Artificial intelligence; Computer science; Plasticity; Deep learning; Normalization (sociology); Regularization (linguistics); Machine learning; Artificial neural network; Task (project management); Engineering","score_opus":0.09071131042013264,"score_gpt":0.20102763528724266,"score_spread":0.11031632486711002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382319506","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":[0.25829864,0.0009039765,0.73095304,0.0014967351,0.00009982827,0.00007342625,0.0001470304,0.0024289107,0.005598345],"genre_scores_gemma":[0.932651,0.00028193014,0.06390341,0.00019381913,0.00004525027,0.00008367933,0.00013023152,0.00014080928,0.0025699094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941254,0.00012262972,0.000034854336,0.00019291956,0.00015546307,0.00008148405],"domain_scores_gemma":[0.9974251,0.0011462441,0.00025587727,0.00070219487,0.00030252594,0.00016819294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016490583,0.0005129828,0.0006547952,0.0005025498,0.00057731895,0.0009805766,0.001913069,0.00095379207,0.0012826616],"category_scores_gemma":[0.007841215,0.00047322238,0.00042514654,0.00046673973,0.00265256,0.0035584792,0.0025136503,0.0023236822,0.00040247905],"study_design_candidate":"bench_or_experimental","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":[0.00049709994,0.00043494726,0.0053283838,0.000287668,0.000117299875,0.00038336313,0.0006197452,0.55600697,0.05318497,0.110656746,0.0033391588,0.26914367],"study_design_scores_gemma":[0.000020324527,0.00012189299,0.0009596014,0.00002412594,0.000012996748,0.00013046092,0.000045330802,0.8678712,0.0121883685,0.11684891,0.0017517321,0.000024994351],"about_ca_topic_score_codex":0.0012818518,"about_ca_topic_score_gemma":0.0012263545,"teacher_disagreement_score":0.001913069,"about_ca_system_score_codex":0.0008080647,"about_ca_system_score_gemma":0.00078208384,"threshold_uncertainty_score":0.008721113},"labels":[],"label_agreement":null},{"id":"W4382457524","doi":"10.1609/aaai.v37i1.25150","title":"RankDNN: Learning to Rank for Few-Shot Learning","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"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":"Science and Technology Commission of Shanghai Municipality; Fudan University","keywords":"Computer science; Ranking (information retrieval); Artificial intelligence; Learning to rank; Ranking SVM; Artificial neural network; Pipeline (software); Pattern recognition (psychology); Benchmark (surveying); Feature (linguistics); Machine learning; Rank (graph theory); Margin (machine learning); Domain (mathematical analysis); Similarity (geometry); Deep learning; Image (mathematics); Mathematics","score_opus":0.1333800895186134,"score_gpt":0.33416931476963424,"score_spread":0.20078922525102083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382457524","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016130367,0.002040794,0.9615345,0.00043891458,0.000364394,0.00034725672,0.0020013922,0.012796382,0.004345909],"genre_scores_gemma":[0.32333955,0.0013886839,0.6365865,0.0015006706,0.0005164287,0.0007822272,0.013747676,0.0014565667,0.020681774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985077,0.00027216508,0.00009416535,0.00050945836,0.00044239362,0.00017413333],"domain_scores_gemma":[0.9983707,0.00051521213,0.00012596189,0.00043875215,0.00040453929,0.00014487964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018270044,0.0021917848,0.0022463303,0.0019974846,0.0008644591,0.0019096297,0.0049578818,0.0022855557,0.008358046],"category_scores_gemma":[0.007374166,0.0009170635,0.0011336321,0.0015352943,0.00093375857,0.004076856,0.0025958898,0.003229605,0.0046655345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000470894,0.00042188235,0.0023648927,0.0005190907,0.00020737502,0.00015577377,0.00011818139,0.09839203,0.006169274,0.01261083,0.04702151,0.83154815],"study_design_scores_gemma":[0.00004935998,0.00012900922,0.0003634093,0.00003665202,0.000034303794,0.000094868126,0.000033430464,0.97210735,0.004082028,0.017894011,0.005140147,0.000035399396],"about_ca_topic_score_codex":0.011248495,"about_ca_topic_score_gemma":0.018086363,"teacher_disagreement_score":0.011248495,"about_ca_system_score_codex":0.0017524685,"about_ca_system_score_gemma":0.0022860952,"threshold_uncertainty_score":0.02796042},"labels":[],"label_agreement":null},{"id":"W4382458079","doi":"10.1609/aaai.v37i1.25193","title":"Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Segmentation; Domain (mathematical analysis); Task (project management); Context (archaeology); Generalization; Artificial intelligence; Domain adaptation; Class (philosophy); Adaptation (eye); Code (set theory); Pattern recognition (psychology); Machine learning; Mathematics; Geography","score_opus":0.10056096458046826,"score_gpt":0.3156389768482556,"score_spread":0.21507801226778733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382458079","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":[0.023519931,0.000329316,0.9675894,0.00019442622,0.00007242742,0.0000824063,0.0002253558,0.0058872886,0.0020995056],"genre_scores_gemma":[0.4543956,0.0003481385,0.53553796,0.0006355646,0.00009024167,0.00029226844,0.0018065571,0.0015138551,0.0053797676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925786,0.00018307996,0.000030717354,0.00029885376,0.00013417377,0.00009530147],"domain_scores_gemma":[0.9988463,0.0004117006,0.000061242754,0.00042608593,0.00016732677,0.00008742453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015451646,0.0015887755,0.0009073235,0.0009675997,0.0005719499,0.0014052673,0.002181676,0.0015663957,0.004006016],"category_scores_gemma":[0.003565818,0.0007035,0.0012685601,0.00086703995,0.0012092936,0.0028200506,0.003652492,0.0031901728,0.002444454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059889886,0.00041487222,0.0035192294,0.00026145444,0.00018639195,0.00034143435,0.0006032951,0.28450903,0.081367776,0.022304637,0.0125055155,0.5933874],"study_design_scores_gemma":[0.000022420169,0.00005503006,0.00037847084,0.000018034552,0.000022062535,0.000117450094,0.00008579395,0.95016634,0.026393393,0.016581118,0.0061315577,0.000028414333],"about_ca_topic_score_codex":0.0032935839,"about_ca_topic_score_gemma":0.005231844,"teacher_disagreement_score":0.004006016,"about_ca_system_score_codex":0.00097100093,"about_ca_system_score_gemma":0.0011092863,"threshold_uncertainty_score":0.013401508},"labels":[],"label_agreement":null},{"id":"W4382467791","doi":"10.1609/aaai.v37i7.26012","title":"The Effect of Diversity in Meta-Learning","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Task (project management); Diversity (politics); Computer science; Distribution (mathematics); Cognitive psychology; Empirical evidence; Machine learning; Artificial intelligence; Psychology; Mathematics; Epistemology; Engineering; Sociology","score_opus":0.14419556179439486,"score_gpt":0.3180096653225486,"score_spread":0.17381410352815374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382467791","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82111496,0.006804678,0.15466389,0.0034709962,0.0002553993,0.00024978674,0.00022042301,0.00070770516,0.012512192],"genre_scores_gemma":[0.9798585,0.00037480186,0.018496454,0.00037785352,0.00012502006,0.00006510602,0.00014465494,0.00007086061,0.00048670446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9888216,0.007043626,0.00050650764,0.001920095,0.0012669304,0.00044124798],"domain_scores_gemma":[0.8659343,0.11412596,0.003579672,0.010659271,0.0028524206,0.0028483076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020563297,0.0012916243,0.0013230874,0.0012797322,0.001379356,0.003022903,0.0017641766,0.0026970895,0.0015983992],"category_scores_gemma":[0.1049333,0.00056509685,0.00077639025,0.00073610933,0.0028401646,0.008254057,0.0053624013,0.0041610324,0.00041385743],"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":[0.006747642,0.0025984712,0.10029088,0.0014925458,0.0026130981,0.00075034774,0.003856447,0.42928708,0.0397624,0.03327208,0.0040933327,0.37523565],"study_design_scores_gemma":[0.00090940704,0.0067701996,0.035251755,0.00045029022,0.0010069804,0.0011344457,0.0012874571,0.7665305,0.028697377,0.15128088,0.0064232456,0.00025738572],"about_ca_topic_score_codex":0.0009148438,"about_ca_topic_score_gemma":0.0009983834,"teacher_disagreement_score":0.020563297,"about_ca_system_score_codex":0.00093316886,"about_ca_system_score_gemma":0.0007596724,"threshold_uncertainty_score":0.10875034},"labels":[],"label_agreement":null},{"id":"W4382469210","doi":"10.1609/aaai.v37i9.26320","title":"Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Generalization; Domain (mathematical analysis); Artificial intelligence; Machine learning; Representation (politics); Mathematics","score_opus":0.1258014299904242,"score_gpt":0.32635200830429706,"score_spread":0.20055057831387285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382469210","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":[0.036086917,0.0006081133,0.95976216,0.00048015657,0.00006758309,0.00007955902,0.00024080138,0.0015978628,0.0010768616],"genre_scores_gemma":[0.59701246,0.00047850207,0.3972412,0.00072159147,0.00012369109,0.00023981689,0.0014508244,0.0002341811,0.0024976614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932075,0.00021587938,0.000031728203,0.00027719198,0.00009168681,0.00006279142],"domain_scores_gemma":[0.99836713,0.0006994865,0.000119763594,0.00056258834,0.00015118154,0.00009991461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016120533,0.0011066255,0.0011258416,0.00076505245,0.00044842137,0.00079171365,0.0021110494,0.0014271139,0.0013748333],"category_scores_gemma":[0.004237863,0.0005509064,0.0013509478,0.00086327584,0.0011838339,0.0031942786,0.0019479675,0.0027393848,0.000563403],"study_design_candidate":"bench_or_experimental","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":[0.00032290272,0.00036434128,0.0033122392,0.00018269022,0.00013337101,0.00023949314,0.00034157338,0.6442824,0.012083129,0.011894018,0.006783757,0.32006007],"study_design_scores_gemma":[0.000010549174,0.0000438862,0.0001930525,0.00000918059,0.00001025275,0.000050411836,0.00002744506,0.9858947,0.0020482312,0.010624038,0.0010779996,0.000010274265],"about_ca_topic_score_codex":0.0029557997,"about_ca_topic_score_gemma":0.0033804693,"teacher_disagreement_score":0.0029557997,"about_ca_system_score_codex":0.0006430629,"about_ca_system_score_gemma":0.0008695982,"threshold_uncertainty_score":0.008525431},"labels":[],"label_agreement":null},{"id":"W4383566581","doi":"10.1016/b978-0-12-805320-1.00008-7","title":"Mean-field inference","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"École de Technologie Supérieure","funders":"","keywords":"Conditional random field; CRFS; Inference; Computer science; Pairwise comparison; Scalability; Approximate inference; Perspective (graphical); Segmentation; Focus (optics); Artificial intelligence; Generalization; Theoretical computer science; Machine learning; Mathematics","score_opus":0.0362903397512312,"score_gpt":0.2697745164339046,"score_spread":0.2334841766826734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383566581","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013293758,0.0030919379,0.9618495,0.0008990376,0.0006495422,0.000042829084,0.00088672945,0.0060990974,0.025152087],"genre_scores_gemma":[0.07302614,0.0033038429,0.74110883,0.001165334,0.0010703211,0.00021009526,0.006840341,0.0032745495,0.17000055],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994325,0.000129238,0.000024580873,0.00025227264,0.00012383715,0.000037617076],"domain_scores_gemma":[0.99892044,0.00047840286,0.00003085352,0.0003473798,0.00017899349,0.00004394811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011793385,0.0014266798,0.0017077789,0.0014158976,0.00087215024,0.002367814,0.0025875631,0.0025555028,0.06129646],"category_scores_gemma":[0.004920864,0.0010986854,0.0014023689,0.0018842948,0.00084204716,0.0031472056,0.0018381429,0.0032059296,0.032657403],"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":[0.00007506474,0.00007864729,0.00028152028,0.0001834608,0.00011394589,0.000045316978,0.000037123646,0.045240812,0.0016380253,0.05208631,0.11765356,0.78256613],"study_design_scores_gemma":[0.000024048863,0.00002661639,0.00043569528,0.000106217,0.000050337985,0.0001271821,0.000030156381,0.656366,0.002645542,0.27381098,0.066342704,0.000034563844],"about_ca_topic_score_codex":0.008811742,"about_ca_topic_score_gemma":0.016503956,"teacher_disagreement_score":0.06129646,"about_ca_system_score_codex":0.00093191385,"about_ca_system_score_gemma":0.0012684973,"threshold_uncertainty_score":0.20505708},"labels":[],"label_agreement":null},{"id":"W4383566636","doi":"10.1016/b978-0-12-805320-1.00015-4","title":"Constrained deep networks","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"École de Technologie Supérieure","funders":"","keywords":"Prior probability; Regularization (linguistics); Artificial intelligence; Computer science; Segmentation; Context (archaeology); Deep learning; Focus (optics); Machine learning; Conditional random field; Mathematical optimization; Mathematics; Bayesian probability","score_opus":0.021777187982862573,"score_gpt":0.23475543613597807,"score_spread":0.2129782481531155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383566636","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004345014,0.015331091,0.80459595,0.0023153406,0.0013827152,0.000052875686,0.0022980368,0.007221467,0.16245748],"genre_scores_gemma":[0.08038957,0.014937709,0.26618809,0.0011378269,0.00092589715,0.00021688147,0.008309545,0.0030860398,0.6248085],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998734,0.000014671396,0.000005230349,0.000049670663,0.000043677475,0.000013356598],"domain_scores_gemma":[0.9997954,0.00006428268,0.000009307387,0.00007014829,0.000045330507,0.000015678314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002689222,0.0011579522,0.0006889326,0.00051721133,0.00028969342,0.0012788019,0.00095185346,0.0011666579,0.05203379],"category_scores_gemma":[0.0010399568,0.00052950776,0.0003960927,0.0010184646,0.00040453102,0.001664101,0.0014016825,0.0018850053,0.026972298],"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":[0.000032292188,0.00004118709,0.000120823985,0.00016918509,0.00003620142,0.00004151729,0.000024393468,0.03567391,0.0029447118,0.04701664,0.16594468,0.7479545],"study_design_scores_gemma":[0.000016214595,0.00004156843,0.0006323199,0.00021348044,0.00003978474,0.00019161307,0.000029567705,0.3654614,0.0064762644,0.24231108,0.38454935,0.000037384925],"about_ca_topic_score_codex":0.0040848306,"about_ca_topic_score_gemma":0.007963559,"teacher_disagreement_score":0.05203379,"about_ca_system_score_codex":0.00052868493,"about_ca_system_score_gemma":0.00059103564,"threshold_uncertainty_score":0.17407036},"labels":[],"label_agreement":null},{"id":"W4384697542","doi":"10.22215/etd/2023-15555","title":"Continual Learning for Classification Tasks","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial intelligence; Machine learning; Computer science; Forgetting; Regularization (linguistics); Feature learning; Feature vector; Multi-task learning; Semi-supervised learning; Classifier (UML); Feature selection; Feature (linguistics); Online machine learning; Task (project management); Engineering","score_opus":0.0483046603267039,"score_gpt":0.32157954260374094,"score_spread":0.27327488227703706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384697542","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":[0.019304896,0.0016348491,0.9694743,0.0011343969,0.00015736869,0.00015206366,0.00021315223,0.0010787902,0.0068502133],"genre_scores_gemma":[0.49906898,0.0020394581,0.48052886,0.0007394921,0.0005982984,0.0006789136,0.0012955569,0.0003501699,0.01470024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823296,0.00045910166,0.000104058774,0.00065609044,0.00042906872,0.00011872828],"domain_scores_gemma":[0.9966628,0.0018084814,0.00024812762,0.00071216683,0.00043384227,0.00013449677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028697911,0.0010563402,0.0009791693,0.000835808,0.00072434725,0.0020294315,0.00241289,0.0013715887,0.0057459166],"category_scores_gemma":[0.0076068128,0.00040295147,0.0011645528,0.0008602738,0.0014758573,0.003405953,0.0027222019,0.0040629287,0.0022751107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035577046,0.0004950334,0.0034474023,0.0006172941,0.00013133789,0.00026656143,0.0005118582,0.22273356,0.0057961512,0.19460851,0.015414511,0.555622],"study_design_scores_gemma":[0.000013978779,0.00009258341,0.00036530773,0.000042629756,0.000014143556,0.00007667657,0.00004891372,0.8728768,0.0011883967,0.11910804,0.0061529274,0.00001961789],"about_ca_topic_score_codex":0.0015044764,"about_ca_topic_score_gemma":0.0016457817,"teacher_disagreement_score":0.0057459166,"about_ca_system_score_codex":0.0012298252,"about_ca_system_score_gemma":0.0010828973,"threshold_uncertainty_score":0.019222021},"labels":[],"label_agreement":null},{"id":"W4384828687","doi":"10.1145/3539618.3591902","title":"SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; Compute Canada","keywords":"Computer science; Information retrieval; Benchmark (surveying); Question answering; Artificial intelligence; Weighting; Python (programming language); Language model; Machine learning","score_opus":0.17711231951964332,"score_gpt":0.36142357382501966,"score_spread":0.18431125430537634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384828687","genre_codex":"software","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":[0.023570705,0.0031756158,0.2922195,0.000637412,0.00073844544,0.0011841172,0.030313503,0.6357872,0.01237346],"genre_scores_gemma":[0.18394463,0.0027238994,0.56288934,0.0016113103,0.00026466735,0.0032145786,0.19343843,0.034168128,0.017745024],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99488515,0.0013310169,0.0006025544,0.00085024576,0.00194658,0.00038440587],"domain_scores_gemma":[0.99402547,0.0020915368,0.00036257974,0.0017471802,0.0014142622,0.00035891685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00702439,0.0037741547,0.0020591787,0.0045117987,0.0010268844,0.003302562,0.0069994093,0.002165701,0.017935267],"category_scores_gemma":[0.02372904,0.0013057379,0.0022834516,0.0029897445,0.0012710649,0.0058117323,0.005625881,0.0036713923,0.01924241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019435188,0.0008738277,0.0042064246,0.003911362,0.0009898192,0.00049854274,0.00034693885,0.083544,0.015011946,0.007362467,0.46138442,0.41992673],"study_design_scores_gemma":[0.0006334888,0.0010464335,0.0028353445,0.0002523454,0.00019113558,0.0006057309,0.00017731907,0.88501203,0.03149815,0.016812246,0.060646683,0.0002890865],"about_ca_topic_score_codex":0.019482577,"about_ca_topic_score_gemma":0.03130217,"teacher_disagreement_score":0.019482577,"about_ca_system_score_codex":0.0022891685,"about_ca_system_score_gemma":0.0041966047,"threshold_uncertainty_score":0.059999466},"labels":[],"label_agreement":null},{"id":"W4385213925","doi":"10.1109/tai.2023.3298297","title":"Contrastive-Enhanced Domain Generalization With Federated Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Normalization (sociology); Classifier (UML); Artificial intelligence; Generalization; Embedding; Machine learning","score_opus":0.04008017325056603,"score_gpt":0.28246220511734005,"score_spread":0.242382031866774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385213925","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":[0.029012118,0.00032407427,0.9676212,0.0001801675,0.000030527473,0.000057715075,0.00009649973,0.0019198102,0.0007578676],"genre_scores_gemma":[0.7588495,0.0001943453,0.23716229,0.0004774936,0.00006524755,0.00015249159,0.0006584826,0.00017512299,0.0022650347],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985827,0.0004328314,0.00006388265,0.00056762784,0.00024575638,0.000107207285],"domain_scores_gemma":[0.99758446,0.000786969,0.00018928114,0.0010951593,0.00024665502,0.000097462485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025220287,0.0011840183,0.0015108581,0.00086931273,0.00052132166,0.00096483296,0.0029255634,0.0015341955,0.0010369295],"category_scores_gemma":[0.0050715655,0.00040789455,0.00122616,0.0009896363,0.0013153383,0.003727424,0.003279083,0.0022027525,0.00051886257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051534444,0.00051850756,0.0039824382,0.00015989637,0.00024623412,0.00030659063,0.00031575537,0.43452707,0.015500659,0.014727791,0.0055470173,0.52365273],"study_design_scores_gemma":[0.00001786677,0.000086834654,0.0003198833,0.000007979343,0.00001986944,0.00010911353,0.000030043273,0.9790796,0.004061892,0.015317986,0.0009352019,0.000013732295],"about_ca_topic_score_codex":0.002226999,"about_ca_topic_score_gemma":0.0023860761,"teacher_disagreement_score":0.0029255634,"about_ca_system_score_codex":0.00107574,"about_ca_system_score_gemma":0.0009767872,"threshold_uncertainty_score":0.01333791},"labels":[],"label_agreement":null},{"id":"W4385322338","doi":"10.1109/iv55152.2023.10186529","title":"Domain Adaptation in LiDAR Semantic Segmentation via Hybrid Learning with Alternating Skip Connections","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Saskatchewan; Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Segmentation; Lidar; Benchmark (surveying); Domain adaptation; Artificial intelligence; Domain (mathematical analysis); Set (abstract data type); Image segmentation; Image (mathematics); Pattern recognition (psychology); Adaptation (eye); Machine learning; Natural language processing; Remote sensing; Mathematics","score_opus":0.018904593570162016,"score_gpt":0.25220264579323814,"score_spread":0.23329805222307612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385322338","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":[0.038539033,0.0006699667,0.9533656,0.00024073529,0.0000760187,0.00007865711,0.00019647961,0.0043696314,0.0024639212],"genre_scores_gemma":[0.65839714,0.00040399615,0.33099505,0.0007232508,0.00013986614,0.00018317328,0.0018742664,0.00065086706,0.0066323825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938047,0.00015479013,0.000017878096,0.00028863418,0.0000928775,0.00006525923],"domain_scores_gemma":[0.9993123,0.000278231,0.000060377974,0.00018864134,0.00010608536,0.000054263717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010122905,0.001232037,0.0011034901,0.00091229985,0.0005393138,0.00079063367,0.0023894156,0.0016126966,0.0024400651],"category_scores_gemma":[0.0021639275,0.0006276563,0.0011188592,0.0010617044,0.001121819,0.0023954269,0.0018711291,0.0020908972,0.0013584838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028510828,0.0002863664,0.0021284358,0.00022514464,0.00014717352,0.0002707161,0.0002488824,0.4659142,0.024719747,0.011815438,0.00833656,0.48562223],"study_design_scores_gemma":[0.000010718095,0.000040230036,0.00024687377,0.0000074376226,0.000010337082,0.000071638366,0.000021708542,0.98602027,0.0040974827,0.008409461,0.0010536059,0.000010182335],"about_ca_topic_score_codex":0.0035385925,"about_ca_topic_score_gemma":0.0061410684,"teacher_disagreement_score":0.0035385925,"about_ca_system_score_codex":0.0008121394,"about_ca_system_score_gemma":0.00090191927,"threshold_uncertainty_score":0.008162856},"labels":[],"label_agreement":null},{"id":"W4385338508","doi":"10.1109/tcds.2023.3299755","title":"CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in Robotics","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Waterloo","funders":"Air Force Office of Scientific Research; California Institute of Technology; National Science Foundation","keywords":"Forgetting; Computer science; Artificial intelligence; Incremental learning; Robot; Class (philosophy); Machine learning; Object (grammar); Set (abstract data type)","score_opus":0.060527478791272384,"score_gpt":0.27734878666937335,"score_spread":0.21682130787810097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385338508","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":[0.016185127,0.00055781944,0.97840273,0.0004468682,0.00010061407,0.0001431256,0.00013977356,0.0013659783,0.0026579907],"genre_scores_gemma":[0.63976866,0.00059515407,0.35159534,0.0007778632,0.00015942093,0.0005789705,0.00046864554,0.00021432352,0.005841545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994517,0.00011283978,0.000021664802,0.00017528345,0.00017137984,0.000067180554],"domain_scores_gemma":[0.9985707,0.00062020903,0.00013345551,0.00026770882,0.00029180152,0.0001162031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010727149,0.0007269346,0.0010229493,0.0008610742,0.00058799563,0.0010785003,0.0051788725,0.0014245326,0.0025897042],"category_scores_gemma":[0.0044304677,0.00047757698,0.00091881055,0.0008925025,0.0013202148,0.002018332,0.0020251945,0.0024716565,0.00062258315],"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":[0.00026368667,0.00037219378,0.0022069826,0.00031227723,0.00016437755,0.00023920224,0.00030423337,0.629123,0.0062480774,0.04318047,0.00830975,0.30927578],"study_design_scores_gemma":[0.000012307382,0.00004772764,0.00015420739,0.000007298172,0.000011440812,0.000042381682,0.000007348366,0.98236185,0.00076650525,0.01540391,0.0011751422,0.000009816512],"about_ca_topic_score_codex":0.011968189,"about_ca_topic_score_gemma":0.013543452,"teacher_disagreement_score":0.011968189,"about_ca_system_score_codex":0.0012651314,"about_ca_system_score_gemma":0.0015522452,"threshold_uncertainty_score":0.023797095},"labels":[],"label_agreement":null},{"id":"W4385484754","doi":"10.1109/ijcnn54540.2023.10191815","title":"Learning What, Where and Which to Transfer","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Concordia University","funders":"","keywords":"Computer science; Transfer of learning; Feature (linguistics); Source code; Artificial intelligence; Knowledge transfer; Code (set theory); Process (computing); Scratch; Machine learning; Bridge (graph theory); Negative transfer; Class (philosophy); Transfer (computing); Data mining","score_opus":0.018661639702153263,"score_gpt":0.2518451764882109,"score_spread":0.23318353678605766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385484754","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":[0.071807206,0.0043686507,0.87363315,0.010013396,0.0010367847,0.00037981293,0.0043861545,0.009575101,0.024799755],"genre_scores_gemma":[0.6886376,0.003343362,0.2796122,0.0028153188,0.00057590654,0.00047263893,0.007904203,0.001696631,0.014942153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987218,0.0003068918,0.000052074698,0.00061664823,0.00015688677,0.00014568334],"domain_scores_gemma":[0.99819857,0.00071510795,0.000113174574,0.0005490615,0.00029051516,0.0001336495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016110289,0.0015737285,0.0010787685,0.0009965138,0.00077354966,0.002453845,0.002011968,0.0020161315,0.0070107966],"category_scores_gemma":[0.010719981,0.00056217035,0.0011166433,0.0008627171,0.0011268085,0.009738283,0.0019011273,0.0037752718,0.0054434943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003178588,0.00062503415,0.008492356,0.00055523316,0.00024068411,0.00030732312,0.00045057572,0.07055807,0.012050078,0.031915434,0.07666994,0.79781747],"study_design_scores_gemma":[0.000060904345,0.0001060556,0.0020775069,0.00026962103,0.00011771032,0.00021111454,0.00057089963,0.68690753,0.020197319,0.25780296,0.031601645,0.000076744735],"about_ca_topic_score_codex":0.0067223366,"about_ca_topic_score_gemma":0.007174181,"teacher_disagreement_score":0.0070107966,"about_ca_system_score_codex":0.0012903983,"about_ca_system_score_gemma":0.0017470764,"threshold_uncertainty_score":0.023453414},"labels":[],"label_agreement":null},{"id":"W4385562802","doi":"10.32920/22734362.v1","title":"Edge-preserving Domain Adaptation for semantic segmentation of Medical Images","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer science; Domain adaptation; Artificial intelligence; Domain (mathematical analysis); Adaptation (eye); Enhanced Data Rates for GSM Evolution; Image (mathematics); Process (computing); Pattern recognition (psychology); Transformation (genetics); Computer vision; Image segmentation; Mathematics","score_opus":0.07401185805104676,"score_gpt":0.336702291871469,"score_spread":0.26269043382042223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385562802","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":[0.031484615,0.00029495472,0.96490055,0.00016173194,0.000035392288,0.000059392238,0.00010549581,0.0019836088,0.00097429176],"genre_scores_gemma":[0.42535573,0.00057808997,0.56639403,0.0005018118,0.00009304103,0.0001316383,0.0013229966,0.00056664436,0.005056012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995701,0.000112397094,0.000019557297,0.00014086707,0.00011242722,0.00004461026],"domain_scores_gemma":[0.99944097,0.00016929078,0.00006836464,0.00018419944,0.00009729084,0.000039778253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009834085,0.0005766315,0.0007210472,0.0013286651,0.0002882278,0.0006508528,0.0010916142,0.0009895981,0.0012097671],"category_scores_gemma":[0.0018952362,0.00039510278,0.0008307303,0.0010098601,0.0008495783,0.0010043987,0.0011460694,0.0013488348,0.0008224287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036580113,0.00018562966,0.0015264275,0.00013831539,0.00012490607,0.0001755481,0.00015510517,0.25848693,0.08156456,0.0074359905,0.0063910023,0.6434498],"study_design_scores_gemma":[0.00001133657,0.000045330846,0.0007407429,0.000008293142,0.000011904464,0.000183331,0.00002218391,0.9655994,0.022522053,0.008784049,0.0020553756,0.0000160359],"about_ca_topic_score_codex":0.0021986386,"about_ca_topic_score_gemma":0.0029860185,"teacher_disagreement_score":0.0021986386,"about_ca_system_score_codex":0.0006103276,"about_ca_system_score_gemma":0.0006835613,"threshold_uncertainty_score":0.0052008033},"labels":[],"label_agreement":null},{"id":"W4385572944","doi":"10.18653/v1/2022.emnlp-main.295","title":"Open World Classification with Adaptive Negative Samples","year":2022,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Office of Naval Research; Defense Advanced Research Projects Agency; U.S. Department of Energy; National Institutes of Health; National Science Foundation","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Inference; Machine learning; Relevance (law); Decision boundary; Binary classification; Task (project management); Key (lock); Identification (biology); Support vector machine","score_opus":0.1041206461614485,"score_gpt":0.2895426281072932,"score_spread":0.1854219819458447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385572944","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":[0.09855884,0.0014510739,0.89209014,0.0006012647,0.00032444985,0.00025236944,0.00037197315,0.0031602357,0.0031895933],"genre_scores_gemma":[0.73595804,0.0003489824,0.2538069,0.0010268918,0.00034917347,0.00032306637,0.0032874597,0.00029616142,0.004603353],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979526,0.00063452194,0.00010650008,0.0006993177,0.00044489958,0.00016217965],"domain_scores_gemma":[0.9953543,0.002080425,0.0003002131,0.0011982122,0.0008193355,0.00024760742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031195546,0.0015602473,0.0018679438,0.001513963,0.00090760377,0.0016563643,0.003574109,0.0025082128,0.0015371396],"category_scores_gemma":[0.01055619,0.00046378287,0.0011206997,0.001021784,0.0018017638,0.0041602068,0.0027865334,0.003163524,0.0013144423],"study_design_candidate":"bench_or_experimental","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":[0.0010071452,0.00096336455,0.00673278,0.00041408688,0.00015937668,0.0004274902,0.00046394244,0.14992397,0.015633918,0.015889935,0.018283362,0.79010063],"study_design_scores_gemma":[0.000030460304,0.00011536167,0.0006458001,0.000024747358,0.000022475873,0.00015198709,0.00009653584,0.97217315,0.0050327685,0.019426027,0.0022561993,0.000024419629],"about_ca_topic_score_codex":0.0017466597,"about_ca_topic_score_gemma":0.0027761343,"teacher_disagreement_score":0.003574109,"about_ca_system_score_codex":0.00083422294,"about_ca_system_score_gemma":0.00077528425,"threshold_uncertainty_score":0.01649797},"labels":[],"label_agreement":null},{"id":"W4385583101","doi":"10.1016/j.cviu.2023.103801","title":"Deep Bregman divergence for self-supervised representations learning","year":2023,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Google (Canada)","funders":"","keywords":"Bregman divergence; Divergence (linguistics); Embedding; Artificial intelligence; Artificial neural network; Metric (unit); Representation (politics); Euclidean distance; Computer science; Supervised learning; Euclidean geometry; Pattern recognition (psychology); Deep learning; Feature learning; Mathematics; Machine learning; Applied mathematics","score_opus":0.05154279473172417,"score_gpt":0.3063525445432608,"score_spread":0.2548097498115366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385583101","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":[0.009090284,0.00064320676,0.98866177,0.00024938956,0.000043826512,0.000029592884,0.00007074815,0.0006098392,0.00060143444],"genre_scores_gemma":[0.4989569,0.00096082065,0.48410127,0.0006475385,0.00018032119,0.00031217918,0.0014988631,0.0006630765,0.012679084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890625,0.000396189,0.000057184065,0.0002646339,0.00027433882,0.00010141973],"domain_scores_gemma":[0.9975406,0.0012222761,0.00016107062,0.0004366753,0.00050715165,0.00013225955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027197213,0.00079948414,0.0019254999,0.0009880489,0.00063501357,0.0011371094,0.0029748234,0.0026411517,0.002231668],"category_scores_gemma":[0.008008814,0.0005964945,0.000757542,0.0011261345,0.0013332855,0.0029213673,0.0026578836,0.0038737308,0.0009908873],"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":[0.0002666854,0.00032525358,0.0008496995,0.0002523084,0.00014527068,0.000106623,0.00021740205,0.48046482,0.008950576,0.07741678,0.012950512,0.41805404],"study_design_scores_gemma":[0.000004482937,0.000020081665,0.000082782106,0.000007792624,0.0000032908342,0.000014685113,0.0000066398547,0.97642446,0.0006503138,0.022385122,0.00039364706,0.000006610834],"about_ca_topic_score_codex":0.0051109986,"about_ca_topic_score_gemma":0.005743645,"teacher_disagreement_score":0.0051109986,"about_ca_system_score_codex":0.0014100189,"about_ca_system_score_gemma":0.0015616614,"threshold_uncertainty_score":0.014383376},"labels":[],"label_agreement":null},{"id":"W4385647259","doi":"10.1016/j.neunet.2023.08.005","title":"ProxyMix: Proxy-based Mixup training with label refinery for source-free domain adaptation","year":2023,"lang":"en","type":"article","venue":"Neural Networks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Exploit; Regularization (linguistics); Data mining; Pattern recognition (psychology); Transfer of learning; Machine learning","score_opus":0.04960374052475323,"score_gpt":0.2570099218405321,"score_spread":0.2074061813157789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385647259","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00505609,0.00040945582,0.9817031,0.00012918425,0.00012649487,0.00009623699,0.00026709435,0.011348128,0.00086423394],"genre_scores_gemma":[0.1586327,0.00037719414,0.8228396,0.00085934624,0.00015801779,0.0004519092,0.003381109,0.0029892356,0.0103108315],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998212,0.00055672065,0.000077281984,0.00062963093,0.00033658082,0.00018776362],"domain_scores_gemma":[0.9977456,0.00083203474,0.00009060478,0.0008486296,0.0003348128,0.00014834186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00278012,0.0023152407,0.0024677834,0.0012226793,0.00097206916,0.0018576832,0.004411288,0.003549157,0.009282605],"category_scores_gemma":[0.008401592,0.0012288316,0.0014563695,0.0014202239,0.0013848369,0.004168425,0.0063557606,0.005153699,0.0075749005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014119071,0.0005327148,0.0012923157,0.0003393517,0.00032390014,0.00028097464,0.00024852078,0.10981232,0.034463726,0.0131786335,0.029325172,0.80879045],"study_design_scores_gemma":[0.00005941638,0.00007633669,0.00020046931,0.00002693256,0.000035355286,0.00008478308,0.000043082215,0.96680933,0.01576957,0.012749462,0.0041088215,0.00003648621],"about_ca_topic_score_codex":0.0045282384,"about_ca_topic_score_gemma":0.009957283,"teacher_disagreement_score":0.009282605,"about_ca_system_score_codex":0.0009325338,"about_ca_system_score_gemma":0.0016245671,"threshold_uncertainty_score":0.031053424},"labels":[],"label_agreement":null},{"id":"W4385801558","doi":"10.1109/cvprw59228.2023.00477","title":"DynaShare: Task and Instance Conditioned Parameter Sharing for Multi-Task Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"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 British Columbia","funders":"","keywords":"Computer science; Inference; Generalization; Task (project management); Artificial intelligence; Multi-task learning; Machine learning; Feature (linguistics); Mathematics","score_opus":0.06338817488341765,"score_gpt":0.3061988352371071,"score_spread":0.24281066035368948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385801558","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":[0.013364883,0.00039763685,0.98131496,0.00020816756,0.00007991816,0.00012457238,0.00014817638,0.0032069082,0.0011547952],"genre_scores_gemma":[0.61001253,0.00028098034,0.38202587,0.0007205966,0.00013188878,0.0005902097,0.00095349853,0.00079483684,0.004489531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863005,0.00043504778,0.000072536044,0.0004996355,0.00021126604,0.00015153867],"domain_scores_gemma":[0.99776673,0.0008797491,0.00016139564,0.000828886,0.00020499996,0.00015819551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002907379,0.0018780858,0.00182162,0.0006595395,0.00076165167,0.0013219903,0.0047662994,0.0025721742,0.0033306396],"category_scores_gemma":[0.008440417,0.00093083875,0.0010688733,0.00083393726,0.0014803305,0.0045213886,0.004024714,0.0041205073,0.0011162445],"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":[0.00048988534,0.000491704,0.0016337923,0.00021532353,0.0002566889,0.00018132552,0.00022964612,0.5868506,0.011416629,0.016866462,0.00845344,0.37291455],"study_design_scores_gemma":[0.0000253304,0.000050973216,0.00012421288,0.000005951209,0.000009700681,0.000024248313,0.000012112871,0.9837414,0.0018971189,0.013290079,0.00080642576,0.000012454542],"about_ca_topic_score_codex":0.0037978312,"about_ca_topic_score_gemma":0.006697998,"teacher_disagreement_score":0.0047662994,"about_ca_system_score_codex":0.0011910395,"about_ca_system_score_gemma":0.0016924412,"threshold_uncertainty_score":0.015375853},"labels":[],"label_agreement":null},{"id":"W4385801596","doi":"10.1109/cvprw59228.2023.00499","title":"CFDP: Common Frequency Domain Pruning","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Robustness (evolution); Pruning; Artificial neural network; Trimming; FLOPS; Feature (linguistics); Pipeline (software); Artificial intelligence; Machine learning; Deep neural networks; Frequency domain; Domain (mathematical analysis); Interoperability; Parallel computing; Mathematics","score_opus":0.025105025345558887,"score_gpt":0.2701033692254175,"score_spread":0.2449983438798586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385801596","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":[0.013960339,0.00050240516,0.9711421,0.00032855093,0.00013458947,0.00011058576,0.00054356636,0.00993752,0.0033403803],"genre_scores_gemma":[0.33853582,0.00048008192,0.64601576,0.00085554447,0.000116493495,0.00035631895,0.0035469602,0.0016025136,0.008490506],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948907,0.00008216954,0.000027013146,0.00015104776,0.00017695974,0.00007369543],"domain_scores_gemma":[0.999181,0.00025652559,0.000053170446,0.00031700163,0.00015132541,0.000041110765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008937901,0.0017599788,0.0010618191,0.0011109113,0.0007706114,0.0012889446,0.0027218163,0.0018353183,0.0053532603],"category_scores_gemma":[0.0034756218,0.000605421,0.0011290412,0.0007694073,0.00085688353,0.0020417522,0.0024700994,0.0028029175,0.0023087084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002460542,0.00019776046,0.0022354242,0.00023445004,0.00021101821,0.0005288416,0.00018138903,0.40897974,0.01735187,0.027120715,0.046517033,0.4961957],"study_design_scores_gemma":[0.00002371998,0.000040768875,0.00021360915,0.000019980378,0.000019708299,0.00016152121,0.000020990821,0.9670405,0.0075664283,0.01892263,0.00595684,0.000013366164],"about_ca_topic_score_codex":0.009381403,"about_ca_topic_score_gemma":0.014761561,"teacher_disagreement_score":0.009381403,"about_ca_system_score_codex":0.0008381851,"about_ca_system_score_gemma":0.001629118,"threshold_uncertainty_score":0.018653631},"labels":[],"label_agreement":null},{"id":"W4385804898","doi":"10.1109/cvprw59228.2023.00242","title":"Online Distillation with Continual Learning for Cyclic Domain Shifts","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Service Public de Wallonie; King Abdullah University of Science and Technology; Fonds De La Recherche Scientifique - FNRS","keywords":"Computer science; Forgetting; Distillation; Robustness (evolution); Artificial intelligence; Machine learning; Domain (mathematical analysis); Context (archaeology); Field (mathematics); Deep learning; Mathematics; Chemistry","score_opus":0.02305362285116487,"score_gpt":0.27381441006733065,"score_spread":0.2507607872161658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385804898","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":[0.052306205,0.00067957514,0.94198686,0.00034402838,0.0001275252,0.000068147834,0.000091546695,0.0027375012,0.0016585925],"genre_scores_gemma":[0.7729583,0.00027661142,0.2214535,0.00048414813,0.00011134313,0.00013428931,0.00037973345,0.00029902463,0.0039030379],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999393,0.00014267024,0.00003712318,0.00021019323,0.0001425197,0.000074496056],"domain_scores_gemma":[0.99829644,0.00077045197,0.00014994908,0.0004778437,0.00020300942,0.00010224858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014784096,0.0010243957,0.0010735983,0.00050161517,0.0006414876,0.0008670415,0.0022497291,0.0010883796,0.0029195591],"category_scores_gemma":[0.0053503695,0.0004896153,0.0005596645,0.0005660038,0.0015605622,0.0031169222,0.0030769024,0.0028610034,0.0007085213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006707252,0.00049137854,0.0025599564,0.00030719608,0.00012047204,0.00028075094,0.0003160155,0.45133293,0.020752618,0.021481642,0.004492988,0.49719334],"study_design_scores_gemma":[0.000018152796,0.00006570937,0.00011823368,0.0000096721415,0.0000071502595,0.00005073551,0.000019022424,0.9843389,0.0050192894,0.009220307,0.001118292,0.000014566373],"about_ca_topic_score_codex":0.0023059566,"about_ca_topic_score_gemma":0.0038198158,"teacher_disagreement_score":0.0029195591,"about_ca_system_score_codex":0.00054887094,"about_ca_system_score_gemma":0.0012187486,"threshold_uncertainty_score":0.009766877},"labels":[],"label_agreement":null},{"id":"W4385804921","doi":"10.1109/cvprw59228.2023.00248","title":"CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of Waterloo","funders":"Microsoft","keywords":"Computer science; Baseline (sea); Artificial intelligence; Task (project management); Regularization (linguistics); Segmentation; Machine learning; Object (grammar); Semi-supervised learning; Supervised learning; Online learning; Learning object; Labeled data; Multimedia; Artificial neural network","score_opus":0.0337194242945671,"score_gpt":0.31346565451913594,"score_spread":0.27974623022456885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385804921","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17396739,0.00863639,0.13770293,0.0026109975,0.0024455106,0.003279528,0.5775216,0.06967902,0.024156643],"genre_scores_gemma":[0.096227564,0.00079499267,0.10731574,0.000562129,0.00019288187,0.00096905866,0.787419,0.0011700182,0.0053486326],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99829465,0.00021302818,0.0001421921,0.000727347,0.00042520193,0.00019759765],"domain_scores_gemma":[0.997712,0.00040447272,0.00020697193,0.00088689127,0.0005359405,0.0002536957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015974324,0.0024895745,0.0013501117,0.002842453,0.001379561,0.001924931,0.00434385,0.003078614,0.0059672454],"category_scores_gemma":[0.006394813,0.0006589128,0.0016192526,0.003090535,0.0010762248,0.0025343143,0.002564297,0.002864973,0.00593515],"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":[0.0014323643,0.0012290104,0.009374894,0.0026262205,0.00042928717,0.00066636666,0.00038678982,0.026113058,0.020907108,0.005530879,0.6409753,0.29032856],"study_design_scores_gemma":[0.00078905415,0.001371438,0.039259564,0.0009571643,0.00024654463,0.0027121992,0.0013183734,0.39837012,0.040012795,0.022671884,0.49181148,0.00047939454],"about_ca_topic_score_codex":0.02948505,"about_ca_topic_score_gemma":0.06329369,"teacher_disagreement_score":0.02948505,"about_ca_system_score_codex":0.001853458,"about_ca_system_score_gemma":0.002185411,"threshold_uncertainty_score":0.05862683},"labels":[],"label_agreement":null},{"id":"W4385815567","doi":"10.1109/cvprw59228.2023.00505","title":"MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Domain adaptation; Classifier (UML); Point cloud; Domain (mathematical analysis); Artificial intelligence; Adaptation (eye); Pattern recognition (psychology); Feature (linguistics); Machine learning; Data mining; Mathematics","score_opus":0.0517536643928786,"score_gpt":0.27978327606918213,"score_spread":0.22802961167630353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385815567","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":[0.01482662,0.00048110576,0.9738835,0.00010044346,0.00013284317,0.00008032419,0.0004904169,0.009246083,0.0007586727],"genre_scores_gemma":[0.2595109,0.00035489857,0.72639644,0.00034933022,0.00016186321,0.00032512596,0.0068647224,0.0012123022,0.0048243953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988563,0.0002779243,0.000051551582,0.00042922978,0.00026338047,0.00012163331],"domain_scores_gemma":[0.99861646,0.00041146125,0.000078437995,0.0004783061,0.0003278874,0.00008752752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00199844,0.0017430974,0.0017718307,0.0017122559,0.0008389186,0.00114062,0.0027192417,0.0014827662,0.0022668033],"category_scores_gemma":[0.0035139935,0.00072651904,0.002082739,0.001985353,0.00064902677,0.0021002467,0.002314734,0.0028044214,0.0022790625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002246053,0.00026500295,0.0027982683,0.000113783084,0.00045846496,0.000101797144,0.0001487633,0.27955273,0.013033277,0.002801021,0.018437792,0.6820645],"study_design_scores_gemma":[0.000009017931,0.000030384594,0.0005500182,0.000005683591,0.000015488547,0.000037921607,0.000021779826,0.99206597,0.0029348326,0.0025947546,0.0017191767,0.000014962234],"about_ca_topic_score_codex":0.008019148,"about_ca_topic_score_gemma":0.015038857,"teacher_disagreement_score":0.008019148,"about_ca_system_score_codex":0.0007625333,"about_ca_system_score_gemma":0.0011436592,"threshold_uncertainty_score":0.015944958},"labels":[],"label_agreement":null},{"id":"W4386065782","doi":"10.1109/cvpr52729.2023.01935","title":"Simulated Annealing in Early Layers Leads to Better Generalization","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Initialization; Computer science; Artificial intelligence; Margin (machine learning); Gradient descent; Benchmark (surveying); Stochastic gradient descent; Machine learning; Transfer of learning; Generalization; Forgetting; Overfitting; Simulated annealing; Matching (statistics); Algorithm; Artificial neural network; Mathematics","score_opus":0.02811438426582509,"score_gpt":0.27916472822531707,"score_spread":0.251050343959492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386065782","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":[0.09083543,0.0010365281,0.89264596,0.0009201522,0.00022543325,0.00013499054,0.00017433673,0.0066586384,0.0073685856],"genre_scores_gemma":[0.71391815,0.00048533693,0.27720192,0.0006414031,0.000087804954,0.00023932023,0.00064015615,0.0010795997,0.005706371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990638,0.00025235882,0.0000718792,0.00031689132,0.00015907253,0.00013594562],"domain_scores_gemma":[0.99662423,0.001577508,0.0002104634,0.0010312255,0.0004546779,0.0001020053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002295625,0.0020635093,0.0017824653,0.0008972505,0.00081206014,0.0013835806,0.0018598639,0.0018408517,0.004479464],"category_scores_gemma":[0.009320966,0.00096177123,0.0015268008,0.0005409786,0.0010382482,0.0032691096,0.0014272865,0.0031714826,0.0016428775],"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":[0.00021442416,0.00015287126,0.0022919683,0.00015456135,0.00012975142,0.0001300088,0.00017655146,0.84914976,0.010863001,0.0065225605,0.0046386984,0.12557577],"study_design_scores_gemma":[0.000008596141,0.000036225218,0.00022062384,0.000010399208,0.000011247496,0.000021797583,0.000010502322,0.9914204,0.0033953085,0.004199619,0.0006564324,0.00000885407],"about_ca_topic_score_codex":0.007341496,"about_ca_topic_score_gemma":0.010692844,"teacher_disagreement_score":0.007341496,"about_ca_system_score_codex":0.0013132534,"about_ca_system_score_gemma":0.0014009392,"threshold_uncertainty_score":0.014985323},"labels":[],"label_agreement":null},{"id":"W4386067004","doi":"10.23919/mva57639.2023.10216197","title":"Dynamic Transfer for Domain Adaptation in Crowd Counting","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Manitoba","funders":"","keywords":"Computer science; Domain adaptation; Benchmark (surveying); Domain (mathematical analysis); Adaptation (eye); Artificial intelligence; Key (lock); Transfer of learning; Machine learning; Artificial neural network; Data modeling; Data mining; Database; Mathematics","score_opus":0.02710659103733413,"score_gpt":0.2726944711241907,"score_spread":0.24558788008685659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386067004","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":[0.022421869,0.0003023886,0.9743456,0.000244167,0.00008628255,0.000080297876,0.000061924424,0.00079422654,0.001663261],"genre_scores_gemma":[0.7138041,0.00048018442,0.2786126,0.00050150783,0.0001962421,0.00028813884,0.00043836943,0.00030466454,0.0053742556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988512,0.00036854224,0.00003920153,0.0004111421,0.00019850586,0.00013151429],"domain_scores_gemma":[0.9982951,0.0008542013,0.00016769231,0.00032045154,0.00022445725,0.00013812819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023596527,0.0012522062,0.0015255234,0.0013458373,0.00096298935,0.0011728688,0.0022749873,0.0018158045,0.0022721186],"category_scores_gemma":[0.007058576,0.0005379538,0.0011168241,0.0012443543,0.0017671073,0.0029314067,0.0033829475,0.0023295858,0.0009313284],"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":[0.00019666075,0.0002730337,0.0018186863,0.00015972495,0.00012324826,0.0003271697,0.00050497125,0.74046606,0.014035901,0.015540065,0.0034857423,0.22306871],"study_design_scores_gemma":[0.000008247745,0.00004644659,0.00028819728,0.000009853634,0.000008643325,0.00008073482,0.00005948438,0.9764687,0.0032618013,0.018565357,0.0011822479,0.000020296875],"about_ca_topic_score_codex":0.003732885,"about_ca_topic_score_gemma":0.0022201089,"teacher_disagreement_score":0.003732885,"about_ca_system_score_codex":0.0012111565,"about_ca_system_score_gemma":0.0010262238,"threshold_uncertainty_score":0.012479246},"labels":[],"label_agreement":null},{"id":"W4386075549","doi":"10.1109/cvpr52729.2023.01938","title":"Re-basin via implicit Sinkhorn differentiation","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Differentiable function; Computer science; Benchmark (surveying); Automatic differentiation; Permutation (music); Mathematical optimization; Function (biology); Code (set theory); State (computer science); Artificial intelligence; Theoretical computer science; Algorithm; Mathematics; Programming language","score_opus":0.02323421236982959,"score_gpt":0.2592694577035934,"score_spread":0.2360352453337638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386075549","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":[0.018750187,0.00017539065,0.97561455,0.00018401866,0.000036994916,0.000044449735,0.000081504906,0.0015373201,0.003575573],"genre_scores_gemma":[0.53268343,0.00024033755,0.45037708,0.00045693034,0.000040467097,0.00024158835,0.0006286131,0.0009939282,0.014337655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998155,0.00003941844,0.000009686666,0.00006122272,0.00005109806,0.0000231246],"domain_scores_gemma":[0.99955314,0.00018809445,0.000043327578,0.00010699213,0.000070547394,0.00003783368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062848505,0.00091336225,0.00097397284,0.0006374895,0.00050960225,0.0007804355,0.0018781896,0.0013501014,0.00575667],"category_scores_gemma":[0.0020325447,0.00051273004,0.0006940829,0.0005005491,0.0009948924,0.0021205854,0.002030618,0.0015636189,0.0013764291],"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":[0.000102085934,0.000061741244,0.0007854371,0.00010429518,0.000034869998,0.00011988811,0.00014889725,0.7972421,0.0066607716,0.045277845,0.0049205897,0.14454141],"study_design_scores_gemma":[0.0000051485945,0.000014817074,0.000024564632,0.0000066627576,0.0000028146746,0.000017855886,0.000007390123,0.987751,0.000832106,0.010355145,0.0009788861,0.0000036353802],"about_ca_topic_score_codex":0.0047652614,"about_ca_topic_score_gemma":0.009017577,"teacher_disagreement_score":0.00575667,"about_ca_system_score_codex":0.0009110763,"about_ca_system_score_gemma":0.0011531024,"threshold_uncertainty_score":0.019258022},"labels":[],"label_agreement":null},{"id":"W4386076493","doi":"10.1109/cvpr52729.2023.01548","title":"ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1324,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Normalization (sociology); Feature learning; Pattern recognition (psychology); Autoencoder; Segmentation; Deep learning; Feature extraction; Ranging; Feature (linguistics); Performance improvement; Engineering","score_opus":0.02219720391790699,"score_gpt":0.2561102292598575,"score_spread":0.2339130253419505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386076493","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":[0.09772447,0.0012620836,0.84987104,0.00046338467,0.0005810833,0.000447475,0.0010633487,0.036228467,0.012358658],"genre_scores_gemma":[0.49994522,0.000503498,0.47809196,0.00085686217,0.00011872818,0.0006946285,0.003736055,0.002379812,0.013673199],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996865,0.000034666326,0.000018130651,0.00012216004,0.000081103964,0.000057473542],"domain_scores_gemma":[0.99962366,0.000073011084,0.000023753193,0.00012022017,0.000119263335,0.00004014398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008231147,0.0019633407,0.00069477636,0.00048661916,0.00032514494,0.00085752923,0.0033852113,0.0009818204,0.0049185185],"category_scores_gemma":[0.0019271134,0.00096734386,0.000975015,0.00039773373,0.00047735695,0.002326881,0.0017320835,0.0017794,0.002876769],"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":[0.00058982946,0.0004153651,0.003068145,0.00042903968,0.0005189277,0.000363197,0.00021167815,0.51944,0.053160254,0.010950928,0.046830393,0.36402228],"study_design_scores_gemma":[0.00003529231,0.00009587006,0.00020083819,0.0000133515005,0.000030829182,0.00005970315,0.00001650863,0.9816005,0.0108982595,0.002333119,0.0046983194,0.000017454375],"about_ca_topic_score_codex":0.0113744745,"about_ca_topic_score_gemma":0.021437155,"teacher_disagreement_score":0.0113744745,"about_ca_system_score_codex":0.00096741907,"about_ca_system_score_gemma":0.0012631454,"threshold_uncertainty_score":0.022616506},"labels":[],"label_agreement":null},{"id":"W4386076614","doi":"10.1109/cvpr52729.2023.01147","title":"EcoTTA: Memory-Efficient Continual Test-Time Adaptation via Self-Distilled Regularization","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Forgetting; Computer science; Regularization (linguistics); Artificial intelligence","score_opus":0.009933171051921366,"score_gpt":0.2172954586590134,"score_spread":0.20736228760709202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386076614","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":[0.038324144,0.0006584409,0.94975203,0.0002377154,0.00020759746,0.0001260197,0.00011079353,0.007576481,0.003006661],"genre_scores_gemma":[0.6509347,0.000309933,0.33654273,0.0006239816,0.00014616153,0.0003726876,0.00068251486,0.0010705331,0.009316776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995715,0.00008058181,0.000026395226,0.00013074967,0.00012872898,0.00006201947],"domain_scores_gemma":[0.9988262,0.00041846195,0.00009487969,0.00028981094,0.00028899757,0.00008167463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010373796,0.001403628,0.00090922095,0.000685093,0.00040984125,0.0007612696,0.0030508211,0.0014123417,0.0028018062],"category_scores_gemma":[0.0043253824,0.0005200342,0.0007361128,0.0005421979,0.00083432795,0.0019724502,0.0017999526,0.0022847946,0.0012041125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034198217,0.0004335948,0.0022255115,0.00016273644,0.00019047513,0.00022331663,0.00020926724,0.36402428,0.03956715,0.0066351015,0.010835072,0.57515144],"study_design_scores_gemma":[0.000016295471,0.000055602013,0.00020717725,0.000008357536,0.000016534954,0.0000435053,0.000011831512,0.991983,0.004590497,0.0019218411,0.0011325284,0.000012773579],"about_ca_topic_score_codex":0.0048973123,"about_ca_topic_score_gemma":0.007825724,"teacher_disagreement_score":0.0048973123,"about_ca_system_score_codex":0.00065732654,"about_ca_system_score_gemma":0.001164291,"threshold_uncertainty_score":0.00973767},"labels":[],"label_agreement":null},{"id":"W4386100208","doi":"10.1007/s13735-023-00286-5","title":"CoCoOpter: Pre-train, prompt, and fine-tune the vision-language model for few-shot image classification","year":2023,"lang":"en","type":"article","venue":"International Journal of Multimedia Information Retrieval","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Overfitting; Artificial intelligence; Convolutional neural network; Generalizability theory; Classifier (UML); Pattern recognition (psychology); Transfer of learning; Contextual image classification; Machine learning; Artificial neural network; Image (mathematics)","score_opus":0.028495614823678975,"score_gpt":0.33388323492829225,"score_spread":0.3053876201046133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386100208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04432603,0.001719611,0.8149218,0.00037748384,0.0008191183,0.00048562797,0.0027274182,0.13210143,0.002521461],"genre_scores_gemma":[0.3001946,0.00062007277,0.66001284,0.0016868269,0.0002163009,0.00093983166,0.016769761,0.005281255,0.014278451],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992693,0.00007119775,0.000031897955,0.00033791686,0.00014999662,0.00013962541],"domain_scores_gemma":[0.9990006,0.00032880402,0.00003992758,0.00022092358,0.00027743078,0.00013229543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001182958,0.0024468866,0.0018340172,0.0012737621,0.0007456196,0.0011035496,0.0038075787,0.001866967,0.008276684],"category_scores_gemma":[0.0029649707,0.00081595586,0.0011589283,0.0010929577,0.00049962645,0.0022143754,0.0022566465,0.0030914724,0.005779997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009523157,0.00091706327,0.0017026105,0.00032901738,0.00026791173,0.0002590548,0.00008982182,0.024560247,0.04323395,0.0011281167,0.06936525,0.85719466],"study_design_scores_gemma":[0.00012634421,0.00023471793,0.0008274782,0.000020227379,0.00006071186,0.00015756485,0.00006614256,0.9597429,0.029646236,0.0032265775,0.005840906,0.000050107665],"about_ca_topic_score_codex":0.017072301,"about_ca_topic_score_gemma":0.034103207,"teacher_disagreement_score":0.017072301,"about_ca_system_score_codex":0.0009143261,"about_ca_system_score_gemma":0.0024306295,"threshold_uncertainty_score":0.03394586},"labels":[],"label_agreement":null},{"id":"W4386103321","doi":"10.48550/arxiv.2308.10014","title":"Semi-Implicit Variational Inference via Score Matching","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Peking University; National Natural Science Foundation of China; Institute for Catastrophic Loss Reduction","keywords":"Inference; Matching (statistics); Minimax; Bayesian inference; Computer science; Bayesian probability; Mathematics; Mathematical optimization; Artificial intelligence; Statistics","score_opus":0.11704319771233422,"score_gpt":0.21773623573503034,"score_spread":0.10069303802269612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386103321","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030192723,0.000072520495,0.9957991,0.000098603894,0.00001171117,0.000022733635,0.000037954473,0.00022148501,0.0007165603],"genre_scores_gemma":[0.34740472,0.00032032843,0.64414966,0.00038298717,0.00014124344,0.0003322377,0.00080041226,0.00062593754,0.0058425064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980095,0.0009758867,0.00007885279,0.000377582,0.00042731818,0.00013079983],"domain_scores_gemma":[0.99547666,0.0028709557,0.00030300225,0.00071447657,0.0004245991,0.00021029585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040980545,0.0011417918,0.0020614895,0.001198915,0.0008106314,0.0018832404,0.0037929926,0.0023696905,0.003940171],"category_scores_gemma":[0.015286086,0.0012878156,0.0014131732,0.0012390623,0.0022100022,0.003160004,0.003331119,0.0036064414,0.0010942296],"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":[0.00009375275,0.00007934053,0.0009797803,0.00013260935,0.00011384531,0.000080502155,0.000152108,0.7613881,0.0021736298,0.15206493,0.003186299,0.079555124],"study_design_scores_gemma":[0.0000046235045,0.0000053584613,0.000030277168,0.000004564922,0.000002806649,0.000007691762,0.0000037933564,0.9716261,0.0002241851,0.027781378,0.0003053209,0.0000039209845],"about_ca_topic_score_codex":0.004719917,"about_ca_topic_score_gemma":0.0062697185,"teacher_disagreement_score":0.004719917,"about_ca_system_score_codex":0.0014087979,"about_ca_system_score_gemma":0.002375523,"threshold_uncertainty_score":0.021672845},"labels":[],"label_agreement":null},{"id":"W4386185017","doi":"10.48550/arxiv.2308.12416","title":"Reframing the Brain Age Prediction Problem to a More Interpretable and Quantitative Approach","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Alberta Innovates; Hotchkiss Brain Institute, University of Calgary","keywords":"Cognitive reframing; Voxel; Artificial intelligence; Computer science; Pattern recognition (psychology); Image (mathematics); Regression; Deep learning; Machine learning; Mathematics; Psychology; Statistics","score_opus":0.09986690048662719,"score_gpt":0.22469910482093028,"score_spread":0.12483220433430309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386185017","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":[0.06179398,0.001255492,0.9319915,0.0022171387,0.00013287387,0.000042973013,0.0003580987,0.0008778303,0.0013301251],"genre_scores_gemma":[0.82736903,0.00072991545,0.16859475,0.0005353694,0.00034065673,0.000055510114,0.00048150908,0.00018163178,0.0017115171],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990803,0.00039243593,0.00003965771,0.00033227133,0.00011225698,0.000043027107],"domain_scores_gemma":[0.9946767,0.003437956,0.0005373895,0.0006422423,0.0005725267,0.00013317708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036681949,0.0013071868,0.0010031038,0.0012104448,0.00025867712,0.0014088718,0.001219504,0.0016981604,0.0011477148],"category_scores_gemma":[0.013104512,0.00037213715,0.0006071005,0.0006828708,0.0014893082,0.0035928702,0.0013755464,0.002755947,0.00036901454],"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":[0.00039643503,0.00026518528,0.015460438,0.00045258654,0.00039574227,0.00038127624,0.0007038109,0.61215055,0.020939207,0.044451475,0.0062124277,0.2981909],"study_design_scores_gemma":[0.000013356023,0.0000667719,0.0023675237,0.000030600248,0.000029113151,0.00010979563,0.0000722859,0.92928547,0.004068279,0.06283147,0.0010987503,0.000026516318],"about_ca_topic_score_codex":0.0034904364,"about_ca_topic_score_gemma":0.0022654536,"teacher_disagreement_score":0.0036681949,"about_ca_system_score_codex":0.00083984033,"about_ca_system_score_gemma":0.00063761615,"threshold_uncertainty_score":0.019399464},"labels":[],"label_agreement":null},{"id":"W4386243195","doi":"10.1109/crv60082.2023.00045","title":"Fast Fine-Tuning Using Curriculum Domain Adaptation","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Fine-tuning; Domain (mathematical analysis); Artificial intelligence; Adaptation (eye); Artificial neural network; Domain adaptation; Machine learning; Enhanced Data Rates for GSM Evolution; Deep learning; Deep neural networks; Train; Training set; Pattern recognition (psychology)","score_opus":0.04296787530832765,"score_gpt":0.2791595648111833,"score_spread":0.23619168950285566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386243195","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05850979,0.0007562313,0.9283625,0.00019046069,0.000196905,0.00017259906,0.00028924565,0.007639254,0.0038829178],"genre_scores_gemma":[0.54730874,0.00061076053,0.44035906,0.00066493155,0.00010963102,0.00043508987,0.0024322462,0.00074608973,0.007333444],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995085,0.000079542726,0.00003067078,0.0002090402,0.00010722473,0.000065044944],"domain_scores_gemma":[0.99917704,0.00020586533,0.00006412656,0.00025487537,0.00024160475,0.000056528537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086274423,0.0012984087,0.0010489168,0.00073777477,0.00046763505,0.0005919179,0.0019011229,0.00086366094,0.0027198805],"category_scores_gemma":[0.0034410115,0.000511188,0.00083199865,0.00084538077,0.00052217406,0.002044383,0.0018644026,0.002331322,0.0016232737],"study_design_candidate":"bench_or_experimental","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":[0.00020317966,0.00047374048,0.0035745301,0.00020877137,0.0001257324,0.00015628546,0.00013689198,0.2829366,0.04277204,0.0062846425,0.016247503,0.64688003],"study_design_scores_gemma":[0.000035683042,0.00006754991,0.0005515792,0.000014478966,0.00001846724,0.0000699871,0.000028005294,0.98026854,0.010463278,0.0052468907,0.003218489,0.000017065177],"about_ca_topic_score_codex":0.0045605665,"about_ca_topic_score_gemma":0.008919035,"teacher_disagreement_score":0.0045605665,"about_ca_system_score_codex":0.0007624635,"about_ca_system_score_gemma":0.0014034456,"threshold_uncertainty_score":0.009098887},"labels":[],"label_agreement":null},{"id":"W4386243210","doi":"10.1109/crv60082.2023.00023","title":"Class Instance Balanced Learning for Long-Tailed Classification","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Skew; Computer science; Classifier (UML); Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Deep neural networks; Cross entropy; Machine learning; Contextual image classification; Entropy (arrow of time); Training set; Image (mathematics)","score_opus":0.04861006792535539,"score_gpt":0.29511952731552443,"score_spread":0.24650945939016905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386243210","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12618151,0.0016509782,0.8603014,0.0010194262,0.00023447917,0.00015926111,0.0006377767,0.005618735,0.0041964096],"genre_scores_gemma":[0.83238655,0.0003662525,0.15645266,0.0010614323,0.0002716304,0.00023846961,0.0025038004,0.0004810617,0.00623817],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99864405,0.00033196225,0.000074616284,0.00048688884,0.00030110998,0.00016139327],"domain_scores_gemma":[0.9963176,0.0016196168,0.00036172275,0.0009970213,0.00045088746,0.00025310446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034463087,0.0017451347,0.0015500077,0.0009993284,0.0007117121,0.0012874305,0.0026432187,0.002261724,0.00363801],"category_scores_gemma":[0.008446477,0.00042574006,0.0007293483,0.0009878371,0.0013958107,0.004765608,0.0026899085,0.0041480833,0.0015963035],"study_design_candidate":"bench_or_experimental","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":[0.002133616,0.0011721579,0.007549962,0.0003035754,0.00022496385,0.00032954622,0.0002413298,0.31735772,0.027386196,0.021906134,0.024149109,0.5972456],"study_design_scores_gemma":[0.00004646476,0.00015252203,0.0006399787,0.000019301664,0.000018326982,0.00007476199,0.00002922834,0.9683415,0.006983993,0.022406941,0.0012686277,0.000018369361],"about_ca_topic_score_codex":0.0017643824,"about_ca_topic_score_gemma":0.0027072409,"teacher_disagreement_score":0.00363801,"about_ca_system_score_codex":0.001341464,"about_ca_system_score_gemma":0.0010057852,"threshold_uncertainty_score":0.018226027},"labels":[],"label_agreement":null},{"id":"W4386243252","doi":"10.1109/infocom53939.2023.10228981","title":"Oblivion: Poisoning Federated Learning by Inducing Catastrophic Forgetting","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Forgetting; Scalability; Exploit; Computer science; Component (thermodynamics); Process (computing); Federated learning; Computer security; Artificial intelligence; Distributed computing; Database","score_opus":0.016565522430657186,"score_gpt":0.24420940135485575,"score_spread":0.22764387892419857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386243252","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18677986,0.00041533675,0.8020404,0.0005482552,0.000099494864,0.00018056217,0.00012986035,0.008422107,0.0013842037],"genre_scores_gemma":[0.9333588,0.000059731527,0.06488034,0.00029648413,0.0000291451,0.00007887721,0.00016251074,0.00010698234,0.0010270642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99831307,0.00043953175,0.000113425274,0.00047019613,0.0004166102,0.00024714772],"domain_scores_gemma":[0.99419034,0.0014929638,0.0006265419,0.0027882687,0.0006158546,0.0002860638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039031692,0.0011509625,0.0014473124,0.00066817045,0.0007227682,0.0010153474,0.0029396496,0.0014347341,0.00075152115],"category_scores_gemma":[0.012120716,0.0004859827,0.0007843127,0.00053053634,0.0018340477,0.0031130353,0.0033625353,0.002141059,0.00031890854],"study_design_candidate":"bench_or_experimental","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":[0.0007977556,0.00047175254,0.014220847,0.00014996906,0.00028754666,0.00048234928,0.000402141,0.71474874,0.014432747,0.008774692,0.004818594,0.24041289],"study_design_scores_gemma":[0.000022740327,0.000114635055,0.00036898997,0.0000066709126,0.000015401703,0.00009040592,0.000017279302,0.9876424,0.0047109826,0.0066113533,0.00038793264,0.000011052015],"about_ca_topic_score_codex":0.0025520725,"about_ca_topic_score_gemma":0.0027964993,"teacher_disagreement_score":0.0039031692,"about_ca_system_score_codex":0.001021585,"about_ca_system_score_gemma":0.0015592424,"threshold_uncertainty_score":0.020642161},"labels":[],"label_agreement":null},{"id":"W4386249616","doi":"10.1109/crv60082.2023.00046","title":"Gradient-Based Maximally Interfered Retrieval for Domain Incremental 3D Object Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Forgetting; Artificial intelligence; Domain (mathematical analysis); Object detection; Software deployment; Scratch; Machine learning; Data mining; Pattern recognition (psychology)","score_opus":0.027035498493039944,"score_gpt":0.26246315551740607,"score_spread":0.23542765702436613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386249616","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":[0.03192555,0.0008073083,0.96004057,0.00013936663,0.00006849945,0.000093858034,0.0002595498,0.0056755804,0.000989708],"genre_scores_gemma":[0.42384297,0.00044546736,0.5698509,0.00048338494,0.00013694541,0.00017310696,0.0019590268,0.00062631187,0.0024819071],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994185,0.00008404219,0.000028073879,0.00020262951,0.00019300259,0.000073778414],"domain_scores_gemma":[0.99926645,0.00019616248,0.00006974074,0.00026204268,0.00015034189,0.000055375356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008001835,0.0011810816,0.0014231862,0.0011469497,0.00040030398,0.00080350036,0.0028470124,0.00097136974,0.0013229884],"category_scores_gemma":[0.002998507,0.0005888231,0.00084285514,0.0011353726,0.00071388006,0.0016144334,0.0015297534,0.0012006521,0.0015642048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000593191,0.00039940246,0.0029184814,0.00025846896,0.00018160502,0.00026863013,0.00029220685,0.17982581,0.07860447,0.004479869,0.016188992,0.71598893],"study_design_scores_gemma":[0.000026663394,0.00011027907,0.00067584665,0.000008375099,0.000020421281,0.0002029792,0.000030236184,0.9774296,0.015144804,0.0040300325,0.0022934724,0.000027322054],"about_ca_topic_score_codex":0.0048651956,"about_ca_topic_score_gemma":0.0066286107,"teacher_disagreement_score":0.0048651956,"about_ca_system_score_codex":0.0005978437,"about_ca_system_score_gemma":0.00077160157,"threshold_uncertainty_score":0.009673774},"labels":[],"label_agreement":null},{"id":"W4386566490","doi":"10.18653/v1/2023.findings-eacl.74","title":"Improving Prediction Backward-Compatiblility in NLP Model Upgrade with Gated Fusion","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Upgrade; Computer science; Regression; Artificial intelligence; Regression analysis; Machine learning; Ensemble forecasting; Baseline (sea); Data mining; Statistics; Mathematics","score_opus":0.02558255835228114,"score_gpt":0.23974776567932257,"score_spread":0.21416520732704142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386566490","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":[0.13935812,0.0012905971,0.8373474,0.00067958084,0.0003526498,0.00015997252,0.00056443777,0.01719088,0.003056328],"genre_scores_gemma":[0.80427206,0.00035764393,0.18784566,0.00070446,0.00017300204,0.00011393731,0.0022384787,0.00083825685,0.0034565532],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790263,0.00059965125,0.000117696516,0.00068096706,0.00045607312,0.00024299439],"domain_scores_gemma":[0.9956458,0.0021625808,0.00020299763,0.0012239264,0.00061812584,0.00014649943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037520204,0.0023171569,0.0020998288,0.001179892,0.0009989928,0.0015007292,0.0027552815,0.0019559565,0.0022246682],"category_scores_gemma":[0.0119039565,0.0010152004,0.0018441641,0.0010752549,0.0009360513,0.0051268847,0.0041481834,0.004011328,0.001179015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080831745,0.0005986012,0.0065543,0.00024276167,0.0004089232,0.0006128533,0.00055950874,0.39931762,0.02722147,0.0049783844,0.011519102,0.54717815],"study_design_scores_gemma":[0.000019898798,0.00008334356,0.00053478754,0.00001426697,0.00006478669,0.000071190436,0.000027502612,0.98814875,0.0067427987,0.0031428598,0.0011239703,0.000025857167],"about_ca_topic_score_codex":0.009339998,"about_ca_topic_score_gemma":0.011271068,"teacher_disagreement_score":0.009339998,"about_ca_system_score_codex":0.0008231618,"about_ca_system_score_gemma":0.0017303103,"threshold_uncertainty_score":0.019842803},"labels":[],"label_agreement":null},{"id":"W4386576747","doi":"10.18653/v1/2023.findings-eacl.171","title":"ICA-Proto: Iterative Cross Alignment Prototypical Network for Incremental Few-Shot Relation Classification","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Canadian Institute for Advanced Research","funders":"National Key Research and Development Program of China","keywords":"Computer science; Benchmark (surveying); Embedding; Relation (database); Representation (politics); Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Iterative and incremental development; Encoding (memory); Feature vector; Machine learning; Feature learning; Data mining","score_opus":0.08793856194669883,"score_gpt":0.3498732966632659,"score_spread":0.26193473471656703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386576747","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049504243,0.0018215877,0.93419516,0.00038645603,0.00021811083,0.0002727259,0.00088875607,0.008341817,0.0043712244],"genre_scores_gemma":[0.68952984,0.00089257525,0.29016668,0.0008755592,0.00027016693,0.0005217663,0.006300024,0.0004900627,0.010953325],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99912673,0.00015196986,0.000027364242,0.00040459223,0.00018314237,0.00010616797],"domain_scores_gemma":[0.9988399,0.00036323935,0.000094274685,0.0003536591,0.0002596326,0.00008934077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012061548,0.0016097679,0.0014721167,0.0015461456,0.0008478065,0.0011055834,0.003786882,0.0014978952,0.003144906],"category_scores_gemma":[0.0038749052,0.00060736784,0.00087158487,0.0017144474,0.0007505782,0.003764516,0.0021383853,0.0021735635,0.0017478241],"study_design_candidate":"bench_or_experimental","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":[0.00058441143,0.00044917996,0.0039345617,0.00026813397,0.00018096126,0.0003075476,0.00035202346,0.09978874,0.01716616,0.007903584,0.02207186,0.8469927],"study_design_scores_gemma":[0.000012638548,0.00008934053,0.0005980952,0.000012091364,0.00002548447,0.00014707992,0.000054424505,0.9862462,0.0033112399,0.007259982,0.0022271131,0.00001632567],"about_ca_topic_score_codex":0.0073404773,"about_ca_topic_score_gemma":0.010967488,"teacher_disagreement_score":0.0073404773,"about_ca_system_score_codex":0.0010463239,"about_ca_system_score_gemma":0.0010569874,"threshold_uncertainty_score":0.014595509},"labels":[],"label_agreement":null},{"id":"W4386634632","doi":"10.1109/tpami.2023.3302150","title":"Information Bottleneck and Aggregated Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment","keywords":"Information bottleneck method; Learning vector quantization; Artificial intelligence; Computer science; Competitive learning; Artificial neural network; Machine learning; Instance-based learning; Vector quantization; Feature learning; Semi-supervised learning; Quantization (signal processing); Contextual image classification; Pattern recognition (psychology); Image (mathematics); Algorithm; Mutual information","score_opus":0.018304197348079827,"score_gpt":0.25811653967010656,"score_spread":0.23981234232202672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386634632","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":[0.019115215,0.0010552927,0.9757607,0.00075973506,0.00007949674,0.000028316148,0.00008245206,0.00019599393,0.0029228325],"genre_scores_gemma":[0.8566476,0.0014121124,0.13549909,0.00063851004,0.00042980336,0.00018646843,0.000457824,0.00014577151,0.0045827134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99751604,0.0008760381,0.00011711761,0.0006164926,0.0006400131,0.00023430907],"domain_scores_gemma":[0.9929484,0.0042976085,0.00062022614,0.0008903396,0.000924616,0.00031882097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004363865,0.00083408825,0.0019826633,0.0012047631,0.00073433353,0.0022999428,0.0026095726,0.0017519095,0.0025077965],"category_scores_gemma":[0.016784213,0.00052334403,0.00082736654,0.0014029787,0.002459826,0.0054845363,0.0035438857,0.0022229077,0.0004733629],"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":[0.00017444344,0.00015635294,0.0018547374,0.00032829307,0.00023444892,0.00019883897,0.00030478914,0.45856607,0.0021474527,0.44299296,0.0036504255,0.08939112],"study_design_scores_gemma":[0.000009523121,0.00004007931,0.00016418763,0.000013886935,0.000013650402,0.000029189117,0.000015903648,0.73967004,0.0005229731,0.2586786,0.0008317958,0.000010148124],"about_ca_topic_score_codex":0.0026014314,"about_ca_topic_score_gemma":0.0012802595,"teacher_disagreement_score":0.004363865,"about_ca_system_score_codex":0.0018376425,"about_ca_system_score_gemma":0.0012424339,"threshold_uncertainty_score":0.02307862},"labels":[],"label_agreement":null},{"id":"W4386837260","doi":"10.21203/rs.3.rs-3256479/v1","title":"Loss of Plasticity in Deep Continual Learning","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"University of Alberta","funders":"","keywords":"Plasticity; Psychology; Artificial intelligence; Cognitive psychology; Computer science; Materials science","score_opus":0.10707379294038187,"score_gpt":0.40179510588862166,"score_spread":0.2947213129482398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386837260","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":[0.09911915,0.0018988308,0.8930424,0.0015431644,0.00017083235,0.00003443533,0.00013542878,0.00081295357,0.0032428289],"genre_scores_gemma":[0.9397349,0.0008216026,0.049462866,0.00028057062,0.00016260077,0.00008844119,0.00021992101,0.0001638384,0.009065309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959594,0.0001337436,0.00001933854,0.00011888846,0.00008025653,0.000051874173],"domain_scores_gemma":[0.9969703,0.0021533577,0.00013854554,0.0003398094,0.00022226591,0.00017566457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016677578,0.0006542298,0.0010209949,0.0004097315,0.0003443381,0.0008611726,0.001637464,0.0015936451,0.001919601],"category_scores_gemma":[0.009937067,0.00048232023,0.00040516668,0.0004965194,0.0017350205,0.003068677,0.0020892494,0.00301837,0.00034264376],"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":[0.00037233002,0.00021764035,0.0012764161,0.00027948318,0.00009158008,0.00010862934,0.000114687085,0.7327861,0.007101908,0.111897066,0.0049045356,0.14084955],"study_design_scores_gemma":[0.000006589838,0.000029814559,0.00013642988,0.000009548929,0.0000053896115,0.000018214902,0.00000569949,0.95951533,0.00069837813,0.039336424,0.00023304448,0.0000051652064],"about_ca_topic_score_codex":0.0019181436,"about_ca_topic_score_gemma":0.0018741273,"teacher_disagreement_score":0.001919601,"about_ca_system_score_codex":0.00090832694,"about_ca_system_score_gemma":0.00067015854,"threshold_uncertainty_score":0.008819997},"labels":[],"label_agreement":null},{"id":"W4387185222","doi":"10.3233/faia230370","title":"Evolving Dictionary Representation for Few-Shot Class-Incremental Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Carleton University","funders":"","keywords":"Computer science; Forgetting; Artificial intelligence; Class (philosophy); Representation (politics); Machine learning; Session (web analytics); Process (computing); Adaptation (eye); Key (lock); Feature (linguistics)","score_opus":0.09085757404646855,"score_gpt":0.31590198609496656,"score_spread":0.225044412048498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387185222","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013285857,0.00062839006,0.9831213,0.00012711107,0.00006569935,0.00004256291,0.00008905278,0.0007626018,0.0018774543],"genre_scores_gemma":[0.50824255,0.0013191843,0.4755266,0.0004525796,0.0001860191,0.00023120895,0.0012341138,0.00031257086,0.012495278],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999744,0.00004552811,0.000010692549,0.00010179745,0.00007026634,0.000027675758],"domain_scores_gemma":[0.9996507,0.00011926883,0.000026311664,0.00010246505,0.00007467248,0.000026476888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004059163,0.0005618768,0.00073571864,0.00050107687,0.00026768335,0.0006683191,0.0016638933,0.0007480693,0.0024994381],"category_scores_gemma":[0.0014282233,0.00029590237,0.0005697308,0.000726029,0.00046422565,0.0015881275,0.0011555348,0.0014077325,0.00091543194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010370984,0.00012459871,0.00090096565,0.00015198185,0.000074858144,0.0001006756,0.00015540907,0.16335833,0.018208941,0.020319331,0.0088651795,0.78763586],"study_design_scores_gemma":[0.0000050132735,0.000034170982,0.00019602291,0.000005466846,0.000011627067,0.00006657384,0.000013208204,0.9846544,0.003179094,0.00950706,0.0023186286,0.000008790258],"about_ca_topic_score_codex":0.0027746824,"about_ca_topic_score_gemma":0.003627918,"teacher_disagreement_score":0.0027746824,"about_ca_system_score_codex":0.0006006835,"about_ca_system_score_gemma":0.0004946527,"threshold_uncertainty_score":0.008361459},"labels":[],"label_agreement":null},{"id":"W4387211047","doi":"10.1007/978-3-031-43895-0_30","title":"FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":17,"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 British Columbia","funders":"","keywords":"Overfitting; Computer science; Generalization; Interpolation (computer graphics); Personalization; Code (set theory); Artificial intelligence; Machine learning; Maxima and minima; Data mining; Image (mathematics); Artificial neural network","score_opus":0.018088572857526065,"score_gpt":0.24132751816574685,"score_spread":0.22323894530822078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387211047","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":[0.01288542,0.0005251577,0.98039687,0.00010813591,0.00008601571,0.000045514393,0.00015369606,0.0049506132,0.0008485662],"genre_scores_gemma":[0.29174027,0.00050712173,0.69341916,0.00046585104,0.00014093005,0.00018142913,0.0018669708,0.0010426278,0.01063564],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897325,0.00026666504,0.000058361624,0.00040250132,0.00020580726,0.000093429946],"domain_scores_gemma":[0.9981529,0.000791392,0.000055781034,0.0006921764,0.00022413697,0.00008357314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019988338,0.0013719498,0.0022497803,0.0010645422,0.00074757833,0.001226362,0.002914662,0.0024075531,0.0045643887],"category_scores_gemma":[0.0045752297,0.0008524354,0.0018038292,0.0011771119,0.0009118887,0.0037192446,0.0038457192,0.003532781,0.001718703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044057937,0.00037920725,0.0007996005,0.00011825882,0.0002012176,0.00017927088,0.0001743733,0.27471277,0.0077631427,0.0078380555,0.011085548,0.69630796],"study_design_scores_gemma":[0.000011526608,0.000036257057,0.000067508234,0.000007081562,0.000014047835,0.00003358322,0.00001200654,0.98981464,0.002203222,0.007063847,0.0007279044,0.000008397942],"about_ca_topic_score_codex":0.0076957853,"about_ca_topic_score_gemma":0.008744938,"teacher_disagreement_score":0.0076957853,"about_ca_system_score_codex":0.00081156817,"about_ca_system_score_gemma":0.0011031532,"threshold_uncertainty_score":0.015302002},"labels":[],"label_agreement":null},{"id":"W4387560181","doi":"10.48550/arxiv.2310.05566","title":"Aggregated f-average Neural Network applied to Few-Shot Class Incremental Learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Task (project management); Artificial intelligence; Stack (abstract data type); Machine learning; Artificial neural network; Class (philosophy); Simple (philosophy); Engineering","score_opus":0.10516747148867868,"score_gpt":0.20641872271731718,"score_spread":0.1012512512286385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387560181","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04648395,0.00048336136,0.9496328,0.00019035675,0.00010287776,0.000051760093,0.00009148148,0.0014568194,0.0015066861],"genre_scores_gemma":[0.7653535,0.00026635232,0.22991715,0.00021117015,0.000112454916,0.00011060404,0.0003488061,0.000148113,0.0035317615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995697,0.00010635635,0.000023112809,0.00013914901,0.00010672069,0.000054941807],"domain_scores_gemma":[0.99870586,0.0005693686,0.00006871935,0.00023663689,0.00034117268,0.000078243356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016294547,0.0008897956,0.0012216485,0.000819454,0.00055576646,0.0009048683,0.0021673176,0.0014236064,0.0014626079],"category_scores_gemma":[0.0052965684,0.00039118406,0.0006432315,0.0007488082,0.0006268749,0.0022330913,0.0015952642,0.0019699961,0.00041269523],"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":[0.00017512812,0.00017228448,0.0030827813,0.00009174023,0.000177157,0.00014138257,0.00020888264,0.5293188,0.0081263585,0.012637121,0.0034585723,0.44240972],"study_design_scores_gemma":[0.0000016416896,0.000015738853,0.00013519426,0.0000029857256,0.000006386609,0.000011467099,0.0000037956145,0.9951239,0.0009971439,0.0035111918,0.00018670781,0.0000037242457],"about_ca_topic_score_codex":0.0074224262,"about_ca_topic_score_gemma":0.009187019,"teacher_disagreement_score":0.0074224262,"about_ca_system_score_codex":0.00090549275,"about_ca_system_score_gemma":0.00091185747,"threshold_uncertainty_score":0.014758408},"labels":[],"label_agreement":null},{"id":"W4387602764","doi":"10.1007/978-3-031-45857-6_1","title":"Domain Adaptation of MRI Scanners as an Alternative to MRI Harmonization","year":2023,"lang":"gl","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto; University of Calgary; University of Alberta","funders":"","keywords":"Computer science; Domain adaptation; Domain (mathematical analysis); Artificial intelligence; Pairwise comparison; Pattern recognition (psychology); Feature (linguistics); Data mining; Source code; Machine learning; Classifier (UML)","score_opus":0.033374550575928856,"score_gpt":0.2831805343203113,"score_spread":0.24980598374438245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387602764","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008049837,0.00047934902,0.9881981,0.00010802961,0.00008176104,0.00004306836,0.000106226136,0.00121906,0.0017145738],"genre_scores_gemma":[0.2590079,0.0012202138,0.7287374,0.0004024863,0.00024309945,0.00016634702,0.0010576559,0.0011409179,0.008024015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936706,0.0002333929,0.000029534242,0.00019823067,0.00011532584,0.000056424447],"domain_scores_gemma":[0.9985077,0.0005670157,0.00008543717,0.00046587473,0.0003112218,0.000062696665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015568407,0.00076266524,0.0011289024,0.0008831254,0.00033257194,0.0010323756,0.0013086892,0.0013211834,0.0040014847],"category_scores_gemma":[0.0037956762,0.0005052696,0.0009321092,0.0013662714,0.0005667736,0.0012334432,0.0017699257,0.0014408729,0.002009006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005945622,0.00023965165,0.00078583544,0.00027107677,0.00021470601,0.00014423026,0.0001482788,0.12942795,0.0729459,0.009786902,0.007037586,0.7784033],"study_design_scores_gemma":[0.000039235827,0.00021109243,0.0017507899,0.000036474004,0.00011639536,0.0006033138,0.000078335834,0.9273505,0.03764734,0.02053917,0.011576816,0.0000503983],"about_ca_topic_score_codex":0.0011423847,"about_ca_topic_score_gemma":0.0012560477,"teacher_disagreement_score":0.0040014847,"about_ca_system_score_codex":0.00026023953,"about_ca_system_score_gemma":0.00058659905,"threshold_uncertainty_score":0.013386369},"labels":[],"label_agreement":null},{"id":"W4387778634","doi":"10.1016/j.eswa.2023.122151","title":"GAF-Net: Graph attention fusion network for multi-view semi-supervised classification","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Artificial intelligence; Embedding; Graph; Machine learning; Graph embedding; Pattern recognition (psychology); Sensor fusion; Data mining; Theoretical computer science","score_opus":0.06728185403664276,"score_gpt":0.30637809189961035,"score_spread":0.23909623786296758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387778634","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":[0.015544388,0.0010791338,0.9668743,0.00033567025,0.00021710232,0.00016721724,0.0011644841,0.0126148965,0.0020027936],"genre_scores_gemma":[0.34967726,0.0007197179,0.6288664,0.0008463438,0.0002119237,0.00041297454,0.006558107,0.00084509735,0.011862201],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994192,0.00011607181,0.000020956393,0.00024130337,0.00011739301,0.00008515026],"domain_scores_gemma":[0.99924743,0.00023275107,0.000049121925,0.00016407084,0.00024281372,0.00006394206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012714838,0.0017019924,0.0016188495,0.0017158901,0.00083927275,0.00088959927,0.0030715156,0.0028104123,0.00511208],"category_scores_gemma":[0.002586402,0.000708071,0.0012666417,0.0015724286,0.0005800552,0.0018615996,0.0020472119,0.0023665675,0.0025675308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038387658,0.0003418208,0.0011344238,0.00015942023,0.00026943,0.00013997268,0.00009827423,0.11484374,0.00923381,0.005513224,0.032750502,0.8351315],"study_design_scores_gemma":[0.000011552775,0.00004533156,0.00025928594,0.000011168048,0.00002264667,0.000036204154,0.00001276194,0.9891104,0.0025105989,0.0062980354,0.0016697652,0.000012200476],"about_ca_topic_score_codex":0.020511637,"about_ca_topic_score_gemma":0.03155181,"teacher_disagreement_score":0.020511637,"about_ca_system_score_codex":0.001406351,"about_ca_system_score_gemma":0.0012974185,"threshold_uncertainty_score":0.04078448},"labels":[],"label_agreement":null},{"id":"W4387805535","doi":"10.1109/comst.2023.3326399","title":"Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues","year":2023,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Generalization; Wireless; Machine learning; Domain (mathematical analysis); Context (archaeology); Artificial intelligence; Independent and identically distributed random variables; Distributed computing; Telecommunications; Random variable; Mathematics","score_opus":0.13194555430256796,"score_gpt":0.37201645732364724,"score_spread":0.24007090302107928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387805535","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057381466,0.048282452,0.9381881,0.0043368405,0.00034725023,0.000054785214,0.00013211278,0.00022101637,0.0026993079],"genre_scores_gemma":[0.39128336,0.19721976,0.39150286,0.0038454751,0.0074396846,0.00060880295,0.0010718234,0.00039193485,0.0066362703],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997652,0.0010769329,0.00013367944,0.0005458687,0.0004899518,0.00010151956],"domain_scores_gemma":[0.98606354,0.011339426,0.0005140613,0.0011512708,0.00075845193,0.0001733241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063558924,0.0013772753,0.0027414006,0.0012993532,0.00064103625,0.0029926554,0.0032463432,0.0026385102,0.0013968459],"category_scores_gemma":[0.016175607,0.00097655226,0.0015032529,0.0029385514,0.00274391,0.00652571,0.0029837294,0.0070398124,0.00084647496],"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":[0.0000961097,0.00016894093,0.0026483613,0.0013262817,0.0002634706,0.00015730342,0.00041827938,0.33482155,0.0010984938,0.25456977,0.010536192,0.39389524],"study_design_scores_gemma":[0.000011113274,0.000072189934,0.0006880137,0.000283467,0.000044526536,0.00014956006,0.00010938813,0.7002591,0.0007242146,0.28304857,0.014560698,0.000049246915],"about_ca_topic_score_codex":0.003206131,"about_ca_topic_score_gemma":0.001558717,"teacher_disagreement_score":0.0063558924,"about_ca_system_score_codex":0.0017082804,"about_ca_system_score_gemma":0.0013359395,"threshold_uncertainty_score":0.033613622},"labels":[],"label_agreement":null},{"id":"W4387848881","doi":"10.1145/3583780.3615270","title":"Noisy Perturbations for Estimating Query Difficulty in Dense Retrievers","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Computer science; Query expansion; Robustness (evolution); Query optimization; Web query classification; Sargable; Ranking (information retrieval); Web search query; Query language; Query by Example; Data mining; Artificial intelligence; Information retrieval; Search engine","score_opus":0.037518473942802145,"score_gpt":0.2890422928063041,"score_spread":0.25152381886350195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387848881","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":[0.4661939,0.0024841174,0.52155596,0.00031381813,0.00008957132,0.0003252309,0.0019148362,0.005011245,0.0021113346],"genre_scores_gemma":[0.9118378,0.00030074708,0.082254656,0.00013194466,0.00012837419,0.0001105856,0.004033076,0.00016287787,0.001039892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972945,0.0006845089,0.0001917302,0.00062960613,0.0010390538,0.00016057423],"domain_scores_gemma":[0.9931599,0.0036984296,0.0010763034,0.0009997961,0.00084537163,0.0002201934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025436927,0.0009806191,0.0013580867,0.0022783482,0.0004194971,0.0010108155,0.0012563404,0.0010530605,0.0007383515],"category_scores_gemma":[0.017346378,0.0003671271,0.00052865874,0.0018686587,0.00091864116,0.0025697262,0.0011481094,0.0011723512,0.000606549],"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":[0.0024024632,0.0006842212,0.026077878,0.00066458085,0.00037745028,0.0004303532,0.00036782637,0.5472085,0.0817412,0.0037815107,0.007619116,0.328645],"study_design_scores_gemma":[0.00002261619,0.00025151615,0.006123564,0.000009105797,0.000027648879,0.00013633417,0.000046589656,0.9773966,0.012826014,0.0025307226,0.00059717166,0.0000321562],"about_ca_topic_score_codex":0.005252955,"about_ca_topic_score_gemma":0.0050289533,"teacher_disagreement_score":0.005252955,"about_ca_system_score_codex":0.0011323225,"about_ca_system_score_gemma":0.0005883595,"threshold_uncertainty_score":0.01345247},"labels":[],"label_agreement":null},{"id":"W4387870696","doi":"10.1109/icc45041.2023.10278623","title":"Continual Learning-Based MIMO Channel Estimation: A Benchmarking Study","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Channel (broadcasting); Machine learning; MIMO; Coherence (philosophical gambling strategy); Signal-to-noise ratio (imaging); Artificial intelligence; Coherence time; Mean squared error; Fading; Wireless; Algorithm; Statistics; Telecommunications; Mathematics","score_opus":0.02630378771026341,"score_gpt":0.2792729913218266,"score_spread":0.25296920361156316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387870696","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8293909,0.0052477,0.14970121,0.0008065881,0.00019401102,0.00017532299,0.0005832586,0.0019873492,0.011913684],"genre_scores_gemma":[0.97651756,0.00037940984,0.021613702,0.000074470976,0.00004173501,0.000026200898,0.0005597283,0.000043651235,0.00074349705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988067,0.00047926256,0.00007166769,0.00022806179,0.00029088988,0.00012342734],"domain_scores_gemma":[0.99200577,0.0052490486,0.00037545394,0.001184645,0.0009517156,0.00023332072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029446867,0.00076877925,0.00078409375,0.00074208376,0.00034083726,0.0007222638,0.0015182681,0.0011564548,0.0008938331],"category_scores_gemma":[0.0114395125,0.00022019133,0.00040329734,0.000808178,0.00091626047,0.0010624876,0.0010908502,0.0010689272,0.00025144054],"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":[0.0004970129,0.0005647066,0.006179085,0.0004148934,0.0001532838,0.000117984404,0.00008704861,0.89423746,0.0020265772,0.0029534511,0.0022680913,0.09050047],"study_design_scores_gemma":[0.00003062556,0.00030263668,0.0017060004,0.000017036542,0.000013302787,0.00006751777,0.00003030293,0.99439126,0.0019345606,0.0010446262,0.00044769177,0.000014474894],"about_ca_topic_score_codex":0.005464828,"about_ca_topic_score_gemma":0.0041197995,"teacher_disagreement_score":0.005464828,"about_ca_system_score_codex":0.0006695527,"about_ca_system_score_gemma":0.00053297577,"threshold_uncertainty_score":0.015573204},"labels":[],"label_agreement":null},{"id":"W4387947426","doi":"10.48550/arxiv.2310.15709","title":"Causal Representation Learning Made Identifiable by Grouping of Observational Variables","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Precursory Research for Embryonic Science and Technology; Japan Society for the Promotion of Science; Canadian Institute for Advanced Research","keywords":"Identifiability; Latent variable; Causality (physics); Robustness (evolution); Observational study; Representation (politics); Computer science; Machine learning; Causal inference; Consistency (knowledge bases); Confounding; Causal model; Feature learning; Artificial intelligence; Causal structure; Instrumental variable; Econometrics; Mathematics; Statistics","score_opus":0.19394874374157725,"score_gpt":0.23325376261584066,"score_spread":0.03930501887426341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387947426","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067203785,0.00024664568,0.9916357,0.00033950774,0.000026986478,0.00004418501,0.00017232608,0.00032169122,0.0004925038],"genre_scores_gemma":[0.5048335,0.0009675259,0.48515943,0.00078904605,0.00043557497,0.00048237396,0.002839152,0.00030425962,0.0041891574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99528044,0.0023431173,0.00018922077,0.0015740879,0.0004179112,0.0001952634],"domain_scores_gemma":[0.9755324,0.016579501,0.002526797,0.0041349675,0.00081679726,0.000409525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069259643,0.0013443622,0.0020819292,0.0019094761,0.0009499951,0.0018439674,0.003070377,0.002604649,0.0028249223],"category_scores_gemma":[0.03746228,0.0011626916,0.0018479879,0.0017341328,0.0027237805,0.0040285923,0.0037916035,0.004346206,0.0008132949],"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":[0.00041024416,0.00039671586,0.00988746,0.0009221611,0.000555516,0.0004804666,0.0006073841,0.45319217,0.004955188,0.25248176,0.007823183,0.26828778],"study_design_scores_gemma":[0.00003970573,0.000040511684,0.00077316206,0.00005347033,0.00004478247,0.00009022494,0.000040246323,0.7958225,0.0012247501,0.19972621,0.0021175863,0.000026864038],"about_ca_topic_score_codex":0.002970962,"about_ca_topic_score_gemma":0.003326248,"teacher_disagreement_score":0.0069259643,"about_ca_system_score_codex":0.0013710506,"about_ca_system_score_gemma":0.002624179,"threshold_uncertainty_score":0.036628485},"labels":[],"label_agreement":null},{"id":"W4388235557","doi":"10.1109/sampta59647.2023.10301413","title":"Data Imputation with an Autoencoder and MAGIC","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Imputation (statistics); Missing data; Autoencoder; Computer science; Robustness (evolution); Mean squared error; Artificial intelligence; Deep learning; Data mining; Machine learning; Pattern recognition (psychology); Statistics; Mathematics","score_opus":0.06830957920597121,"score_gpt":0.309401058408185,"score_spread":0.2410914792022138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388235557","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046111974,0.00025710388,0.993519,0.00019268076,0.00005931259,0.000021936623,0.00010809493,0.00085460563,0.0003759218],"genre_scores_gemma":[0.22197746,0.00046173247,0.7706912,0.00060960226,0.00022165726,0.00019714398,0.0016786879,0.00030872156,0.003853689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976609,0.0008160678,0.00016606292,0.00073307863,0.0004786714,0.00014528978],"domain_scores_gemma":[0.99512225,0.0018491396,0.00040405427,0.0014303264,0.0010364328,0.00015776238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048893574,0.0010466689,0.0021837146,0.0012453948,0.00085053896,0.001013685,0.00360708,0.0021924397,0.0020557216],"category_scores_gemma":[0.012785305,0.0009900281,0.0015787587,0.0019139496,0.0011025717,0.0029166401,0.0027907582,0.0036865459,0.0011050196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033179083,0.00017491735,0.0063123214,0.000195567,0.0005381117,0.00025562657,0.00023534583,0.41393626,0.003510479,0.025041603,0.01122708,0.5382409],"study_design_scores_gemma":[0.000014011708,0.000042752352,0.00051851134,0.000021131196,0.000029067482,0.000104509745,0.000017660996,0.9781718,0.00169351,0.017368818,0.0019944285,0.000023757331],"about_ca_topic_score_codex":0.004131197,"about_ca_topic_score_gemma":0.0066368016,"teacher_disagreement_score":0.0048893574,"about_ca_system_score_codex":0.00071724877,"about_ca_system_score_gemma":0.001896782,"threshold_uncertainty_score":0.025857687},"labels":[],"label_agreement":null},{"id":"W4388336668","doi":"10.1016/j.patcog.2023.110091","title":"Learning a target-dependent classifier for cross-domain semantic segmentation: Fine-tuning versus meta-learning","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Classifier (UML); Computer science; Artificial intelligence; Domain adaptation; Segmentation; Initialization; Exploit; Pattern recognition (psychology); Machine learning","score_opus":0.11628204876231511,"score_gpt":0.32773212658198375,"score_spread":0.21145007781966862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388336668","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":[0.04366653,0.0008761519,0.94964343,0.00033334357,0.00013431742,0.0000901393,0.00017064277,0.0039320146,0.0011534431],"genre_scores_gemma":[0.6765733,0.00048883964,0.3157669,0.0008372122,0.00017054102,0.00020419343,0.0014823802,0.0008153434,0.003661378],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99860746,0.00026836156,0.00007880476,0.0007275697,0.00014160325,0.00017618692],"domain_scores_gemma":[0.9975314,0.0011825969,0.00013278787,0.0005276321,0.00043932037,0.00018621022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026508789,0.0017840168,0.0028830802,0.0014896146,0.000667268,0.001932283,0.0035233097,0.003467893,0.0019712735],"category_scores_gemma":[0.005397098,0.0007414596,0.0018223103,0.001425085,0.0010872419,0.0032690957,0.0023419834,0.0037537299,0.0015986976],"study_design_candidate":"bench_or_experimental","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":[0.00071516016,0.0007639174,0.003310524,0.0002935378,0.00048392877,0.00014704384,0.00017954853,0.13979425,0.044821,0.0033914994,0.008023106,0.7980765],"study_design_scores_gemma":[0.0000144793485,0.000067877874,0.00042474602,0.000014667963,0.0000483678,0.000051742678,0.00003734741,0.98623765,0.006847481,0.005757014,0.00048452037,0.000014021906],"about_ca_topic_score_codex":0.00429547,"about_ca_topic_score_gemma":0.00560872,"teacher_disagreement_score":0.00429547,"about_ca_system_score_codex":0.0010974804,"about_ca_system_score_gemma":0.0015871262,"threshold_uncertainty_score":0.0140193105},"labels":[],"label_agreement":null},{"id":"W4388535692","doi":"10.21203/rs.3.rs-3557409/v1","title":"Transductive Meta-Learning with Enhanced Feature Ensemble for Few-shot Semantic Segmentation","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Pascal (unit); Segmentation; Benchmark (surveying); Pattern recognition (psychology); Matching (statistics); Machine learning; Shot (pellet); Extractor; Feature (linguistics); Class (philosophy)","score_opus":0.18503424934757254,"score_gpt":0.41484794333128205,"score_spread":0.22981369398370952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388535692","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":[0.042183876,0.0005563782,0.9504715,0.00023117532,0.000078119985,0.000079182304,0.00017147604,0.004787004,0.0014413934],"genre_scores_gemma":[0.7291321,0.00021999158,0.26383567,0.0005536073,0.000119648634,0.00017098975,0.0013677961,0.0004245488,0.0041756006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989599,0.00022511045,0.00004053219,0.00044135354,0.00019670391,0.00013644189],"domain_scores_gemma":[0.99867415,0.00051376474,0.00010470037,0.00031790743,0.00030234575,0.00008713796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016119351,0.0018164607,0.0021285415,0.0014901019,0.0006056839,0.0010806879,0.0032389762,0.0021591852,0.001852525],"category_scores_gemma":[0.0032085273,0.00068643305,0.0014600147,0.0010971898,0.0011196161,0.0030443321,0.0019498856,0.0027437275,0.00091099384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037920958,0.0005156925,0.0022144406,0.00015415077,0.00030315682,0.00021707873,0.00021164402,0.43558758,0.024828482,0.005781142,0.0055823773,0.5242251],"study_design_scores_gemma":[0.0000040812565,0.000045490844,0.00015964563,0.0000050515023,0.000013672721,0.000024703735,0.000011447991,0.992442,0.0034761836,0.0035493814,0.00026106447,0.0000073939586],"about_ca_topic_score_codex":0.0037399177,"about_ca_topic_score_gemma":0.005059,"teacher_disagreement_score":0.0037399177,"about_ca_system_score_codex":0.0012292501,"about_ca_system_score_gemma":0.00083664345,"threshold_uncertainty_score":0.008918941},"labels":[],"label_agreement":null},{"id":"W4388624016","doi":"10.1109/ro-man57019.2023.10309520","title":"How Do Human Users Teach a Continual Learning Robot in Repeated Interactions?","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Human–computer interaction; Robot; Human–robot interaction; Artificial intelligence","score_opus":0.036759286490175735,"score_gpt":0.2997803178653409,"score_spread":0.26302103137516514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388624016","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9783403,0.00039334345,0.015272665,0.00076628214,0.00003308191,0.000105056206,0.00011634442,0.0005351813,0.004437648],"genre_scores_gemma":[0.9894001,0.00027011428,0.007864204,0.00030929322,0.000012924966,0.00009737569,0.00012682792,0.000066818066,0.0018522319],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978568,0.001129833,0.000065996195,0.0004156047,0.00034119582,0.00019059915],"domain_scores_gemma":[0.985926,0.0082855,0.001757198,0.0012729616,0.0015016356,0.0012567977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029435586,0.0004743259,0.0003656254,0.0006642599,0.0005932975,0.0020628907,0.00097154424,0.0010584908,0.0030514377],"category_scores_gemma":[0.02941251,0.00032807438,0.00027557035,0.0002827877,0.0010407061,0.002354657,0.0009109987,0.0006591706,0.0018142357],"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":[0.0011842878,0.0021486527,0.39318803,0.001371082,0.00022107681,0.0009152567,0.10798584,0.0041286363,0.038057365,0.0015312177,0.0095092105,0.43975922],"study_design_scores_gemma":[0.00028504228,0.0075495234,0.5746035,0.0010782658,0.00039794118,0.0048675216,0.21928592,0.058895078,0.0323086,0.013925211,0.08588446,0.00091898756],"about_ca_topic_score_codex":0.0013994571,"about_ca_topic_score_gemma":0.0024255442,"teacher_disagreement_score":0.0030514377,"about_ca_system_score_codex":0.00054019794,"about_ca_system_score_gemma":0.00040939296,"threshold_uncertainty_score":0.015567243},"labels":[],"label_agreement":null},{"id":"W4388706387","doi":"10.1088/1742-5468/ad01b8","title":"Exact learning dynamics of deep linear networks with prior knowledge <sup>*</sup>","year":2023,"lang":"en","type":"article","venue":"Journal of Statistical Mechanics Theory and Experiment","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Wellcome Trust; Wellcome","keywords":"Artificial intelligence; Computer science; Similarity (geometry); Artificial neural network; Deep learning; Machine learning; Class (philosophy); Task (project management)","score_opus":0.011863093124490487,"score_gpt":0.2754090009627654,"score_spread":0.2635459078382749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388706387","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":[0.15588093,0.00026000972,0.8315518,0.0014167452,0.00005488542,0.000051066854,0.000118460055,0.00039411217,0.010271984],"genre_scores_gemma":[0.9533468,0.0001523031,0.041584216,0.00015117998,0.000019777615,0.000073175135,0.0000934515,0.00012113376,0.0044579282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99972004,0.00010151475,0.000013136276,0.000053185333,0.00007282778,0.00003936659],"domain_scores_gemma":[0.9980357,0.0012892754,0.0001990616,0.00019479707,0.00017698255,0.000104144325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014445962,0.0004288782,0.00043605288,0.0003475901,0.00032340287,0.00074493885,0.00086466095,0.0010417695,0.0025100925],"category_scores_gemma":[0.008580155,0.0004388997,0.0004480097,0.00027643237,0.0016076036,0.0024267826,0.0012993155,0.0014960278,0.0003631962],"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":[0.00006516041,0.000038489954,0.0007219169,0.00006577828,0.000020289512,0.00007899976,0.00019226705,0.7477594,0.005295032,0.22774161,0.0013496086,0.016671358],"study_design_scores_gemma":[0.0000023038044,0.000007781594,0.00013421747,0.000006140585,0.000001580785,0.000009958554,0.000009566176,0.94264275,0.0005838231,0.05644341,0.00015350686,0.000004876093],"about_ca_topic_score_codex":0.002705061,"about_ca_topic_score_gemma":0.0019673663,"teacher_disagreement_score":0.002705061,"about_ca_system_score_codex":0.0011805361,"about_ca_system_score_gemma":0.00061273854,"threshold_uncertainty_score":0.008565366},"labels":[],"label_agreement":null},{"id":"W4388726194","doi":"10.1109/iecon51785.2023.10311616","title":"Investigating Continual Learning Strategies in Neural Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Dilemma; Memory consolidation; Machine learning; Stability (learning theory); Consolidation (business); Regularization (linguistics); Deep learning; Recurrent neural network; Psychology; Neuroscience","score_opus":0.029709594800834186,"score_gpt":0.27317571629231185,"score_spread":0.24346612149147767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388726194","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6543428,0.0006756322,0.339908,0.0006816235,0.000026087791,0.000069655136,0.000016452032,0.00015502868,0.0041248],"genre_scores_gemma":[0.9845531,0.00013330861,0.014517996,0.000022668548,0.000009845938,0.00003797781,0.000007864041,0.0000116576675,0.00070551503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954575,0.00019509105,0.000026839005,0.00009553826,0.00007836661,0.000058388996],"domain_scores_gemma":[0.9955113,0.0032018237,0.00043031084,0.0003741109,0.00028876096,0.00019372578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022017383,0.00041044614,0.00044142036,0.00048505477,0.00031746973,0.0010422335,0.0012265589,0.000984761,0.0012927739],"category_scores_gemma":[0.012073363,0.00032583662,0.000353911,0.000263285,0.0018192468,0.0030381095,0.0012935682,0.0011091882,0.00013130196],"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":[0.00023354136,0.00036454058,0.008672784,0.00028822204,0.0002013265,0.00035846408,0.0010664853,0.6633437,0.01918322,0.22565977,0.00039757142,0.080230355],"study_design_scores_gemma":[0.000011234855,0.00009566682,0.0006532695,0.000013316476,0.000009710058,0.00004181431,0.00008716372,0.91308314,0.0017409045,0.08404608,0.00020517249,0.000012512439],"about_ca_topic_score_codex":0.00076625595,"about_ca_topic_score_gemma":0.0005780052,"teacher_disagreement_score":0.0022017383,"about_ca_system_score_codex":0.00058418536,"about_ca_system_score_gemma":0.0003955799,"threshold_uncertainty_score":0.011644006},"labels":[],"label_agreement":null},{"id":"W4388739641","doi":"10.2139/ssrn.4634958","title":"S2match: Self-Paced Sampling for Data-Limited Semi-Supervised Learning","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Sampling (signal processing); Computer science; Semi-supervised learning; Artificial intelligence; Machine learning; Statistics; Psychology; Mathematics; Computer vision","score_opus":0.09530627498332121,"score_gpt":0.32460915399096796,"score_spread":0.22930287900764673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388739641","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008057544,0.00025955084,0.9849562,0.000099932055,0.00012332834,0.00014903955,0.00021037758,0.005738107,0.0004059007],"genre_scores_gemma":[0.1949002,0.00016482212,0.7954758,0.00055439037,0.00020944963,0.0007184307,0.002524571,0.0016392843,0.0038131154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972664,0.0011136264,0.00014694402,0.0007841899,0.0005376435,0.00015127058],"domain_scores_gemma":[0.9929668,0.0042360513,0.00020434753,0.0014606504,0.0007149666,0.0004172941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005171672,0.0013350593,0.0023439517,0.001108399,0.0008366777,0.0013921566,0.0050189206,0.0032030405,0.0061538345],"category_scores_gemma":[0.018223185,0.0009953205,0.0010306083,0.0012457577,0.0011656777,0.002546191,0.0044085244,0.0031731564,0.0029260474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017694355,0.0007970889,0.0027977705,0.0004740462,0.00037498833,0.0002573108,0.00034082745,0.1881361,0.015176385,0.015356623,0.022932611,0.7515868],"study_design_scores_gemma":[0.00005404732,0.000077149016,0.0001419767,0.000009234456,0.000010253303,0.000036508565,0.000014147897,0.9894386,0.0023426763,0.0066629327,0.0012028896,0.000009520896],"about_ca_topic_score_codex":0.0031174142,"about_ca_topic_score_gemma":0.006377564,"teacher_disagreement_score":0.0061538345,"about_ca_system_score_codex":0.0006855214,"about_ca_system_score_gemma":0.0017557782,"threshold_uncertainty_score":0.027350724},"labels":[],"label_agreement":null},{"id":"W4388748447","doi":"10.48550/arxiv.2311.09175","title":"Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Pipeline (software); Ranking (information retrieval); Artificial intelligence; Fuse (electrical); Filter (signal processing); Weighting; Query expansion; Data mining; Information retrieval; Computer vision","score_opus":0.09350871488186248,"score_gpt":0.2233487573134619,"score_spread":0.1298400424315994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388748447","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":[0.38978422,0.0072336597,0.56126076,0.0018062157,0.00054115977,0.00042517294,0.0018227445,0.025724571,0.011401485],"genre_scores_gemma":[0.86504817,0.0008093751,0.118499205,0.0010917637,0.00025234267,0.00013929946,0.0032178785,0.0006375272,0.01030446],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973808,0.0008301227,0.00018479773,0.0007503926,0.0005775576,0.00027633694],"domain_scores_gemma":[0.99436814,0.002355738,0.00030844723,0.0016959761,0.0010378395,0.00023387233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046439306,0.0018585302,0.0015253642,0.0017054084,0.0005616238,0.0012773819,0.0019320293,0.0014425383,0.0037794362],"category_scores_gemma":[0.015055549,0.000554329,0.0010381844,0.0010554006,0.00089454406,0.0044138436,0.0021857312,0.0018947275,0.0034505595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013135988,0.0011507154,0.012651353,0.0005883114,0.00039301638,0.000349879,0.00046458156,0.09775239,0.044535603,0.006225169,0.023504538,0.81107086],"study_design_scores_gemma":[0.00014290355,0.00104646,0.0058800257,0.00005927921,0.00022426338,0.00059721206,0.00023085966,0.941507,0.033873413,0.00821428,0.00811054,0.00011383719],"about_ca_topic_score_codex":0.0056944485,"about_ca_topic_score_gemma":0.009114705,"teacher_disagreement_score":0.0056944485,"about_ca_system_score_codex":0.0009670698,"about_ca_system_score_gemma":0.0012057412,"threshold_uncertainty_score":0.024559736},"labels":[],"label_agreement":null},{"id":"W4388999256","doi":"10.1007/978-981-99-8141-0_10","title":"LDW-RS Loss: Label Density-Weighted Loss with Ranking Similarity Regularization for Imbalanced Deep Fetal Brain Age Regression","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Regression; Similarity (geometry); Artificial intelligence; Ranking (information retrieval); Regularization (linguistics); Pattern recognition (psychology); Computer science; Machine learning; Mathematics; Statistics","score_opus":0.03795491185278576,"score_gpt":0.29141419559120224,"score_spread":0.2534592837384165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388999256","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":[0.004547986,0.0011829948,0.9884416,0.00047556305,0.00020000493,0.00007659368,0.0005589097,0.0027363452,0.0017801474],"genre_scores_gemma":[0.18852541,0.002193523,0.75691897,0.0011961152,0.0005308379,0.00057471043,0.00613409,0.0024841262,0.04144227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989722,0.0003192156,0.00005213621,0.00024398945,0.000320376,0.00009209388],"domain_scores_gemma":[0.99914634,0.00028532,0.000064572385,0.00019029825,0.00025548277,0.000057983412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003323456,0.0018245383,0.0018877203,0.0010302042,0.0005266537,0.0014976491,0.0038447748,0.0026758104,0.005563489],"category_scores_gemma":[0.004744637,0.00060845277,0.0010237152,0.0012059491,0.0009286087,0.0025155952,0.0026073658,0.0036147095,0.0039969375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002510526,0.00029698617,0.00095443253,0.00022994025,0.00017358738,0.000103951344,0.0000732253,0.17935473,0.00648294,0.026898976,0.07278965,0.7123905],"study_design_scores_gemma":[0.000016591595,0.000054840562,0.00021151522,0.00002970654,0.000018407552,0.000074780946,0.000013492526,0.97796714,0.002587127,0.014047072,0.0049624285,0.000016872287],"about_ca_topic_score_codex":0.0042866245,"about_ca_topic_score_gemma":0.0060540433,"teacher_disagreement_score":0.005563489,"about_ca_system_score_codex":0.001185823,"about_ca_system_score_gemma":0.0017151282,"threshold_uncertainty_score":0.01861173},"labels":[],"label_agreement":null},{"id":"W4389042697","doi":"10.1016/j.patrec.2023.11.026","title":"Basis scaling and double pruning for efficient inference in network-based transfer learning","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"McMaster University","funders":"","keywords":"Pruning; Computer science; MNIST database; Basis (linear algebra); Inference; Artificial intelligence; Reduction (mathematics); Transfer of learning; Deep learning; Algorithm; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.048604363962606645,"score_gpt":0.2698073820698968,"score_spread":0.22120301810729015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389042697","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":[0.013500656,0.0002976252,0.98444587,0.00015481036,0.00006174356,0.000028868963,0.000051377156,0.00079618173,0.0006628157],"genre_scores_gemma":[0.491844,0.00034747663,0.50182897,0.00026364357,0.00015395891,0.00029269006,0.0005048246,0.00037367461,0.0043906523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991227,0.00028283117,0.000072675924,0.00018814008,0.00022555933,0.00010811676],"domain_scores_gemma":[0.9961261,0.0023369694,0.00010511288,0.0007933795,0.00050166616,0.00013678652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021061844,0.00082203303,0.0020982558,0.0008721519,0.0008127766,0.0010091407,0.0028097176,0.0018288576,0.0034010604],"category_scores_gemma":[0.010137131,0.00074632163,0.0008086644,0.0010139284,0.0010643638,0.002721956,0.0026572163,0.003224709,0.0009988691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045173903,0.000327654,0.0010775961,0.00018382668,0.00013081878,0.0001631613,0.00015894134,0.35906637,0.012061618,0.050248772,0.0061452356,0.56998426],"study_design_scores_gemma":[0.000009549016,0.000017420765,0.0000721997,0.000004845056,0.0000077815885,0.0000152030825,0.000006637575,0.98255116,0.0015225537,0.015531374,0.00025683307,0.0000043689815],"about_ca_topic_score_codex":0.005492214,"about_ca_topic_score_gemma":0.009025119,"teacher_disagreement_score":0.005492214,"about_ca_system_score_codex":0.0007235456,"about_ca_system_score_gemma":0.0015007557,"threshold_uncertainty_score":0.011377692},"labels":[],"label_agreement":null},{"id":"W4389163154","doi":"10.1145/3611643.3616244","title":"On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Computer science; Robustness (evolution); Encoder; Software; Artificial intelligence; Machine learning; Code (set theory); Language model; Source code; Programming language","score_opus":0.041076711412266716,"score_gpt":0.30036186199549797,"score_spread":0.25928515058323126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389163154","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":[0.061390348,0.0014854403,0.9308988,0.0014371332,0.0001222971,0.00009754887,0.000118970216,0.0022603339,0.0021891405],"genre_scores_gemma":[0.82270455,0.001581395,0.16758363,0.0013791549,0.00020223853,0.00029009944,0.0005694717,0.0005076122,0.005181841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983553,0.0004880661,0.00010732024,0.0005805776,0.0003108967,0.00015774873],"domain_scores_gemma":[0.984574,0.011412704,0.00068148656,0.0019331727,0.0010647848,0.00033388243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005131579,0.0023333358,0.0012054472,0.0008680294,0.00074437645,0.0014591384,0.003032609,0.0020524168,0.0017326996],"category_scores_gemma":[0.025582734,0.0011607065,0.0011685397,0.0007722221,0.0026112662,0.0045131957,0.0027199257,0.006475526,0.0009123954],"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":[0.00029295226,0.00020527102,0.0044339155,0.00017985687,0.00017912971,0.00027232568,0.00037585676,0.74595946,0.007088678,0.013037217,0.0028785144,0.2250968],"study_design_scores_gemma":[0.000010497709,0.000063713516,0.0003025131,0.000022949076,0.00001493852,0.000043713884,0.000015726528,0.99134594,0.0020391748,0.005741098,0.00038626188,0.000013482487],"about_ca_topic_score_codex":0.012331308,"about_ca_topic_score_gemma":0.015244548,"teacher_disagreement_score":0.012331308,"about_ca_system_score_codex":0.0015505295,"about_ca_system_score_gemma":0.002220168,"threshold_uncertainty_score":0.02713865},"labels":[],"label_agreement":null},{"id":"W4389196652","doi":"10.1007/978-3-031-47401-9_1","title":"Continual-GEN: Continual Group Ensembling for Domain-agnostic Skin Lesion Classification","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Subnetwork; Computer science; Artificial intelligence; Inference; Forgetting; Machine learning; Domain (mathematical analysis); Metric (unit); Deep learning; Similarity (geometry); Field (mathematics); Pattern recognition (psychology); Image (mathematics)","score_opus":0.04670565894918269,"score_gpt":0.28449793403996226,"score_spread":0.23779227509077958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389196652","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":[0.013324189,0.0009191213,0.9752143,0.0001559464,0.00022139988,0.00009143203,0.00026360905,0.0075611314,0.002248874],"genre_scores_gemma":[0.21525039,0.00039341263,0.76860815,0.00036297124,0.00016571925,0.00018221888,0.0018507038,0.0010478065,0.01213848],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995034,0.0001522034,0.000021985714,0.0001673141,0.000109175606,0.000045885045],"domain_scores_gemma":[0.9993212,0.00028975552,0.000024650037,0.00017725902,0.0001450826,0.00004200745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018930085,0.00087127025,0.0010301931,0.00076536735,0.00044002995,0.00069546804,0.0014758285,0.0014910805,0.0051569683],"category_scores_gemma":[0.0024001924,0.00042878705,0.00074484374,0.00050346775,0.0005394719,0.0010167651,0.0014234667,0.0015704616,0.0024620031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035188918,0.00015572172,0.0009316406,0.00014956786,0.00013659606,0.00011449571,0.000119382545,0.06964776,0.018589996,0.008252731,0.022903653,0.87864655],"study_design_scores_gemma":[0.00001766691,0.00009336317,0.00047940432,0.00002377229,0.0000226443,0.00011128368,0.000020226722,0.9783846,0.0062950826,0.010365131,0.0041703475,0.000016434078],"about_ca_topic_score_codex":0.0019850838,"about_ca_topic_score_gemma":0.0050574616,"teacher_disagreement_score":0.0051569683,"about_ca_system_score_codex":0.0004135297,"about_ca_system_score_gemma":0.00049788126,"threshold_uncertainty_score":0.01725179},"labels":[],"label_agreement":null},{"id":"W4389272161","doi":"10.1007/978-3-031-47966-3_14","title":"Regularized Meta-Training with Embedding Mixup for Improved Few-Shot Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of British Columbia","funders":"","keywords":"Embedding; Computer science; Generalization; Artificial intelligence; Regularization (linguistics); Machine learning; Metric (unit); Shot (pellet); Set (abstract data type); Training set; Domain (mathematical analysis); GRASP; One shot; Range (aeronautics); Mathematics","score_opus":0.07115953134820122,"score_gpt":0.2897249329974847,"score_spread":0.21856540164928348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389272161","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":[0.011121423,0.0017856859,0.980631,0.00020545468,0.00021646947,0.00007385224,0.00030580908,0.0044114524,0.0012488659],"genre_scores_gemma":[0.313459,0.00094938197,0.6618625,0.00095914304,0.0003662718,0.00045369277,0.0042231125,0.0016884339,0.016038409],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886453,0.00030095997,0.00006562236,0.00043571612,0.00019609068,0.00013712201],"domain_scores_gemma":[0.9973598,0.001573573,0.00008653599,0.0005196038,0.00031593777,0.00014449836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019340679,0.0021070384,0.0031834396,0.001263684,0.00075303344,0.001385129,0.004441912,0.003815529,0.006877049],"category_scores_gemma":[0.0056739068,0.0011351375,0.0017315875,0.0015119265,0.00090935745,0.0035790321,0.0033992575,0.004647787,0.0036527815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048861705,0.0004436285,0.0005827579,0.00033145852,0.00030559045,0.00017462639,0.00014530262,0.23150627,0.016280493,0.0060996804,0.016139971,0.7275016],"study_design_scores_gemma":[0.000012921834,0.000056505527,0.00008767937,0.000016624623,0.000024947425,0.000039043567,0.0000139268695,0.99172956,0.0026736339,0.004547091,0.0007855729,0.000012426179],"about_ca_topic_score_codex":0.0053119683,"about_ca_topic_score_gemma":0.009118018,"teacher_disagreement_score":0.006877049,"about_ca_system_score_codex":0.0008081875,"about_ca_system_score_gemma":0.0013137319,"threshold_uncertainty_score":0.023005962},"labels":[],"label_agreement":null},{"id":"W4389500881","doi":"10.48550/arxiv.2312.03911","title":"Improving Gradient-guided Nested Sampling for Posterior Inference","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Samsung; Alliance de recherche numérique du Canada; Genentech; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Computer science; Cluster analysis; Algorithm; Sampling (signal processing); Inference; Curse of dimensionality; Artificial intelligence","score_opus":0.2399513012788558,"score_gpt":0.2536179828552966,"score_spread":0.01366668157644077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389500881","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046103587,0.000084275984,0.99409604,0.000059408987,0.000016266129,0.000029127888,0.000036826685,0.00062494975,0.00044263178],"genre_scores_gemma":[0.21162489,0.00013412704,0.7842994,0.00027012237,0.000073186624,0.00022665164,0.0006560923,0.0007190373,0.0019966299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987147,0.00056417333,0.000041907475,0.00025463745,0.0003217007,0.000102915015],"domain_scores_gemma":[0.9957534,0.0029371919,0.00013772881,0.0005158454,0.00043761905,0.00021822091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002724301,0.0013623617,0.0015979396,0.0009517949,0.00065418833,0.0010933435,0.002896657,0.0014613263,0.0031467376],"category_scores_gemma":[0.016253121,0.00082632183,0.0009964164,0.00084928895,0.0013353836,0.0022710196,0.0025386792,0.0027087217,0.0013140736],"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":[0.00035964567,0.00024653462,0.0018299725,0.0001876653,0.00013156867,0.00014468055,0.00030612067,0.7116658,0.0070187766,0.07651492,0.005850523,0.1957437],"study_design_scores_gemma":[0.000014083105,0.000010453914,0.000037270187,0.000003807103,0.0000031830534,0.000011112491,0.0000037797781,0.9864884,0.00046605073,0.012679951,0.00027819988,0.0000036731317],"about_ca_topic_score_codex":0.008558537,"about_ca_topic_score_gemma":0.012822759,"teacher_disagreement_score":0.008558537,"about_ca_system_score_codex":0.0010704829,"about_ca_system_score_gemma":0.0022471435,"threshold_uncertainty_score":0.017017424},"labels":[],"label_agreement":null},{"id":"W4389520055","doi":"10.18653/v1/2023.findings-emnlp.423","title":"How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":35,"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 Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Relevance (law); Generative grammar; Encoder; Machine learning; Contrast (vision); Training set; Information retrieval","score_opus":0.07580167410820143,"score_gpt":0.30194154710098803,"score_spread":0.2261398729927866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389520055","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":[0.11487513,0.0024721557,0.850746,0.00087460544,0.00025122362,0.0004142957,0.0011109026,0.023377802,0.0058779637],"genre_scores_gemma":[0.5663275,0.00062079716,0.41410148,0.0020498838,0.00020222405,0.000371854,0.0051737297,0.0012704624,0.009882098],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988493,0.00035412883,0.00005395596,0.00042238145,0.00019564405,0.00012457307],"domain_scores_gemma":[0.9980171,0.00094265636,0.00006847824,0.00064556784,0.0002303312,0.0000958192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022272184,0.0016061576,0.001532471,0.00076552934,0.000483383,0.0010791585,0.002312755,0.0016891893,0.0037481526],"category_scores_gemma":[0.0064131483,0.0006553889,0.0010238137,0.0006399296,0.0011726364,0.0039308867,0.0024683843,0.0027236445,0.002521056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010507584,0.00081155973,0.0027083382,0.0007896031,0.0002802622,0.00050761807,0.00058858393,0.19084589,0.06479619,0.009991952,0.03785067,0.68977857],"study_design_scores_gemma":[0.00011793909,0.0004024965,0.000524087,0.00003312259,0.00005832281,0.00034236565,0.00011045697,0.962905,0.020794291,0.00870973,0.005956622,0.000045600973],"about_ca_topic_score_codex":0.0056742057,"about_ca_topic_score_gemma":0.008945586,"teacher_disagreement_score":0.0056742057,"about_ca_system_score_codex":0.00073737476,"about_ca_system_score_gemma":0.00093225803,"threshold_uncertainty_score":0.012538791},"labels":[],"label_agreement":null},{"id":"W4389520153","doi":"10.18653/v1/2023.findings-emnlp.392","title":"Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Korea Evaluation Institute of Industrial Technology; Ministry of Science and ICT, South Korea; Ministry of Trade, Industry and Energy; Seoul National University; Hanyang University","keywords":"Domain adaptation; Computer science; Robustness (evolution); Leverage (statistics); Artificial intelligence; Testbed; Generalizability theory; Adaptation (eye); Viewpoints; Machine learning; Benchmark (surveying); Natural language processing; Human–computer interaction; Cartography","score_opus":0.04723287929223613,"score_gpt":0.2601785977514434,"score_spread":0.2129457184592073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389520153","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":[0.062247906,0.0009812063,0.93028337,0.00043741058,0.000116576426,0.000115509814,0.0003586546,0.0038343123,0.0016250125],"genre_scores_gemma":[0.73622406,0.00043934322,0.25795138,0.00054285774,0.00015242945,0.00029979195,0.0017032755,0.0004895493,0.0021972682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795234,0.0007740847,0.00012837835,0.0007529889,0.00024145204,0.00015072494],"domain_scores_gemma":[0.99317896,0.004317409,0.0004498708,0.0013432296,0.00048124415,0.00022923015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003525867,0.0011603715,0.0011236068,0.0012333903,0.0008937777,0.0014953065,0.0015701947,0.0016382431,0.0017853753],"category_scores_gemma":[0.015858,0.00042691562,0.0009335543,0.0010077227,0.0014961539,0.0030408027,0.003139924,0.0036181512,0.00088319945],"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":[0.00048765354,0.00041136198,0.00648881,0.0005063958,0.00024335716,0.00053479255,0.0006525183,0.51099205,0.024306023,0.013794087,0.009373835,0.43220913],"study_design_scores_gemma":[0.000016284363,0.000054989923,0.0008550459,0.000018002866,0.000013058424,0.00014421155,0.00008949749,0.9765692,0.0056054485,0.014810526,0.0018034759,0.00002028306],"about_ca_topic_score_codex":0.0035296937,"about_ca_topic_score_gemma":0.0031522408,"teacher_disagreement_score":0.0035296937,"about_ca_system_score_codex":0.0010473876,"about_ca_system_score_gemma":0.0012639349,"threshold_uncertainty_score":0.018646777},"labels":[],"label_agreement":null},{"id":"W4389542237","doi":"10.1109/embc40787.2023.10340049","title":"Class-imbalanced Unsupervised and Semi-Supervised Domain Adaptation for Histopathology Images","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Waterloo","funders":"","keywords":"Computer science; Leverage (statistics); Domain adaptation; Artificial intelligence; Transfer of learning; Labeled data; Machine learning; Domain (mathematical analysis); Adaptation (eye); Pattern recognition (psychology); Supervised learning; Semi-supervised learning; Class (philosophy); Classifier (UML); Artificial neural network; Mathematics","score_opus":0.028024267503909128,"score_gpt":0.2606612081500057,"score_spread":0.23263694064609658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389542237","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":[0.07986588,0.0005578092,0.9164593,0.00024262928,0.000072481365,0.00012255779,0.00020748709,0.0012355474,0.0012363106],"genre_scores_gemma":[0.74694955,0.00037013422,0.24672894,0.00030440866,0.00012010163,0.00025871638,0.0013644458,0.0001635162,0.0037401936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901617,0.0003482588,0.000042777592,0.00033555247,0.00017646373,0.00008070165],"domain_scores_gemma":[0.99844533,0.0005983232,0.00017558756,0.00037935484,0.0003064107,0.00009511652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023233704,0.0006713974,0.0007337721,0.001043643,0.0004215046,0.00073979003,0.0013215006,0.0010496951,0.0007320024],"category_scores_gemma":[0.0034307956,0.00028723557,0.00088623585,0.00083018746,0.00088493805,0.0011499943,0.0010122712,0.001274095,0.00050888554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005104499,0.00048448544,0.008008196,0.00026807777,0.00019311848,0.0003378166,0.0003949823,0.4077112,0.04495205,0.005689492,0.0062955706,0.5251546],"study_design_scores_gemma":[0.00001151843,0.0000677482,0.002072507,0.000012946184,0.000014603324,0.00014799756,0.000050716197,0.98014015,0.010642272,0.0054024556,0.0014174912,0.000019565518],"about_ca_topic_score_codex":0.0016884354,"about_ca_topic_score_gemma":0.0022974855,"teacher_disagreement_score":0.0023233704,"about_ca_system_score_codex":0.0006241246,"about_ca_system_score_gemma":0.0006763673,"threshold_uncertainty_score":0.012287319},"labels":[],"label_agreement":null},{"id":"W4389990420","doi":"10.1016/j.patcog.2023.110213","title":"TFS-ViT: Token-level feature stylization for domain generalization","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Artificial Intelligence in Medicine (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Normalization (sociology); Security token; Artificial intelligence; Generalization; Convolutional neural network; Feature (linguistics); Pattern recognition (psychology); Transformer; Machine learning; Mathematics","score_opus":0.08098606940649306,"score_gpt":0.28775043936422806,"score_spread":0.206764369957735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389990420","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":[0.008708102,0.00038151967,0.91622823,0.00013829207,0.00030352606,0.00017344722,0.002996262,0.06858474,0.002485831],"genre_scores_gemma":[0.19135548,0.0004263791,0.7694466,0.0004342448,0.00019244797,0.0003978166,0.018240636,0.0049041687,0.014602123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901736,0.00016253442,0.000055043314,0.0004540645,0.00019695156,0.000114075345],"domain_scores_gemma":[0.99859196,0.0003347667,0.000046907433,0.0007479489,0.0002029965,0.00007540627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011372725,0.0015611233,0.0016990806,0.0017869862,0.00072544045,0.0012929954,0.0031940448,0.0015265638,0.01846399],"category_scores_gemma":[0.003585498,0.0007409302,0.0016072876,0.0015157589,0.00073459,0.0027324841,0.003239221,0.0029875676,0.012445415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006293655,0.00022954248,0.0010384056,0.00025545678,0.00015061187,0.00021094517,0.00010837629,0.018404,0.030393848,0.009113762,0.060465507,0.87900007],"study_design_scores_gemma":[0.00008900867,0.00021199428,0.0012524714,0.00004462578,0.00008275674,0.00040157803,0.00009582479,0.871339,0.061498944,0.034228306,0.030676227,0.00007924178],"about_ca_topic_score_codex":0.004391371,"about_ca_topic_score_gemma":0.0077351104,"teacher_disagreement_score":0.01846399,"about_ca_system_score_codex":0.00080657384,"about_ca_system_score_gemma":0.0012618509,"threshold_uncertainty_score":0.061768234},"labels":[],"label_agreement":null},{"id":"W4390450951","doi":"10.1145/3617233.3617240","title":"Entropy-based Sampling for Streaming learning with Move-to-Data approach on Video","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Retargeting; Computer science; Forgetting; Artificial intelligence; Deep learning; Convolutional neural network; Machine learning; Entropy (arrow of time); Artificial neural network; Transformer; On the fly; Streaming data; Data mining","score_opus":0.09427279541616039,"score_gpt":0.31023415501629137,"score_spread":0.21596135960013096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390450951","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":[0.027627693,0.00048733925,0.9689114,0.00020482295,0.00006385651,0.00008812804,0.00018963558,0.0014809922,0.00094613835],"genre_scores_gemma":[0.6994946,0.00042490198,0.29389587,0.0003217788,0.00015818076,0.00022426236,0.0012303293,0.00036142248,0.0038886531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995142,0.00012177914,0.00002885067,0.00015262542,0.00013118082,0.00005140467],"domain_scores_gemma":[0.9985201,0.0008208703,0.00008698877,0.000206863,0.00026188066,0.00010328823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013830549,0.00090578175,0.00095962937,0.00076909724,0.00035079374,0.0006466835,0.0019494968,0.00092565874,0.0024447783],"category_scores_gemma":[0.005258056,0.00039116768,0.00059478625,0.0006533618,0.00073785946,0.0018434953,0.0013404908,0.0014810693,0.0004605857],"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":[0.0003869338,0.00018918495,0.0021653366,0.00018135051,0.00007664348,0.00018072981,0.00013750706,0.6384218,0.009247288,0.015792822,0.0051201773,0.32810023],"study_design_scores_gemma":[0.000007547547,0.000032647826,0.00011888069,0.0000029993564,0.0000038936605,0.00001681895,0.0000061819383,0.9949419,0.0012690704,0.003263516,0.0003325778,0.000003834194],"about_ca_topic_score_codex":0.0049418365,"about_ca_topic_score_gemma":0.0054328847,"teacher_disagreement_score":0.0049418365,"about_ca_system_score_codex":0.0010619522,"about_ca_system_score_gemma":0.0008331898,"threshold_uncertainty_score":0.009826183},"labels":[],"label_agreement":null},{"id":"W4390481570","doi":"10.1109/sipaim56729.2023.10373522","title":"Moment-alignment domain adaptation in the few-shot and low-resource context","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Hospital for Sick Children","funders":"HORIZON EUROPE Health","keywords":"Domain adaptation; Computer science; Adaptation (eye); Domain (mathematical analysis); Matching (statistics); Moment (physics); Context (archaeology); Set (abstract data type); Artificial intelligence; Resource (disambiguation); Pattern recognition (psychology); Data mining; Machine learning; Algorithm; Mathematics; Statistics","score_opus":0.03677872169227351,"score_gpt":0.2579231320490851,"score_spread":0.22114441035681162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390481570","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":[0.013875768,0.00037628532,0.983871,0.00014633093,0.000080568214,0.000032025306,0.000111534486,0.0008923112,0.00061414414],"genre_scores_gemma":[0.46800324,0.0006918964,0.52322435,0.0004907441,0.00031616542,0.00022003514,0.0018243912,0.0005541824,0.0046749664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905795,0.0002724509,0.000039940078,0.00037920897,0.0001722949,0.00007815638],"domain_scores_gemma":[0.99803215,0.0009436394,0.00015413482,0.00050873397,0.00026307482,0.000098201366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016373822,0.0011263845,0.0013346621,0.00077230344,0.00057381263,0.0009901046,0.0017845852,0.0012233263,0.0023023665],"category_scores_gemma":[0.006734902,0.0005476027,0.0009292331,0.0010122745,0.0010489653,0.0020578348,0.0021613087,0.0027939063,0.0014756317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005764134,0.00032050296,0.0036062342,0.00040615257,0.00030582616,0.00035109237,0.00036961288,0.29954943,0.08093907,0.016629392,0.010139768,0.58680654],"study_design_scores_gemma":[0.000021652304,0.00009363376,0.0016606655,0.000025293162,0.00004012532,0.0002457088,0.000063489446,0.95152754,0.017556697,0.024463901,0.004251358,0.000049964583],"about_ca_topic_score_codex":0.0024630586,"about_ca_topic_score_gemma":0.0035449013,"teacher_disagreement_score":0.0024630586,"about_ca_system_score_codex":0.00047228366,"about_ca_system_score_gemma":0.0011654972,"threshold_uncertainty_score":0.008659422},"labels":[],"label_agreement":null},{"id":"W4390636011","doi":"10.1016/j.neunet.2024.106112","title":"Preserving domain private information via mutual information maximization","year":2024,"lang":"en","type":"article","venue":"Neural Networks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"Mitacs","keywords":"Mutual information; Computer science; Artificial intelligence; Domain (mathematical analysis); Generalization; Divergence (linguistics); Maximization; Pattern recognition (psychology); Machine learning; Mathematics; Mathematical optimization","score_opus":0.0072716715606734195,"score_gpt":0.21128315247051915,"score_spread":0.20401148090984572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390636011","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":[0.0067749573,0.00019669616,0.9919979,0.00023338453,0.000017654722,0.000015950138,0.00005849745,0.00012927507,0.00057563494],"genre_scores_gemma":[0.6835726,0.0009470721,0.30808347,0.0004899687,0.00027773168,0.00021367682,0.0005769104,0.00026392762,0.0055747284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972097,0.0013676874,0.00009764977,0.0006012535,0.00054938876,0.00017423117],"domain_scores_gemma":[0.99066925,0.0063303644,0.00052557845,0.0017796918,0.00046161143,0.00023352692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037935562,0.0011143023,0.0024253202,0.0012234575,0.00071893114,0.0017038991,0.0026028263,0.002324017,0.00145339],"category_scores_gemma":[0.014169961,0.00084466697,0.001105335,0.001448378,0.002885247,0.0060419813,0.0046844827,0.0033491561,0.00043920233],"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":[0.00046963652,0.00027699638,0.0010752763,0.00031612822,0.00032404225,0.0001823113,0.00021669472,0.608396,0.008003367,0.22815093,0.0056837946,0.14690489],"study_design_scores_gemma":[0.000012211946,0.00002916943,0.00012871015,0.0000102506,0.00001645461,0.000046144138,0.000012807778,0.8714084,0.0013762183,0.12651159,0.00043182494,0.00001629029],"about_ca_topic_score_codex":0.0013235372,"about_ca_topic_score_gemma":0.0012306173,"teacher_disagreement_score":0.0037935562,"about_ca_system_score_codex":0.0011132217,"about_ca_system_score_gemma":0.0013829955,"threshold_uncertainty_score":0.020062447},"labels":[],"label_agreement":null},{"id":"W4390673744","doi":"10.1016/j.patcog.2024.110264","title":"Edge-labeling based modified gated graph network for few-shot learning","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Graph; Benchmark (surveying); Node (physics); Enhanced Data Rates for GSM Evolution; Convolutional neural network; Feature learning; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science","score_opus":0.06803367941487512,"score_gpt":0.28186803010775885,"score_spread":0.21383435069288373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390673744","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017918533,0.00035801725,0.97946334,0.0001592185,0.00005799791,0.000057018904,0.00021034387,0.00092514337,0.000850361],"genre_scores_gemma":[0.61968106,0.0006681079,0.36685747,0.0005612605,0.00012693509,0.00032704053,0.0026974415,0.00034305602,0.008737594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994843,0.00010268313,0.000019739731,0.0002295329,0.00009245656,0.000071189716],"domain_scores_gemma":[0.999134,0.00038109248,0.000059699025,0.00015981834,0.00018617955,0.00007925295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005694697,0.0010104597,0.0017557908,0.0012537237,0.0006311791,0.0007127832,0.0034052217,0.0021098645,0.0029724257],"category_scores_gemma":[0.002230109,0.0005155007,0.00083484245,0.0014545926,0.0008397643,0.0019042332,0.0015953394,0.001755027,0.000847269],"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":[0.00038160392,0.00033650297,0.0015935322,0.00023838025,0.00015363592,0.00021415412,0.000131633,0.5209483,0.018767446,0.015996829,0.00740273,0.4338354],"study_design_scores_gemma":[0.0000043650302,0.000017987102,0.000117504715,0.0000036290842,0.000007786993,0.00001807694,0.0000051721404,0.9942964,0.0009349367,0.0043228515,0.00026565313,0.000005660379],"about_ca_topic_score_codex":0.01124743,"about_ca_topic_score_gemma":0.014578366,"teacher_disagreement_score":0.01124743,"about_ca_system_score_codex":0.00094352034,"about_ca_system_score_gemma":0.0011528927,"threshold_uncertainty_score":0.022363901},"labels":[],"label_agreement":null},{"id":"W4390872520","doi":"10.1109/iccv51070.2023.01063","title":"Continual Zero-Shot Learning through Semantically Guided Generative Random Walks","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Random walk; Zero (linguistics); Computer science; Generative grammar; Shot (pellet); Artificial intelligence; Generative model; Mathematics; Statistics; Linguistics; Materials science","score_opus":0.05919702822837183,"score_gpt":0.31183914355655556,"score_spread":0.25264211532818376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390872520","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":[0.02500382,0.0007223443,0.96958256,0.00034456913,0.000053594365,0.00007668857,0.00021995095,0.002664228,0.0013322702],"genre_scores_gemma":[0.71351194,0.0006036235,0.2732582,0.0010870846,0.00018694624,0.00028336793,0.0030706555,0.0007151785,0.0072829616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99890506,0.00035141205,0.00004710292,0.00040212146,0.00017875327,0.00011549217],"domain_scores_gemma":[0.9974107,0.0016341682,0.00013855784,0.00046425473,0.00019831772,0.00015405031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002096322,0.001444352,0.0019123445,0.0010652518,0.0005893618,0.0012957636,0.0040718894,0.0022058655,0.0026881706],"category_scores_gemma":[0.0057301326,0.00083362625,0.0014927684,0.00095632806,0.001879478,0.0032881955,0.0027971512,0.0036641734,0.0012479525],"study_design_candidate":"bench_or_experimental","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":[0.0003752229,0.00039976437,0.002703475,0.00031805132,0.00018223925,0.00024687123,0.00031882967,0.6100597,0.0071184635,0.032038976,0.010759681,0.33547872],"study_design_scores_gemma":[0.000015506293,0.000039423,0.00010548,0.000013177878,0.000009688526,0.000046170975,0.000013558444,0.9807171,0.0009878989,0.017509691,0.0005319259,0.0000103684715],"about_ca_topic_score_codex":0.004724641,"about_ca_topic_score_gemma":0.008198829,"teacher_disagreement_score":0.004724641,"about_ca_system_score_codex":0.0012576609,"about_ca_system_score_gemma":0.0012069553,"threshold_uncertainty_score":0.011086583},"labels":[],"label_agreement":null},{"id":"W4390874577","doi":"10.1109/iccv51070.2023.01439","title":"PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Agency for Defense Development","keywords":"Computer science; Generalization; Style (visual arts); Artificial intelligence; Classifier (UML); Class (philosophy); Space (punctuation); Natural language processing; Mathematics","score_opus":0.04185642702210183,"score_gpt":0.27057881342237566,"score_spread":0.22872238640027384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390874577","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":[0.025095917,0.00015541248,0.94269645,0.00009974839,0.00013790412,0.00020336214,0.00040844682,0.027382607,0.0038201124],"genre_scores_gemma":[0.33114126,0.00017295784,0.6546254,0.0004427541,0.00007705173,0.0005329357,0.0020320213,0.0024904613,0.008485044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992642,0.0001916927,0.00003570438,0.0003106211,0.00014183481,0.000055836797],"domain_scores_gemma":[0.9987198,0.00046909865,0.00006195067,0.00048913376,0.00017838058,0.00008174417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013152324,0.0016078869,0.0006278175,0.0005047596,0.00025589735,0.0007607209,0.0021815577,0.0013028459,0.007627224],"category_scores_gemma":[0.004533783,0.0004879476,0.0010288885,0.00036849914,0.0006914401,0.0020267477,0.0018426978,0.0016844093,0.0028291028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000778356,0.0005228234,0.0018766645,0.00041137676,0.00008104573,0.00038043509,0.00060403865,0.15100741,0.07484281,0.016573336,0.025177125,0.7277445],"study_design_scores_gemma":[0.000111458074,0.00016705101,0.0003130564,0.00001434486,0.000016570855,0.0001595837,0.000052878117,0.9517573,0.025930434,0.013132345,0.008316377,0.000028550125],"about_ca_topic_score_codex":0.0010516084,"about_ca_topic_score_gemma":0.0015848962,"teacher_disagreement_score":0.007627224,"about_ca_system_score_codex":0.0005012691,"about_ca_system_score_gemma":0.00054708414,"threshold_uncertainty_score":0.025515616},"labels":[],"label_agreement":null},{"id":"W4390906408","doi":"10.3390/jimaging10010023","title":"Fully Self-Supervised Out-of-Domain Few-Shot Learning with Masked Autoencoders","year":2024,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Computer science; Artificial intelligence; Machine learning; Supervised learning; One shot; Domain (mathematical analysis); Shot (pellet); Transformer; Pattern recognition (psychology); Training set; Semi-supervised learning; Deep learning; Artificial neural network; Mathematics","score_opus":0.014136025646209749,"score_gpt":0.2531754631964844,"score_spread":0.23903943755027462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390906408","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":[0.07912863,0.00058771094,0.9155943,0.00014522816,0.000098028075,0.00011302434,0.00017770611,0.002727439,0.0014280301],"genre_scores_gemma":[0.71615463,0.00026089733,0.27670702,0.00039792698,0.000091857095,0.00013786044,0.0014378929,0.0002140313,0.0045978185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995078,0.00011014607,0.000025039717,0.00019794007,0.000098886114,0.000060238108],"domain_scores_gemma":[0.99871886,0.0005383895,0.000114730574,0.0003003418,0.00025436256,0.00007330973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012387448,0.0011179214,0.001254693,0.0006093642,0.00035504525,0.0005810462,0.0016726484,0.0011111022,0.0011171019],"category_scores_gemma":[0.0031381096,0.00053505506,0.0008523997,0.00045334513,0.0007382938,0.0016115553,0.0011272744,0.0016193242,0.00075680745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005582176,0.0007161109,0.0044749705,0.0002759452,0.00033890572,0.00023373603,0.00027705586,0.33266366,0.051694233,0.0035471504,0.006558544,0.5986614],"study_design_scores_gemma":[0.00000868137,0.00006402464,0.00051258714,0.000007900083,0.000014072062,0.000061252525,0.000015594263,0.98960215,0.007663767,0.0016386113,0.00040132503,0.000010056533],"about_ca_topic_score_codex":0.0030367493,"about_ca_topic_score_gemma":0.0065879836,"teacher_disagreement_score":0.0030367493,"about_ca_system_score_codex":0.00047670762,"about_ca_system_score_gemma":0.00087238505,"threshold_uncertainty_score":0.006551206},"labels":[],"label_agreement":null},{"id":"W4391012831","doi":"10.48550/arxiv.2401.08732","title":"Bayes Conditional Distribution Estimation for Knowledge Distillation Based on Conditional Mutual Information","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Estimator; Estimation; Bayes' theorem; Shot (pellet); Computer science; Conditional probability; Image (mathematics); Process (computing); Conditional probability distribution; Set (abstract data type); Artificial intelligence; Statistics; Machine learning; Pattern recognition (psychology); Mathematics; Bayesian probability; Engineering","score_opus":0.053859606455400805,"score_gpt":0.2118131156306399,"score_spread":0.1579535091752391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391012831","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":[0.007944388,0.0005424902,0.9877364,0.0003069323,0.000039989944,0.000052327414,0.0002093971,0.0010704289,0.0020977897],"genre_scores_gemma":[0.50152,0.00090123777,0.48767418,0.0005674952,0.00018591744,0.00035136344,0.001743036,0.00048117747,0.006575624],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976005,0.0006842242,0.0001156729,0.00067787635,0.00071783946,0.00020387954],"domain_scores_gemma":[0.99558383,0.002757516,0.00036110354,0.0004632727,0.0006824771,0.00015183387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029800849,0.0011540888,0.0016635061,0.0014949497,0.00080783415,0.0017829236,0.002582992,0.0013845362,0.004719637],"category_scores_gemma":[0.013866895,0.000688176,0.000923423,0.0011366036,0.0015977619,0.0037258198,0.002626219,0.003049051,0.0014101282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041155404,0.00017558597,0.0026072024,0.0004988969,0.00017641313,0.00014439948,0.0004066086,0.37686765,0.006271537,0.07995699,0.009292277,0.5231909],"study_design_scores_gemma":[0.000012379843,0.000029639155,0.00043149592,0.000037711423,0.0000148911595,0.000050486517,0.00002349158,0.96267927,0.003238691,0.031730767,0.0017246582,0.0000266062],"about_ca_topic_score_codex":0.007924547,"about_ca_topic_score_gemma":0.0075534442,"teacher_disagreement_score":0.007924547,"about_ca_system_score_codex":0.0015921884,"about_ca_system_score_gemma":0.002659898,"threshold_uncertainty_score":0.015788794},"labels":[],"label_agreement":null},{"id":"W4391555765","doi":"10.48550/arxiv.2402.01098","title":"Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Gradient descent; Artificial intelligence; Estimation; Descent (aeronautics); Bayesian probability; Computer science; Deep learning; Machine learning; Mathematics; Artificial neural network; Economics; Geography; Meteorology","score_opus":0.06066480546784925,"score_gpt":0.2014475025477208,"score_spread":0.14078269707987157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391555765","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":[0.012138186,0.00029587004,0.9858889,0.00023034315,0.000022315267,0.000021490592,0.00008997943,0.00050679676,0.00080613553],"genre_scores_gemma":[0.70110667,0.0005813794,0.29075557,0.0003923166,0.00007985452,0.00020591305,0.0008972807,0.00039584274,0.0055852034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995907,0.00012990131,0.000020229028,0.000094430885,0.00011275897,0.000051977597],"domain_scores_gemma":[0.99874973,0.000756121,0.0001087754,0.0001131316,0.00020832449,0.00006381423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017000497,0.00095911336,0.0011930588,0.00068339845,0.00039547696,0.0008681576,0.002030102,0.001404914,0.0021749206],"category_scores_gemma":[0.005669309,0.0007652963,0.0007047602,0.00063389284,0.0010764489,0.0016969411,0.0013287119,0.0019357383,0.0005890252],"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":[0.000058891357,0.000040664476,0.0008212175,0.000059189486,0.00003846127,0.000039601295,0.000048379734,0.9345489,0.0013803722,0.016379943,0.001882534,0.04470177],"study_design_scores_gemma":[0.0000016971886,0.000003276731,0.00003565772,0.0000026425266,0.000001044084,0.0000031011607,0.0000011988773,0.99632734,0.00014619496,0.003378735,0.00009738646,0.0000017350288],"about_ca_topic_score_codex":0.012542522,"about_ca_topic_score_gemma":0.014775047,"teacher_disagreement_score":0.012542522,"about_ca_system_score_codex":0.0015157703,"about_ca_system_score_gemma":0.0017506342,"threshold_uncertainty_score":0.024939},"labels":[],"label_agreement":null},{"id":"W4391591778","doi":"10.48550/arxiv.2402.01887","title":"On $f$-Divergence Principled Domain Adaptation: An Improved Framework","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Divergence (linguistics); Adaptation (eye); Domain adaptation; Domain (mathematical analysis); Computer science; Mathematics; Artificial intelligence; Psychology; Philosophy; Neuroscience; Mathematical analysis; Linguistics","score_opus":0.07917987523748386,"score_gpt":0.2161960191340352,"score_spread":0.1370161438965513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391591778","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":[0.0052465056,0.00058386667,0.9921164,0.0002817476,0.000059597947,0.000039233288,0.000051230156,0.00037820727,0.0012431488],"genre_scores_gemma":[0.3439504,0.0012158522,0.6467072,0.0009405302,0.00053439604,0.0004337616,0.0007323624,0.00065259996,0.004832942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996714,0.0014228354,0.00015594832,0.00074792973,0.00077625224,0.00018295363],"domain_scores_gemma":[0.9901336,0.0060420074,0.00039477574,0.0017524308,0.0013198663,0.00035723226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062749367,0.0015326235,0.0023222463,0.001680572,0.0010134281,0.0020399613,0.0034337768,0.0024830976,0.0025213007],"category_scores_gemma":[0.02265907,0.00062575564,0.0011859727,0.001968239,0.0029103248,0.004841493,0.006040401,0.005550733,0.0014392253],"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":[0.0002737294,0.00028677788,0.0026694504,0.00034590202,0.00013372516,0.00017908677,0.00028614217,0.51419836,0.0071836556,0.15702987,0.0080431225,0.30937016],"study_design_scores_gemma":[0.000013971879,0.000057903268,0.00020172696,0.000016565644,0.000010303435,0.0000668163,0.000017220453,0.9523131,0.001141357,0.044361833,0.0017807038,0.000018467097],"about_ca_topic_score_codex":0.002244813,"about_ca_topic_score_gemma":0.0014198547,"teacher_disagreement_score":0.0062749367,"about_ca_system_score_codex":0.0013570136,"about_ca_system_score_gemma":0.0022417894,"threshold_uncertainty_score":0.033185422},"labels":[],"label_agreement":null},{"id":"W4391609962","doi":"10.3389/frai.2024.1301997","title":"A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm","year":2024,"lang":"en","type":"article","venue":"Frontiers in Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Hotchkiss Brain Institute; Alberta Children's Hospital; Women and Children’s Health Research Institute; Université de Montréal; University of Alberta; University of Calgary","funders":"Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Consortium canadien en neurodégénérescence associée au vieillissement; Alzheimer's Disease Neuroimaging Initiative","keywords":"Computer science; Artificial intelligence; Machine learning; Convolutional neural network; Deep learning; Contextual image classification; Artificial neural network; Simplicity; Image (mathematics)","score_opus":0.12086459632646034,"score_gpt":0.3232367173409536,"score_spread":0.20237212101449326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391609962","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":[0.06601425,0.0004433524,0.9306441,0.0005335515,0.00006320841,0.00006620838,0.00012625905,0.0007363892,0.001372764],"genre_scores_gemma":[0.8882176,0.00020127466,0.10733819,0.00034355983,0.00006688618,0.00011388809,0.00046776162,0.00006786652,0.0031830063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995925,0.000106510306,0.000021355212,0.00015815462,0.00005439978,0.00006705194],"domain_scores_gemma":[0.99939823,0.00018904568,0.00006032676,0.00012390957,0.00016604413,0.00006245168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013296303,0.0006479995,0.0008404336,0.00052616023,0.00041569525,0.0007927223,0.0017254822,0.0011626114,0.0013961457],"category_scores_gemma":[0.0017181868,0.00029940245,0.0009234445,0.0006804888,0.0005493428,0.0015346849,0.0012735031,0.0013477517,0.0004355731],"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":[0.00039870135,0.00028022865,0.0060248207,0.00009209682,0.0001837646,0.00021272633,0.00017014681,0.7393658,0.006544457,0.011767323,0.005027737,0.22993223],"study_design_scores_gemma":[0.000006877482,0.000041106563,0.00016836244,0.000002545785,0.00000887363,0.000024479226,0.000014770728,0.99532336,0.00072773965,0.0033732173,0.00030432845,0.0000044051117],"about_ca_topic_score_codex":0.0055991057,"about_ca_topic_score_gemma":0.005813973,"teacher_disagreement_score":0.0055991057,"about_ca_system_score_codex":0.000925079,"about_ca_system_score_gemma":0.0011701147,"threshold_uncertainty_score":0.011133015},"labels":[],"label_agreement":null},{"id":"W4391766513","doi":"10.48550/arxiv.2402.06171","title":"Pushing Boundaries: Mixup's Influence on Neural Collapse","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science","score_opus":0.057892005814535075,"score_gpt":0.19863230638950524,"score_spread":0.14074030057497017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391766513","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":[0.46888196,0.0010605676,0.5204398,0.0012282688,0.00010594636,0.00012247988,0.00021077748,0.0018080573,0.0061421786],"genre_scores_gemma":[0.9728099,0.00015708238,0.024954295,0.00030012437,0.000028515851,0.00009099583,0.00020179186,0.00022783905,0.0012293311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827814,0.00053824997,0.000102046724,0.00039690596,0.0003936873,0.0002909914],"domain_scores_gemma":[0.99362826,0.00329959,0.00063091575,0.001349553,0.00066559156,0.00042619396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004544444,0.0012791461,0.0011450693,0.0010679994,0.00089886686,0.0021970877,0.0017080691,0.0017560875,0.0037074042],"category_scores_gemma":[0.031053284,0.00083789794,0.0009262268,0.00061751105,0.003332674,0.004168526,0.006529425,0.0029702908,0.00055397255],"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":[0.00084600825,0.000227022,0.016076291,0.0003243078,0.00020791565,0.00088369753,0.0008233213,0.72547877,0.026376216,0.07837217,0.0043669674,0.14601742],"study_design_scores_gemma":[0.000025869973,0.0002482382,0.0024551384,0.00006500871,0.000025455058,0.00024480463,0.00016505523,0.9537282,0.011147664,0.030539388,0.0013218902,0.000033195476],"about_ca_topic_score_codex":0.0016173897,"about_ca_topic_score_gemma":0.0015885094,"teacher_disagreement_score":0.004544444,"about_ca_system_score_codex":0.0010777223,"about_ca_system_score_gemma":0.0009209899,"threshold_uncertainty_score":0.024033546},"labels":[],"label_agreement":null},{"id":"W4391922244","doi":"10.1038/s41598-024-54640-6","title":"Transductive meta-learning with enhanced feature ensemble for few-shot semantic segmentation","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Segmentation; Shot (pellet); Feature (linguistics); Ensemble learning; Pattern recognition (psychology); Semantic feature; Machine learning","score_opus":0.031733843478467576,"score_gpt":0.2811229541598212,"score_spread":0.2493891106813536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391922244","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02421038,0.00046894647,0.96909183,0.00015158903,0.000062365754,0.00006686671,0.0001375661,0.0045635533,0.0012469001],"genre_scores_gemma":[0.62954795,0.00029968997,0.36259046,0.0005933559,0.000126745,0.00023219183,0.0015314741,0.0005644245,0.004513766],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907887,0.00016232367,0.00003716705,0.00041932092,0.00018019846,0.00012216314],"domain_scores_gemma":[0.9990496,0.00036264854,0.00008503163,0.0002181546,0.0002139947,0.00007052598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013483128,0.0021207298,0.0020277847,0.0014305217,0.0006216075,0.0011709287,0.0035568972,0.0021030707,0.0021278237],"category_scores_gemma":[0.0026996674,0.0008136344,0.0016729421,0.0011272647,0.0010655593,0.0033994124,0.0019130463,0.0027983338,0.0011415787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032643945,0.00040117832,0.0019239633,0.00015428264,0.00028216327,0.0002549473,0.0002568533,0.43486896,0.021791812,0.0068784263,0.005037187,0.5278238],"study_design_scores_gemma":[0.0000037390328,0.000051368355,0.0001335745,0.0000056928952,0.000015753298,0.00003250088,0.00001338579,0.9912282,0.0034665393,0.004682578,0.0003568479,0.00000974135],"about_ca_topic_score_codex":0.004233326,"about_ca_topic_score_gemma":0.0059238966,"teacher_disagreement_score":0.004233326,"about_ca_system_score_codex":0.0013141475,"about_ca_system_score_gemma":0.0009271487,"threshold_uncertainty_score":0.009534836},"labels":[],"label_agreement":null},{"id":"W4392121755","doi":"10.48550/arxiv.2402.14789","title":"Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office of Naval Research; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Domain (mathematical analysis); Artificial intelligence; Machine learning; Mathematics","score_opus":0.06082061123542746,"score_gpt":0.20627329499428976,"score_spread":0.1454526837588623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392121755","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021946592,0.00022771233,0.9746362,0.000174294,0.000037699614,0.000037448746,0.000092361726,0.001902577,0.0009450605],"genre_scores_gemma":[0.59756446,0.00022845526,0.39632118,0.00050868077,0.000100593534,0.00020786592,0.00087138935,0.00039122012,0.0038062027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992361,0.0002624416,0.00003504438,0.00024657472,0.00016135765,0.000058414345],"domain_scores_gemma":[0.9979583,0.0009695182,0.00016087278,0.0005501442,0.00027936534,0.0000818562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016103213,0.0010355316,0.0007498161,0.000504776,0.00035160716,0.0006418488,0.0015614437,0.0013612852,0.0015749572],"category_scores_gemma":[0.0046390067,0.0005155209,0.00076785375,0.00040904657,0.0010524427,0.0016858734,0.0016073916,0.0019856053,0.0008146181],"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":[0.0002942659,0.0003119744,0.0023601798,0.00025078896,0.00023762976,0.00013110304,0.0002782299,0.6332968,0.033044823,0.018016597,0.007766117,0.3040115],"study_design_scores_gemma":[0.000005081433,0.000024584311,0.00012540839,0.0000049234964,0.0000051754128,0.000016582197,0.0000061548467,0.9903701,0.0035263023,0.005503225,0.00040755438,0.000004884357],"about_ca_topic_score_codex":0.0016592566,"about_ca_topic_score_gemma":0.003170266,"teacher_disagreement_score":0.0016592566,"about_ca_system_score_codex":0.0005943615,"about_ca_system_score_gemma":0.0009618313,"threshold_uncertainty_score":0.008516312},"labels":[],"label_agreement":null},{"id":"W4392293106","doi":"10.1007/s00521-024-09471-x","title":"A lightweight siamese transformer for few-shot semantic segmentation","year":2024,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Liaoning Province","keywords":"Computer science; Transformer; Segmentation; Pascal (unit); Artificial intelligence; Information retrieval; Voltage; Programming language","score_opus":0.02666150186036346,"score_gpt":0.3091090357777974,"score_spread":0.28244753391743394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392293106","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034312808,0.00026326778,0.99025106,0.000079815436,0.00008450128,0.000071461945,0.00025539869,0.004441922,0.0011212268],"genre_scores_gemma":[0.17301449,0.0006050395,0.8114671,0.0004496464,0.00014910878,0.00020582878,0.0034970473,0.001274666,0.0093370285],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921095,0.00011349006,0.000050427854,0.00028597322,0.00023808838,0.00010108841],"domain_scores_gemma":[0.99917513,0.000218212,0.00003540773,0.00025985524,0.00022624336,0.00008522028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010195144,0.0011652485,0.0019254027,0.001523306,0.0006080733,0.0015691306,0.0027649598,0.0016495207,0.012897536],"category_scores_gemma":[0.0028071871,0.00058860483,0.0012234488,0.0018100301,0.0006273402,0.003058167,0.0029893762,0.0025599403,0.007443724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004727588,0.00029610455,0.00041800027,0.00019213064,0.00012892671,0.00013054899,0.000065450186,0.027652754,0.03540379,0.015416945,0.0146712605,0.9051512],"study_design_scores_gemma":[0.000031802323,0.000083614985,0.0002797512,0.000016700636,0.00004023896,0.0002141252,0.000042020965,0.94948393,0.01785583,0.02681501,0.005111243,0.000025692294],"about_ca_topic_score_codex":0.006508088,"about_ca_topic_score_gemma":0.013410049,"teacher_disagreement_score":0.012897536,"about_ca_system_score_codex":0.0006994978,"about_ca_system_score_gemma":0.0019694404,"threshold_uncertainty_score":0.04314649},"labels":[],"label_agreement":null},{"id":"W4392797338","doi":"10.2139/ssrn.4758921","title":"Full-Stage Augmentation for Exemplar-Free Class-Incremental Learning","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 Calgary","funders":"","keywords":"Class (philosophy); Stage (stratigraphy); Computer science; Artificial intelligence; Geology","score_opus":0.018892322460964915,"score_gpt":0.28385176180379207,"score_spread":0.2649594393428272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392797338","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":[0.015992368,0.00096421153,0.97561896,0.0002419209,0.00020577801,0.00011209915,0.00040950568,0.0045360173,0.0019191479],"genre_scores_gemma":[0.524434,0.0007090486,0.4563114,0.0007713558,0.0003212135,0.00045755482,0.0036900328,0.00083501096,0.0124703245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910295,0.00018867775,0.000047109013,0.00035260216,0.00019771542,0.000110931986],"domain_scores_gemma":[0.9971475,0.00128333,0.00008454612,0.0009926145,0.00035102363,0.00014107728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017826164,0.0013034857,0.0024316967,0.0008369249,0.0006757527,0.0012046404,0.0050233463,0.003050789,0.0070302924],"category_scores_gemma":[0.006321974,0.0008939821,0.0013796162,0.0011353185,0.0010465262,0.0032781418,0.0039499123,0.0035825965,0.0034127983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056461984,0.00040841973,0.0010149844,0.00036091136,0.0001900406,0.00019217588,0.00013970803,0.12017654,0.018967113,0.010912313,0.018264692,0.8288085],"study_design_scores_gemma":[0.000024594456,0.000074727155,0.0002634847,0.000019998603,0.000032679483,0.000095907824,0.000015965212,0.97858423,0.0047583887,0.014146972,0.0019635845,0.000019506424],"about_ca_topic_score_codex":0.0032095204,"about_ca_topic_score_gemma":0.00608255,"teacher_disagreement_score":0.0070302924,"about_ca_system_score_codex":0.0005465457,"about_ca_system_score_gemma":0.0012740138,"threshold_uncertainty_score":0.023518622},"labels":[],"label_agreement":null},{"id":"W4392903228","doi":"10.1109/icassp48485.2024.10446522","title":"Revisiting the Equivalence of In-Context Learning and Gradient Descent: The Impact of Data Distribution","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gradient descent; Computer science; Equivalence (formal languages); Softmax function; Covariance; Algorithm; Artificial intelligence; Mathematics; Deep learning; Statistics; Discrete mathematics; Artificial neural network","score_opus":0.053974765455947815,"score_gpt":0.33526814959083584,"score_spread":0.281293384134888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392903228","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":[0.043775972,0.00056496594,0.9459755,0.0015548082,0.00009417662,0.00008998213,0.00008935326,0.0008759866,0.0069793034],"genre_scores_gemma":[0.87897015,0.00043698703,0.11561906,0.000802235,0.00015325371,0.00013228846,0.0001422576,0.00044569932,0.0032979865],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772364,0.0010207193,0.00008381946,0.00067485333,0.00030620056,0.00019081525],"domain_scores_gemma":[0.9858154,0.009583226,0.00052667066,0.0029841056,0.00061281654,0.00047766906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047676708,0.0008168147,0.0012722622,0.0005146683,0.00072370935,0.0023722877,0.002573053,0.001570941,0.004562056],"category_scores_gemma":[0.037481602,0.000704505,0.00061762694,0.00065550016,0.0033154734,0.007201001,0.0053168405,0.0050408514,0.00087598927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082371203,0.00046299878,0.007358832,0.00039277482,0.00015394697,0.00031235194,0.0014110481,0.2008588,0.014915106,0.42118007,0.005385536,0.3467448],"study_design_scores_gemma":[0.000049256236,0.00018550133,0.0012217923,0.000043017364,0.000023584565,0.00014120596,0.000084987434,0.7668895,0.004087544,0.22502503,0.0022222542,0.000026263482],"about_ca_topic_score_codex":0.003938902,"about_ca_topic_score_gemma":0.0031096025,"teacher_disagreement_score":0.0047676708,"about_ca_system_score_codex":0.0012904487,"about_ca_system_score_gemma":0.0014706671,"threshold_uncertainty_score":0.025214136},"labels":[],"label_agreement":null},{"id":"W4392903502","doi":"10.1109/icassp48485.2024.10448032","title":"Unsupervised Continual Learning of Image Representation Via Rememory-Based Simsiam","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Henan Institute of Science and Technology; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Forgetting; Dependency (UML); Representation (politics); Focus (optics); Process (computing); Artificial intelligence; Image (mathematics); Feature learning; Machine learning","score_opus":0.017200362843556867,"score_gpt":0.2806896362105125,"score_spread":0.26348927336695566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392903502","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":[0.06953897,0.00077498355,0.9238999,0.00032035462,0.00009029746,0.000072945535,0.00015866493,0.0034483515,0.0016954711],"genre_scores_gemma":[0.7277582,0.0003766335,0.265169,0.0005374651,0.00013139745,0.00014957895,0.00088466174,0.00029203083,0.0047009736],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995714,0.000091417525,0.000027770802,0.00014713482,0.00009854557,0.00006370626],"domain_scores_gemma":[0.9991228,0.00027885556,0.000101573365,0.00021960886,0.00020729327,0.00006987903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011642321,0.00079954625,0.0010585045,0.00092056044,0.00038827036,0.0007534045,0.0026019288,0.0011012659,0.0019548964],"category_scores_gemma":[0.0031275903,0.0004598811,0.0009588334,0.0007784264,0.00084831193,0.0018314439,0.0019842347,0.0015544965,0.00086348323],"study_design_candidate":"bench_or_experimental","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":[0.00045816525,0.00025042746,0.0024315112,0.00020669815,0.00017786627,0.0001951038,0.00021264836,0.30942732,0.02798837,0.0077115204,0.00550661,0.6454337],"study_design_scores_gemma":[0.000009651171,0.00007625315,0.000217581,0.000005437649,0.000011949346,0.000044954617,0.000012537565,0.99291104,0.003471042,0.0027005395,0.0005306948,0.000008339754],"about_ca_topic_score_codex":0.0021284686,"about_ca_topic_score_gemma":0.0034419145,"teacher_disagreement_score":0.0026019288,"about_ca_system_score_codex":0.0005612292,"about_ca_system_score_gemma":0.0008824911,"threshold_uncertainty_score":0.006539762},"labels":[],"label_agreement":null},{"id":"W4392904080","doi":"10.1109/icassp48485.2024.10447379","title":"Engineering the Neural Collapse Geometry of Supervised-Contrastive Loss","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Embedding; Limiting; Computer science; Artificial intelligence; Entropy (arrow of time); Classifier (UML); Benchmark (surveying); Feature (linguistics); Artificial neural network; Geometry; Feature vector; Cross entropy; Machine learning; Pattern recognition (psychology); Algorithm; Mathematics; Engineering; Mechanical engineering","score_opus":0.010375203711460607,"score_gpt":0.22234689663753046,"score_spread":0.21197169292606985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392904080","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":[0.057642024,0.00027942687,0.9366184,0.0007580558,0.000056505698,0.00008658619,0.00012449636,0.00060759287,0.003826962],"genre_scores_gemma":[0.8454738,0.0002903856,0.14800286,0.00056907814,0.00011656092,0.00027818442,0.00037805052,0.00032502745,0.0045660674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984718,0.00062307157,0.00006479988,0.00032966488,0.00039700608,0.00011374469],"domain_scores_gemma":[0.9962096,0.0019266284,0.00038703636,0.000675478,0.00054532196,0.00025586665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004277953,0.0011858688,0.0010167423,0.00074901665,0.0004585078,0.0014871222,0.0016630968,0.001628914,0.0021299012],"category_scores_gemma":[0.015911259,0.0005118408,0.0005559593,0.00041698152,0.0024633855,0.0035634504,0.0035972705,0.0031126917,0.0006831153],"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":[0.00045654527,0.00027282682,0.0034868885,0.00017196275,0.000082354476,0.00033031064,0.00017683797,0.7309101,0.01559909,0.12789546,0.00689322,0.113724425],"study_design_scores_gemma":[0.000015200146,0.00012472188,0.0002955303,0.000012699956,0.0000053221233,0.000083140294,0.000012947451,0.9652893,0.0026258153,0.03090711,0.00061670016,0.0000115404155],"about_ca_topic_score_codex":0.0010213727,"about_ca_topic_score_gemma":0.0011703756,"teacher_disagreement_score":0.004277953,"about_ca_system_score_codex":0.0017235695,"about_ca_system_score_gemma":0.0011529163,"threshold_uncertainty_score":0.022624254},"labels":[],"label_agreement":null},{"id":"W4392931151","doi":"10.1016/j.knosys.2024.111653","title":"Approximate and Memorize (A&amp;M) : Settling opposing views in replay-based continuous unsupervised domain adaptation","year":2024,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"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; University of New Brunswick","funders":"","keywords":"Memorization; Stability (learning theory); Computer science; Adaptation (eye); Forgetting; Scalability; Artificial intelligence; Domain (mathematical analysis); Machine learning; Theoretical computer science; Mathematics","score_opus":0.05531971084796615,"score_gpt":0.28906562793377916,"score_spread":0.233745917085813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392931151","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":[0.039430723,0.00046043406,0.95690703,0.00053901965,0.00009444715,0.00004737475,0.000046969333,0.0011559431,0.0013180333],"genre_scores_gemma":[0.71561193,0.00026236573,0.27995965,0.0005242665,0.00012115926,0.00009866885,0.00015721982,0.00025977415,0.0030049628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922454,0.0003059525,0.00003457504,0.00023961415,0.00012228913,0.00007300024],"domain_scores_gemma":[0.9956066,0.0031907463,0.00020167847,0.00058204425,0.00025919673,0.00015976554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00245683,0.00075245963,0.0012057314,0.0004317027,0.00071204145,0.0013203111,0.0019109888,0.0021271352,0.0021363075],"category_scores_gemma":[0.01117645,0.00058120216,0.0005388953,0.00041845648,0.0016370362,0.0033049,0.0031695429,0.0029351052,0.00052407355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016657648,0.00027309958,0.0018009238,0.00026122123,0.00033342774,0.00032943606,0.0014950308,0.28114304,0.027259843,0.033688594,0.0065878425,0.6451618],"study_design_scores_gemma":[0.000024231133,0.00007113191,0.00025843483,0.000010633455,0.000021948785,0.00004228835,0.00006840434,0.97943044,0.0042565432,0.015149979,0.0006454017,0.000020587418],"about_ca_topic_score_codex":0.0043382584,"about_ca_topic_score_gemma":0.0041728807,"teacher_disagreement_score":0.0043382584,"about_ca_system_score_codex":0.000489257,"about_ca_system_score_gemma":0.0009139024,"threshold_uncertainty_score":0.012993097},"labels":[],"label_agreement":null},{"id":"W4392942841","doi":"10.1109/icmla58977.2023.00041","title":"Improving Adversarial Robustness of Few-Shot Learning with Contrastive Learning and Hypersphere Embedding","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hypersphere; Adversarial system; Robustness (evolution); Embedding; Computer science; Artificial intelligence","score_opus":0.014303067578156976,"score_gpt":0.24996922197675098,"score_spread":0.235666154398594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392942841","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":[0.13270083,0.0008114434,0.8614897,0.00049832475,0.00010594663,0.00010649141,0.000106136344,0.0013848818,0.0027961913],"genre_scores_gemma":[0.9220517,0.00021514492,0.07455824,0.00028958093,0.000054759625,0.000062797495,0.00029455323,0.00010676703,0.0023664453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991505,0.00025025933,0.000036582875,0.00024076962,0.00022738983,0.000094527124],"domain_scores_gemma":[0.99740356,0.0013504496,0.0002584981,0.00053984823,0.0003107363,0.00013699773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022788914,0.0012920802,0.0011574124,0.000740052,0.00047458822,0.0008872413,0.0015558855,0.0014536345,0.0010781792],"category_scores_gemma":[0.0075690206,0.00036326767,0.0007110372,0.0003635251,0.0016796843,0.0024211071,0.0023650674,0.0023631633,0.0003315924],"study_design_candidate":"bench_or_experimental","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":[0.00022944176,0.00023320525,0.0020315815,0.00010266762,0.00011645331,0.00018908136,0.00013325858,0.8552741,0.016118156,0.01032202,0.0020682723,0.113181785],"study_design_scores_gemma":[0.0000037284935,0.000060317612,0.00016688519,0.000004725166,0.0000047694793,0.00003673131,0.000008250249,0.9937139,0.002956144,0.0028626365,0.00017540233,0.0000064010983],"about_ca_topic_score_codex":0.0024901514,"about_ca_topic_score_gemma":0.002294321,"teacher_disagreement_score":0.0024901514,"about_ca_system_score_codex":0.0009711795,"about_ca_system_score_gemma":0.000736649,"threshold_uncertainty_score":0.012052059},"labels":[],"label_agreement":null},{"id":"W4393029186","doi":"10.1016/j.compmedimag.2024.102373","title":"A novel center-based deep contrastive metric learning method for the detection of polymicrogyria in pediatric brain MRI","year":2024,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polymicrogyria; Computer science; Deep learning; Magnetic resonance imaging; Artificial intelligence; Metric (unit); Medicine; Radiology","score_opus":0.012324269159233096,"score_gpt":0.2900185064092529,"score_spread":0.27769423725001985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393029186","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043478496,0.0011221435,0.95183384,0.00028521495,0.000080398015,0.00006125208,0.0003065006,0.001859718,0.0009724094],"genre_scores_gemma":[0.38607574,0.0009116601,0.60579014,0.0005109694,0.00013868813,0.00012855126,0.0013940401,0.00038257687,0.0046676286],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979,0.00004114249,0.000009746194,0.00006019814,0.00006374049,0.000035069523],"domain_scores_gemma":[0.9995964,0.00009823689,0.000039621515,0.000040391726,0.00017223788,0.0000531304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049531,0.00082277355,0.0008595908,0.0009973813,0.0002855446,0.00046561455,0.001429043,0.001050515,0.0010646593],"category_scores_gemma":[0.0010934435,0.000290092,0.0005828753,0.0005910655,0.00026735305,0.00075398054,0.0011058805,0.0009939134,0.0006247861],"study_design_candidate":"bench_or_experimental","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":[0.00026460268,0.00019623418,0.0038322147,0.00015848968,0.00014076267,0.0003030544,0.0000777682,0.06806887,0.06537993,0.005060234,0.014038517,0.8424794],"study_design_scores_gemma":[0.000011264199,0.00006595855,0.000950674,0.000009092445,0.000022936578,0.00030301316,0.0000122269985,0.98094064,0.014326467,0.0017367374,0.0016035809,0.000017447399],"about_ca_topic_score_codex":0.00524322,"about_ca_topic_score_gemma":0.008989259,"teacher_disagreement_score":0.00524322,"about_ca_system_score_codex":0.0004256928,"about_ca_system_score_gemma":0.0011411296,"threshold_uncertainty_score":0.010425389},"labels":[],"label_agreement":null},{"id":"W4393112045","doi":"10.1186/s40537-024-00898-6","title":"Multi-sample $$\\zeta $$-mixup: richer, more realistic synthetic samples from a p-series interpolant","year":2024,"lang":"en","type":"article","venue":"Journal Of Big Data","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Compute Canada; Simon Fraser University; Nvidia","keywords":"Algorithm; Computer science; Machine learning; Artificial intelligence; Series (stratigraphy)","score_opus":0.18171046952873535,"score_gpt":0.33451089087990676,"score_spread":0.15280042135117142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393112045","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15421394,0.00026475126,0.8348506,0.0009682633,0.00019425686,0.00010368956,0.00072454935,0.0026600328,0.006019993],"genre_scores_gemma":[0.7311571,0.00009029359,0.26201275,0.00042869762,0.000053286072,0.00015771257,0.0015963102,0.0006129006,0.0038909377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904305,0.0003276104,0.000047731875,0.00020639834,0.00028707462,0.000088110246],"domain_scores_gemma":[0.9962978,0.0020745168,0.00022710265,0.0006389547,0.00047843176,0.0002832485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029291597,0.0012648538,0.0008412404,0.0006604522,0.00055677834,0.0013365046,0.0014319337,0.0016490285,0.005790364],"category_scores_gemma":[0.009177419,0.00044933773,0.0009997445,0.00054139586,0.0013845995,0.0017385829,0.0024213663,0.0027556522,0.0008733049],"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":[0.0010011207,0.00030029265,0.00375371,0.00022675333,0.000092176815,0.00041299165,0.00017472163,0.85881394,0.011218164,0.048018828,0.008416055,0.067571245],"study_design_scores_gemma":[0.000017213531,0.000036747668,0.00010874733,0.000009150447,0.0000027107778,0.00002546454,0.000011342879,0.9904185,0.0033219117,0.0053460817,0.00069456524,0.000007672692],"about_ca_topic_score_codex":0.0021291818,"about_ca_topic_score_gemma":0.002129819,"teacher_disagreement_score":0.005790364,"about_ca_system_score_codex":0.00080458797,"about_ca_system_score_gemma":0.00072849845,"threshold_uncertainty_score":0.019370675},"labels":[],"label_agreement":null},{"id":"W4393147203","doi":"10.1609/aaai.v38i15.29573","title":"Leveraging Normalization Layer in Adapters with Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Normalization (sociology); Computer science; Distillation; Shot (pellet); Layer (electronics); Artificial intelligence; Materials science; Chemistry; Chromatography; Nanotechnology","score_opus":0.07970498633852996,"score_gpt":0.32645086472122903,"score_spread":0.24674587838269907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393147203","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":[0.03304473,0.0006016687,0.95667547,0.00020548163,0.00008946565,0.00017435059,0.00014616702,0.0074922633,0.0015703634],"genre_scores_gemma":[0.5290129,0.00032931482,0.4620069,0.00094373355,0.00010653424,0.0004667669,0.0015368775,0.00071635627,0.0048806104],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979907,0.00047320852,0.000114036935,0.0008132651,0.00041830537,0.00019047047],"domain_scores_gemma":[0.99704677,0.0009796259,0.00019760641,0.0011532344,0.00042095853,0.00020174247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037594032,0.0018724022,0.0020155704,0.0011615012,0.0008093833,0.001512134,0.0047776387,0.002127219,0.0027585786],"category_scores_gemma":[0.010785534,0.00083829963,0.0011808639,0.0010864654,0.0019043946,0.0051656477,0.005836078,0.0043604835,0.0015973914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056128035,0.000675594,0.0051813205,0.00031381866,0.00029787363,0.0003013918,0.00068461726,0.1946832,0.032551773,0.013156439,0.0068082693,0.7447844],"study_design_scores_gemma":[0.000027098467,0.00013758536,0.0006094199,0.000021634636,0.000036298363,0.00010543946,0.000075235126,0.972431,0.0124500645,0.011956131,0.0021150154,0.00003500814],"about_ca_topic_score_codex":0.004811682,"about_ca_topic_score_gemma":0.008171631,"teacher_disagreement_score":0.004811682,"about_ca_system_score_codex":0.0012073277,"about_ca_system_score_gemma":0.0016481354,"threshold_uncertainty_score":0.019881845},"labels":[],"label_agreement":null},{"id":"W4393147607","doi":"10.1609/aaai.v38i14.29497","title":"Lost Domain Generalization Is a Natural Consequence of Lack of Training Domains","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Generalization; Natural (archaeology); Training (meteorology); Domain (mathematical analysis); Computer science; Psychology; Artificial intelligence; Mathematics; History; Geography","score_opus":0.14685991232785084,"score_gpt":0.3403925985309042,"score_spread":0.19353268620305336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393147607","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":[0.1566231,0.0012866154,0.8304766,0.003107807,0.0001460218,0.00022343989,0.000612159,0.0015957317,0.005928625],"genre_scores_gemma":[0.7453358,0.00060857856,0.24661413,0.0018669945,0.00018280774,0.00052362186,0.0013601704,0.00043953193,0.0030684115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9919851,0.0023624212,0.00067969697,0.002627431,0.001819772,0.0005254721],"domain_scores_gemma":[0.9350708,0.044069998,0.0026084164,0.014821212,0.0022573306,0.0011722632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010209936,0.001267979,0.002731046,0.0009517904,0.0013451619,0.0022303762,0.0034136355,0.0026616517,0.0023276939],"category_scores_gemma":[0.05860348,0.00090277434,0.0018735228,0.0010427078,0.0040172185,0.0057180994,0.00541513,0.005793569,0.0008539968],"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":[0.0013114595,0.00067360554,0.024746092,0.00086431485,0.00037306364,0.0008668573,0.0006707516,0.58696675,0.018791866,0.06968399,0.015430275,0.27962098],"study_design_scores_gemma":[0.000105848114,0.00052024506,0.004508443,0.00007975387,0.00006641657,0.0009794362,0.00017270097,0.81384146,0.013041116,0.16122319,0.0054125544,0.000048876158],"about_ca_topic_score_codex":0.0016798394,"about_ca_topic_score_gemma":0.0013705258,"teacher_disagreement_score":0.010209936,"about_ca_system_score_codex":0.0016549794,"about_ca_system_score_gemma":0.0015096623,"threshold_uncertainty_score":0.053995907},"labels":[],"label_agreement":null},{"id":"W4393281987","doi":"10.1016/j.media.2024.103150","title":"Boundary-aware information maximization for self-supervised medical image segmentation","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Hôpital Notre-Dame","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Artificial intelligence; Computer science; Cluster analysis; Feature learning; Segmentation; Pattern recognition (psychology); Mutual information; Maximization; Image segmentation; Feature (linguistics); Machine learning; Mathematics","score_opus":0.007274381179233694,"score_gpt":0.2781048143756569,"score_spread":0.2708304331964232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393281987","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":[0.008207381,0.00033571487,0.9902113,0.00012408133,0.000015316395,0.000036270587,0.000055029777,0.00068087864,0.00033393787],"genre_scores_gemma":[0.3915145,0.0005836129,0.6019086,0.00048740063,0.00015258137,0.0002904438,0.0008450007,0.0007318516,0.0034859357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999159,0.00027338453,0.000054654436,0.0002573,0.00016590106,0.000089717636],"domain_scores_gemma":[0.99766326,0.0014932464,0.0001956648,0.00022865325,0.000329003,0.00009025107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025588,0.00088608643,0.0025774904,0.0015225693,0.0005475936,0.0010240722,0.0025512886,0.0024637955,0.0012168898],"category_scores_gemma":[0.005387033,0.0010864917,0.001495785,0.001162478,0.001381458,0.0015796783,0.0020658004,0.0015069944,0.00066333247],"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":[0.00043269582,0.00028848945,0.0008855628,0.0004990594,0.00026697025,0.00013704835,0.00021714673,0.6049861,0.036389034,0.009200343,0.0055623264,0.34113526],"study_design_scores_gemma":[0.0000058790465,0.00001827684,0.00014795526,0.0000072867465,0.000009745454,0.00003088519,0.000005231496,0.99310136,0.0024329033,0.004030903,0.00020300671,0.000006545741],"about_ca_topic_score_codex":0.003457415,"about_ca_topic_score_gemma":0.0038016762,"teacher_disagreement_score":0.003457415,"about_ca_system_score_codex":0.0009827493,"about_ca_system_score_gemma":0.0014496462,"threshold_uncertainty_score":0.0135324},"labels":[],"label_agreement":null},{"id":"W4393404820","doi":"10.1109/tmlcn.2024.3384329","title":"Transfer Learning With Reconstruction Loss","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Machine Learning in Communications and Networking","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Transfer of learning; Computer science; Transfer (computing); Artificial intelligence; Parallel computing","score_opus":0.022578982595965476,"score_gpt":0.25661272879570274,"score_spread":0.23403374619973727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393404820","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009211695,0.0008226844,0.98367506,0.000668559,0.000094945426,0.00010198133,0.00011224324,0.0010126284,0.0043001883],"genre_scores_gemma":[0.7156341,0.0013604988,0.26202893,0.0009877298,0.0004236921,0.00086673466,0.001116107,0.00045337033,0.017128835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842876,0.0006396323,0.00009021349,0.00033048895,0.00036834742,0.00014244753],"domain_scores_gemma":[0.9963303,0.002199554,0.00023090333,0.00062723755,0.0004791442,0.00013293164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004646742,0.00220107,0.0018994233,0.0010498646,0.00062237016,0.0015913221,0.003151508,0.0035778743,0.0067277355],"category_scores_gemma":[0.0117859375,0.00061382283,0.0012044517,0.0013402393,0.0021228006,0.0038889328,0.0037709828,0.003645502,0.0022356608],"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":[0.00031314546,0.000214837,0.0010979883,0.0002775702,0.0001436101,0.00021109097,0.00007575851,0.741279,0.001586189,0.051167786,0.0095768375,0.19405618],"study_design_scores_gemma":[0.000016601145,0.00006980577,0.00007948885,0.000014312201,0.000011119873,0.000037693928,0.000008157538,0.9732438,0.00083968806,0.024778083,0.0008939707,0.0000074292084],"about_ca_topic_score_codex":0.001681466,"about_ca_topic_score_gemma":0.0010017735,"teacher_disagreement_score":0.0067277355,"about_ca_system_score_codex":0.0018308617,"about_ca_system_score_gemma":0.0014257826,"threshold_uncertainty_score":0.024574637},"labels":[],"label_agreement":null},{"id":"W4394625538","doi":"10.1109/wacv57701.2024.00221","title":"Domain Generalization by Rejecting Extreme Augmentations","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalization; Computer science; Domain (mathematical analysis); Artificial intelligence; Mathematics","score_opus":0.029020749414381635,"score_gpt":0.26994198945212694,"score_spread":0.24092124003774532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394625538","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":[0.06484586,0.00035927372,0.9279075,0.00046939825,0.00012301687,0.00010731186,0.00020431716,0.0031688584,0.0028144817],"genre_scores_gemma":[0.7875376,0.00025034905,0.20449539,0.0008212834,0.0001481988,0.0003048448,0.0012393254,0.0005842062,0.0046186866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981664,0.00058942626,0.00011801146,0.0004632228,0.0004545183,0.00020846746],"domain_scores_gemma":[0.99387014,0.0025835312,0.00043112578,0.0021675285,0.0006974294,0.00025018933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038510845,0.0013363645,0.0015989023,0.00074316515,0.00061905285,0.0011608441,0.0021283927,0.0017160918,0.003021228],"category_scores_gemma":[0.016427007,0.00049659633,0.0012733345,0.0006181965,0.0022540414,0.0023865304,0.004166537,0.0036875831,0.0014798788],"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":[0.00092296966,0.00042378713,0.0068989554,0.00036969644,0.0001728466,0.0004771478,0.0004281386,0.58513844,0.033742048,0.039622054,0.012709317,0.31909463],"study_design_scores_gemma":[0.00002804054,0.00012344723,0.00055384124,0.000033500237,0.000019125742,0.00014132712,0.000032664935,0.9665269,0.009914649,0.02062301,0.0019811897,0.000022315438],"about_ca_topic_score_codex":0.0012156386,"about_ca_topic_score_gemma":0.0013799999,"teacher_disagreement_score":0.0038510845,"about_ca_system_score_codex":0.0006370822,"about_ca_system_score_gemma":0.001128209,"threshold_uncertainty_score":0.020366728},"labels":[],"label_agreement":null},{"id":"W4396242298","doi":"10.2197/ipsjtbio.17.33","title":"Segmentation of Mouse Brain Slices with Unsupervised Domain Adaptation Considering Cross-sectional Locations","year":2024,"lang":"en","type":"article","venue":"IPSJ Transactions on Bioinformatics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Genetics; Japan Society for the Promotion of Science; Ritsumeikan University","keywords":"Adaptation (eye); Segmentation; Domain adaptation; Computer science; Domain (mathematical analysis); Artificial intelligence; Neuroscience; Biology; Mathematics","score_opus":0.028651243274764555,"score_gpt":0.2721494927949177,"score_spread":0.24349824952015311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396242298","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2523007,0.0007453868,0.7413054,0.00015776025,0.00009363079,0.00017010221,0.0009226718,0.0032613375,0.0010430702],"genre_scores_gemma":[0.5194659,0.00048278537,0.47567725,0.00016907591,0.00003532426,0.00022661006,0.0025228492,0.00046912665,0.0009510426],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997173,0.00005042748,0.00002340752,0.00013215018,0.000043804885,0.000032897384],"domain_scores_gemma":[0.9994141,0.00017849063,0.00006993194,0.0001599818,0.00014204996,0.000035505684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001042171,0.0007528964,0.0006029581,0.0010749364,0.00026319842,0.0005900866,0.00062580575,0.000880524,0.00056727976],"category_scores_gemma":[0.0016916436,0.00037053085,0.00071712025,0.0007657858,0.00047325305,0.0005213552,0.0005986594,0.00079849595,0.00028939804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074966124,0.00023769875,0.0065608765,0.00053029286,0.00024105085,0.00047862213,0.0003568077,0.13487,0.6209721,0.0023547397,0.0040525817,0.22859572],"study_design_scores_gemma":[0.000026740378,0.00020725164,0.009400136,0.000037249996,0.00008819761,0.0006945534,0.000121497105,0.7826742,0.19929336,0.004016429,0.0033860325,0.00005441782],"about_ca_topic_score_codex":0.0016631269,"about_ca_topic_score_gemma":0.0027940592,"teacher_disagreement_score":0.0016631269,"about_ca_system_score_codex":0.00039857082,"about_ca_system_score_gemma":0.0007353079,"threshold_uncertainty_score":0.005511582},"labels":[],"label_agreement":null},{"id":"W4396778270","doi":"10.3389/frai.2024.1255566","title":"Leveraging diffusion models for unsupervised out-of-distribution detection on image manifold","year":2024,"lang":"en","type":"article","venue":"Frontiers in Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Division of Materials Research; Materials Research Science and Engineering Center, Harvard University; Natural Sciences and Engineering Research Council of Canada; Cornell Center for Materials Research; Defense Advanced Research Projects Agency; National Science Foundation","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Domain (mathematical analysis); Inference; Subspace topology; Image (mathematics); Manifold (fluid mechanics); Computer vision; Mathematics","score_opus":0.05809887125542123,"score_gpt":0.28941310745885995,"score_spread":0.23131423620343872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396778270","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":[0.032455157,0.0002460252,0.9657569,0.00017152003,0.000028421558,0.000046123987,0.000057630743,0.000844244,0.00039385533],"genre_scores_gemma":[0.6327166,0.00048828166,0.3630624,0.00031143427,0.00013276942,0.000121360266,0.0006807126,0.0003816609,0.002104748],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992279,0.0001585475,0.000043548906,0.00027218403,0.00021006372,0.00008772097],"domain_scores_gemma":[0.9969457,0.0015168961,0.00040742225,0.0005221793,0.00046326185,0.00014462194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001713731,0.0009718321,0.0013385351,0.0018457939,0.0005790863,0.0012752218,0.0018228082,0.0013896333,0.00061039533],"category_scores_gemma":[0.008010963,0.00050148775,0.0010831893,0.0010649936,0.0012202517,0.0022626638,0.001708287,0.002140525,0.00045689743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004411637,0.00037779094,0.012809971,0.000428796,0.00032235758,0.00056948327,0.00076942146,0.3972269,0.078122884,0.02677501,0.0061685233,0.47598773],"study_design_scores_gemma":[0.000006141333,0.000023943743,0.00070334325,0.000008126909,0.000008919086,0.00011092263,0.000024556406,0.9842998,0.005163173,0.009072031,0.00056549313,0.0000135695245],"about_ca_topic_score_codex":0.0032065136,"about_ca_topic_score_gemma":0.004356957,"teacher_disagreement_score":0.0032065136,"about_ca_system_score_codex":0.000865223,"about_ca_system_score_gemma":0.0007817295,"threshold_uncertainty_score":0.009063244},"labels":[],"label_agreement":null},{"id":"W4396834786","doi":"10.1016/j.knosys.2024.111926","title":"Contrastive learning based open-set recognition with unknown score","year":2024,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"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":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Benchmark (surveying); Computer science; Open set; Set (abstract data type); Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.059372020603164974,"score_gpt":0.2876586539285124,"score_spread":0.22828663332534743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396834786","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":[0.03857216,0.0005863129,0.9554444,0.00014571898,0.00013336226,0.00009949004,0.00026073566,0.003227598,0.0015302628],"genre_scores_gemma":[0.5489236,0.00029435818,0.4419334,0.0003440948,0.00016581573,0.00019773886,0.0014906602,0.00042466476,0.006225673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977062,0.00039118988,0.00012456122,0.00097226497,0.0005337364,0.0002720081],"domain_scores_gemma":[0.99632937,0.0018410562,0.00014598294,0.0007155334,0.0008027913,0.00016531908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020683992,0.0012955768,0.002754976,0.0016374976,0.00081709266,0.0020693305,0.004291359,0.0028972258,0.003472606],"category_scores_gemma":[0.006190842,0.0005611553,0.0014863075,0.0012704537,0.0010636562,0.0029493314,0.0035741616,0.0030507152,0.002374921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007147677,0.00041726438,0.0015825273,0.00017147261,0.00021079506,0.00024885812,0.00012927949,0.060807236,0.03202217,0.0060311235,0.00439693,0.8932677],"study_design_scores_gemma":[0.000015870766,0.000089505826,0.0006616289,0.000013662484,0.000042062788,0.00013814091,0.000026806205,0.9748001,0.017582664,0.005908822,0.00069441803,0.000026308944],"about_ca_topic_score_codex":0.004497041,"about_ca_topic_score_gemma":0.006261684,"teacher_disagreement_score":0.004497041,"about_ca_system_score_codex":0.0010810904,"about_ca_system_score_gemma":0.0009380547,"threshold_uncertainty_score":0.011617005},"labels":[],"label_agreement":null},{"id":"W4396846305","doi":"10.1007/s10845-024-02402-6","title":"Selecting subsets of source data for transfer learning with applications in metal additive manufacturing","year":2024,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Transfer of learning; Production (economics); Manufacturing engineering; Computer science; Biochemical engineering; Artificial intelligence; Engineering; Microeconomics; Economics","score_opus":0.03309566026631317,"score_gpt":0.2833188996956974,"score_spread":0.25022323942938424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396846305","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":[0.21528685,0.0012394291,0.77785474,0.00041889277,0.000118293945,0.00018847894,0.0006434788,0.0027618767,0.0014880712],"genre_scores_gemma":[0.8060927,0.00042288384,0.18542145,0.00019993057,0.00009296907,0.0002515027,0.004490579,0.00037125652,0.0026568526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899226,0.00044142295,0.000066831504,0.00025120095,0.00015394638,0.0000942603],"domain_scores_gemma":[0.99565804,0.0025402617,0.00011947928,0.0007825826,0.00078288495,0.000116695184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030756225,0.0013069342,0.0015553338,0.0017772644,0.00085221906,0.0011094428,0.0018810807,0.0018251324,0.0015483437],"category_scores_gemma":[0.009484692,0.00055562396,0.0014549029,0.0015430738,0.0008117189,0.0024719175,0.0023408507,0.0018464713,0.00079264934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012939172,0.0010337585,0.0057955314,0.00034661562,0.00039832675,0.00029243992,0.00034448545,0.24973175,0.026439274,0.0024659906,0.006936416,0.7049215],"study_design_scores_gemma":[0.000024426312,0.00010670175,0.0014098139,0.000014066234,0.000056260807,0.000048772854,0.000096374475,0.98449796,0.008582051,0.0042829616,0.00086364814,0.000016939972],"about_ca_topic_score_codex":0.004317768,"about_ca_topic_score_gemma":0.0035505618,"teacher_disagreement_score":0.004317768,"about_ca_system_score_codex":0.000524222,"about_ca_system_score_gemma":0.0010422921,"threshold_uncertainty_score":0.01626569},"labels":[],"label_agreement":null},{"id":"W4398219779","doi":"10.1007/978-3-031-57534-1_6","title":"Conclusions and Future Research Directions","year":2024,"lang":"en","type":"book-chapter","venue":"SpringerBriefs in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Psychology","score_opus":0.04349654215331714,"score_gpt":0.3215831540746913,"score_spread":0.2780866119213741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398219779","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052173384,0.26805052,0.02164102,0.51125103,0.04014962,0.00043285318,0.0034847383,0.0010467024,0.14872622],"genre_scores_gemma":[0.11348721,0.4356004,0.05611936,0.16384983,0.028544853,0.0014051452,0.008659238,0.0007118223,0.19162208],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9953335,0.0017075078,0.00027490314,0.00093327276,0.0009930449,0.0007578378],"domain_scores_gemma":[0.97294766,0.0093428,0.00069690694,0.0016145996,0.011473972,0.0039239763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013668422,0.001263794,0.0014601564,0.0028141448,0.0026249802,0.011212278,0.004244458,0.0055752415,0.14474542],"category_scores_gemma":[0.023476439,0.00040224876,0.0014790235,0.0036310737,0.0035235728,0.015537417,0.0035124563,0.004598543,0.036263373],"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":[0.000997904,0.000540427,0.0021379052,0.003586072,0.00010406532,0.00024675365,0.0009935093,0.001070848,0.0008255331,0.1790856,0.34167773,0.4687337],"study_design_scores_gemma":[0.00019354338,0.00026548567,0.0019553613,0.0060117757,0.00017763318,0.00033221018,0.00919663,0.001638996,0.0010531432,0.2994414,0.6796541,0.00007968437],"about_ca_topic_score_codex":0.008475035,"about_ca_topic_score_gemma":0.009422326,"teacher_disagreement_score":0.14474542,"about_ca_system_score_codex":0.0038639382,"about_ca_system_score_gemma":0.014564802,"threshold_uncertainty_score":0.4842217},"labels":[],"label_agreement":null},{"id":"W4399197779","doi":"10.1109/cvpr52733.2024.02722","title":"Transductive Zero-Shot and Few-Shot CLIP","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Shot (pellet); Inference; Minification; Pattern recognition (psychology); Machine learning; Class (philosophy); Construct (python library); Code (set theory); Prior probability; Algorithm; Set (abstract data type); Bayesian probability","score_opus":0.0557164139823397,"score_gpt":0.2997714592293158,"score_spread":0.2440550452469761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399197779","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021628711,0.0007388196,0.9700693,0.0004270222,0.00015685466,0.00016397236,0.00051366276,0.003734073,0.002567595],"genre_scores_gemma":[0.5693171,0.00066746114,0.40795162,0.0014386066,0.0004710082,0.00040335717,0.00632916,0.0011563989,0.012265264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997884,0.00058557035,0.00006427651,0.0009154241,0.0003464154,0.00020438686],"domain_scores_gemma":[0.996518,0.0018957318,0.0001678414,0.000878385,0.00035202948,0.00018803404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034286866,0.001900201,0.0022296973,0.0011391526,0.00089119334,0.0021970565,0.004436913,0.002543913,0.004837197],"category_scores_gemma":[0.009517765,0.0008014457,0.0014667609,0.001242189,0.0022700832,0.0040673013,0.0033653583,0.004840657,0.001988112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084081304,0.0006641519,0.0030857758,0.0008242366,0.00032518548,0.00041350033,0.0005502703,0.34374553,0.018371902,0.03339345,0.03298762,0.56479764],"study_design_scores_gemma":[0.00001636854,0.000086147185,0.00047908933,0.000022299035,0.00002076036,0.00011093167,0.00006259103,0.9639531,0.005310118,0.027770681,0.0021437523,0.000024180777],"about_ca_topic_score_codex":0.004339818,"about_ca_topic_score_gemma":0.0060192407,"teacher_disagreement_score":0.004837197,"about_ca_system_score_codex":0.0016768243,"about_ca_system_score_gemma":0.0011529938,"threshold_uncertainty_score":0.018132806},"labels":[],"label_agreement":null},{"id":"W4399708787","doi":"10.1016/j.media.2024.103239","title":"CCSI: Continual Class-Specific Impression for data-free class incremental learning","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver General Hospital; Vector Institute; University of British Columbia","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Artificial intelligence; Forgetting; Machine learning; Class (philosophy); Margin (machine learning); Cross entropy; Normalization (sociology); Deep learning; Pattern recognition (psychology); Data mining","score_opus":0.03183739835390364,"score_gpt":0.3246820824993474,"score_spread":0.29284468414544373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399708787","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008340976,0.00032991136,0.97841483,0.00020398993,0.00020810711,0.00020121307,0.0004662003,0.009730372,0.0021043967],"genre_scores_gemma":[0.26920432,0.0002979983,0.7156983,0.0003968112,0.00028818552,0.00047255476,0.0028849214,0.0016851902,0.009071762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988211,0.0001596239,0.000049870294,0.0003937991,0.00044287616,0.00013274708],"domain_scores_gemma":[0.9961169,0.00096565584,0.00013398488,0.0015922249,0.0008767248,0.00031457262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026091794,0.0010328267,0.0016818567,0.0014442268,0.00084428594,0.0014777762,0.0044730487,0.0019644604,0.011288287],"category_scores_gemma":[0.010917013,0.0006758352,0.0010587177,0.001240916,0.0013806071,0.0025988682,0.0042500217,0.0032710866,0.0030440933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006540215,0.00030915806,0.0011922914,0.00027404228,0.000121421275,0.00011505258,0.00014481615,0.04302473,0.016191335,0.023157172,0.03413292,0.880683],"study_design_scores_gemma":[0.000035932582,0.000118510325,0.00069061323,0.000026666232,0.000027724609,0.000096177915,0.00002818961,0.95940757,0.011339896,0.022158423,0.006038559,0.00003176436],"about_ca_topic_score_codex":0.006179045,"about_ca_topic_score_gemma":0.010347672,"teacher_disagreement_score":0.011288287,"about_ca_system_score_codex":0.0013291909,"about_ca_system_score_gemma":0.0020648844,"threshold_uncertainty_score":0.03776306},"labels":[],"label_agreement":null},{"id":"W4399743014","doi":"10.3390/a17060267","title":"Semi-Self-Supervised Domain Adaptation: Developing Deep Learning Models with Limited Annotated Data for Wheat Head Segmentation","year":2024,"lang":"en","type":"article","venue":"Algorithms","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Domain adaptation; Computer science; Artificial intelligence; Head (geology); Segmentation; Adaptation (eye); Domain (mathematical analysis); Machine learning; Deep learning; Training set; Pattern recognition (psychology); Mathematics; Psychology; Biology","score_opus":0.08303672847703203,"score_gpt":0.29578710347106485,"score_spread":0.21275037499403282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399743014","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13577706,0.0006096253,0.8565748,0.00022967109,0.00008989744,0.00016887902,0.0005455068,0.0043788785,0.0016257806],"genre_scores_gemma":[0.688941,0.0002589988,0.30317953,0.00043729832,0.00005088397,0.00027503274,0.0027594361,0.00031897298,0.0037787927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959904,0.000090782705,0.000016912736,0.00019427671,0.0000464709,0.00005250944],"domain_scores_gemma":[0.9991291,0.00040314655,0.00009189032,0.00013888381,0.00019011556,0.00004682787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011452804,0.001058876,0.0006224354,0.00066020613,0.00026530187,0.0006187128,0.0015038871,0.0012447359,0.0007076528],"category_scores_gemma":[0.0025498825,0.00050065573,0.00083599275,0.0005836847,0.0005967475,0.0009961695,0.00085117295,0.0013238153,0.00047275098],"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":[0.0002072955,0.00018317612,0.0021002057,0.00009932167,0.0000968881,0.00013412026,0.000104374085,0.7866964,0.020671636,0.001343808,0.003187673,0.18517517],"study_design_scores_gemma":[0.0000047633525,0.000019903786,0.00025610707,0.0000042591537,0.000004512519,0.000011781767,0.000008699285,0.9959416,0.0029406287,0.00053730974,0.00026544763,0.000004874052],"about_ca_topic_score_codex":0.007982933,"about_ca_topic_score_gemma":0.010360972,"teacher_disagreement_score":0.007982933,"about_ca_system_score_codex":0.00093105744,"about_ca_system_score_gemma":0.0010319484,"threshold_uncertainty_score":0.015872955},"labels":[],"label_agreement":null},{"id":"W4399889507","doi":"10.1007/s13042-024-02243-y","title":"Subspace learning via Hessian regularized latent representation learning with $${l}_{2,0}$$-norm constraint: unsupervised feature selection","year":2024,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Seneca Polytechnic; Western University; Sunnybrook Health Science Centre","funders":"","keywords":"Feature learning; Artificial intelligence; Subspace topology; Constraint (computer-aided design); Norm (philosophy); Pattern recognition (psychology); Computational intelligence; Feature selection; Representation (politics); Computer science; Mathematics; Machine learning","score_opus":0.008179313839013382,"score_gpt":0.257073041468594,"score_spread":0.24889372762958062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399889507","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":[0.004122113,0.00018350016,0.99429,0.00015438403,0.00003148123,0.00003262774,0.000108303,0.0006324103,0.0004452372],"genre_scores_gemma":[0.26561847,0.00051337114,0.72163063,0.00052252534,0.00017803488,0.00041190523,0.0022622414,0.00070227456,0.008160528],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988537,0.00043329684,0.000052923086,0.00028082042,0.00025680911,0.0001225318],"domain_scores_gemma":[0.9986455,0.00059270195,0.00010245704,0.00024691736,0.00030140346,0.00011102086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014556298,0.001182142,0.0020150354,0.0008420002,0.0007033602,0.0011883157,0.0022964224,0.0016178548,0.0033378177],"category_scores_gemma":[0.004474723,0.00069857616,0.0011750762,0.0014196545,0.0011955537,0.0022412061,0.0024153902,0.0022562365,0.001752989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041845487,0.0004671354,0.0011503834,0.00033633976,0.00022856709,0.00014958136,0.00018491507,0.34328893,0.014236702,0.054193523,0.030678272,0.55466706],"study_design_scores_gemma":[0.000014427913,0.000033784836,0.00010300241,0.000005931258,0.0000069083167,0.00002519954,0.000011531085,0.9879693,0.00087248394,0.010328276,0.0006175469,0.000011590335],"about_ca_topic_score_codex":0.0054379804,"about_ca_topic_score_gemma":0.007838285,"teacher_disagreement_score":0.0054379804,"about_ca_system_score_codex":0.00063452905,"about_ca_system_score_gemma":0.0025703984,"threshold_uncertainty_score":0.011166155},"labels":[],"label_agreement":null},{"id":"W4399913048","doi":"10.48550/arxiv.2406.13653","title":"Dual-Phase Continual Learning: Supervised Adaptation Meets Unsupervised Retention","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Forgetting; Test (biology); Computer science; Artificial intelligence; Psychology; Machine learning; Cognitive psychology; Biology","score_opus":0.10397624978189787,"score_gpt":0.2187472267450198,"score_spread":0.11477097696312194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399913048","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":[0.06868062,0.00044110525,0.9244064,0.0004559309,0.00008665428,0.00014348047,0.00011018815,0.003629703,0.0020459231],"genre_scores_gemma":[0.80610883,0.00017513167,0.1887022,0.0004180659,0.00012594415,0.00023397757,0.0004455168,0.0003345023,0.0034558263],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989341,0.00030548932,0.000053226853,0.00040696142,0.00019294144,0.000107351225],"domain_scores_gemma":[0.9945833,0.0024990945,0.0003169502,0.0015680413,0.00073292013,0.0002996912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027658422,0.0012466849,0.0012985404,0.0006421761,0.00068344345,0.0010779379,0.003647671,0.001711406,0.0020759373],"category_scores_gemma":[0.012947056,0.00072259497,0.0007154574,0.00062420306,0.0015287371,0.0035069946,0.0033491896,0.0033649139,0.0011438406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069086644,0.00086859916,0.0062837405,0.00026267525,0.00015357538,0.00024146077,0.0005029414,0.32006648,0.019561412,0.012259527,0.0069944896,0.6321142],"study_design_scores_gemma":[0.00002626092,0.00012110106,0.00027859074,0.000010090691,0.000012347442,0.000060732204,0.000029395356,0.987748,0.0036486932,0.007350729,0.0006998025,0.000014322328],"about_ca_topic_score_codex":0.0031232298,"about_ca_topic_score_gemma":0.0046028844,"teacher_disagreement_score":0.003647671,"about_ca_system_score_codex":0.0006882351,"about_ca_system_score_gemma":0.0014937324,"threshold_uncertainty_score":0.014627397},"labels":[],"label_agreement":null},{"id":"W4400104382","doi":"10.1007/978-3-031-72114-4_23","title":"Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Lyon; Agence Nationale de la Recherche","keywords":"Computer science; Segmentation; Domain adaptation; Artificial intelligence; Reinforcement learning; Domain (mathematical analysis); Adaptation (eye); Machine learning; Prior probability; Transferability; Pattern recognition (psychology); Bayesian probability; Mathematics","score_opus":0.014707857006786555,"score_gpt":0.24052773144765485,"score_spread":0.2258198744408683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400104382","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019754207,0.00043725755,0.9778707,0.000127633,0.000049815542,0.000030829924,0.0000362992,0.00053742167,0.0011558664],"genre_scores_gemma":[0.7954052,0.00041111623,0.1997815,0.00023351092,0.00008279206,0.00013665226,0.00022735671,0.00015356271,0.0035683108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999705,0.000098770455,0.000013129015,0.00010019497,0.00004200067,0.000040986662],"domain_scores_gemma":[0.99868244,0.0009271319,0.000065433494,0.0000903064,0.00017364768,0.000060971306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011335382,0.00061948545,0.0011193617,0.00043560858,0.00023846621,0.00066152844,0.0010936663,0.0012924481,0.0013968858],"category_scores_gemma":[0.0030783475,0.00045332414,0.0005988922,0.00041435362,0.00065475993,0.00070374337,0.0013626699,0.0013026802,0.00043650705],"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":[0.00017372036,0.00009218998,0.0006271944,0.0000725628,0.00005592127,0.000065712135,0.00007105203,0.823698,0.0066719716,0.0029920628,0.0016034561,0.16387615],"study_design_scores_gemma":[0.0000039655292,0.000012625861,0.00007565568,0.0000031472161,0.0000032581625,0.00001000438,0.0000026538687,0.99831235,0.00051577954,0.0009639983,0.00009374796,0.0000028627894],"about_ca_topic_score_codex":0.00495121,"about_ca_topic_score_gemma":0.0030010229,"teacher_disagreement_score":0.00495121,"about_ca_system_score_codex":0.0005563733,"about_ca_system_score_gemma":0.0006754852,"threshold_uncertainty_score":0.00984478},"labels":[],"label_agreement":null},{"id":"W4400248355","doi":"10.1007/978-3-031-58181-6_38","title":"ConvMTL: Multi-task Learning via Self-supervised Learning for Simultaneous Dense Predictions","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Athabasca University","funders":"","keywords":"Computer science; Task (project management); Artificial intelligence; Supervised learning; Machine learning; Engineering; Artificial neural network","score_opus":0.03268561146353205,"score_gpt":0.28626544466361087,"score_spread":0.2535798332000788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400248355","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":[0.0033101474,0.0012873812,0.968272,0.0003277262,0.00035906766,0.00013444404,0.0010414877,0.022816764,0.0024509265],"genre_scores_gemma":[0.10734867,0.001058615,0.85429144,0.0010499893,0.0004485069,0.0007424409,0.009086801,0.0037763207,0.02219714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864703,0.00028439006,0.00007080305,0.0005419706,0.00031636088,0.00013946289],"domain_scores_gemma":[0.99842465,0.00071744854,0.00007124653,0.0004363626,0.00026038836,0.00008986458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002078722,0.0030944587,0.002465369,0.001261079,0.00080144755,0.0020955298,0.006482432,0.0035575393,0.012716684],"category_scores_gemma":[0.004372582,0.0015896623,0.0020992623,0.0019018232,0.0010015874,0.003933182,0.004095076,0.004664644,0.009347404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028879393,0.00029272013,0.00030681794,0.0003460131,0.00025868294,0.00017571467,0.00007665732,0.099434294,0.0060863644,0.008786468,0.08051413,0.80343324],"study_design_scores_gemma":[0.0000224676,0.000042713706,0.00009303445,0.000019080611,0.000021037307,0.00004008703,0.000012403445,0.97942984,0.003048339,0.012936218,0.004318449,0.00001627089],"about_ca_topic_score_codex":0.010313488,"about_ca_topic_score_gemma":0.016497549,"teacher_disagreement_score":0.012716684,"about_ca_system_score_codex":0.0012412794,"about_ca_system_score_gemma":0.0014859671,"threshold_uncertainty_score":0.042541564},"labels":[],"label_agreement":null},{"id":"W4400524898","doi":"10.1109/access.2024.3426542","title":"Memory-Efficient Continual Learning Object Segmentation for Long Videos","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Alliance de recherche numérique du Canada; Microsoft","keywords":"Computer science; Artificial intelligence; Segmentation; Robustness (evolution); Object (grammar); Machine learning; Computer vision; Representation (politics); Market segmentation; Pattern recognition (psychology)","score_opus":0.0325712815419496,"score_gpt":0.33668637769122506,"score_spread":0.30411509614927545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400524898","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09279156,0.0011492311,0.8985998,0.00028794812,0.00008571538,0.00010282217,0.0002160197,0.0049151843,0.0018517055],"genre_scores_gemma":[0.74984777,0.00048564855,0.24313562,0.00034667543,0.000117197545,0.00015141345,0.0011725726,0.00035470122,0.0043883584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995135,0.000057311234,0.000030434023,0.00022979194,0.00010863758,0.00006033562],"domain_scores_gemma":[0.99908936,0.0003198677,0.00013026131,0.00021923259,0.00017120386,0.00007005591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097481953,0.0011654445,0.0011702095,0.00083079643,0.0004844423,0.00077284744,0.002581486,0.0011209677,0.0015778698],"category_scores_gemma":[0.002594146,0.00053658494,0.0007613277,0.00078765134,0.0009516861,0.0016761794,0.0014396968,0.0012890667,0.0006872519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005747321,0.0002804185,0.00205926,0.00016215246,0.00008496096,0.00018824873,0.00019611954,0.35591528,0.024131138,0.0030399512,0.0036094894,0.6097582],"study_design_scores_gemma":[0.000007101936,0.000041790743,0.00023435988,0.000005988054,0.000006656297,0.00003513302,0.000014259009,0.99432796,0.0035064737,0.0013950315,0.00041833127,0.000006933993],"about_ca_topic_score_codex":0.008245529,"about_ca_topic_score_gemma":0.008508639,"teacher_disagreement_score":0.008245529,"about_ca_system_score_codex":0.0008485064,"about_ca_system_score_gemma":0.0012844531,"threshold_uncertainty_score":0.016395092},"labels":[],"label_agreement":null},{"id":"W4400617758","doi":"10.1016/j.patcog.2024.110779","title":"Few-shot relational triple extraction with hierarchical prototype optimization","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"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 Waterloo","funders":"Major Basic Research Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions","keywords":"Computer science; Shot (pellet); Extraction (chemistry); Artificial intelligence; Pattern recognition (psychology); Computer vision; Chromatography; Chemistry","score_opus":0.051944685425059464,"score_gpt":0.2807868888145493,"score_spread":0.2288422033894898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400617758","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":[0.017151078,0.00065882405,0.9729944,0.00015781993,0.000077494005,0.0001666917,0.00076491566,0.0068663317,0.0011624172],"genre_scores_gemma":[0.20955303,0.0003823672,0.77832645,0.0002687794,0.00006704405,0.0002513249,0.0066111092,0.00074128696,0.003798543],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986594,0.0001525867,0.00010814363,0.00063674815,0.00031637267,0.00012691037],"domain_scores_gemma":[0.99843043,0.00045304012,0.000090664296,0.0005626171,0.00039381723,0.00006938465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001041896,0.0012092377,0.0022637758,0.0023252324,0.00082823465,0.0016075438,0.0035016362,0.0017117554,0.005879201],"category_scores_gemma":[0.0036374952,0.0007692131,0.0019219129,0.0023091524,0.0006395574,0.0031148025,0.0024364404,0.002008488,0.0039756256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041739573,0.000360153,0.0014850384,0.0004134847,0.00022361493,0.00029331126,0.00015989787,0.030274559,0.03453219,0.006158191,0.017630005,0.9080522],"study_design_scores_gemma":[0.000038774826,0.00014522606,0.0010492493,0.000039946277,0.0001258434,0.00044978107,0.00016855518,0.9465448,0.02409425,0.022035193,0.0052615963,0.000046662037],"about_ca_topic_score_codex":0.0041425438,"about_ca_topic_score_gemma":0.009072395,"teacher_disagreement_score":0.005879201,"about_ca_system_score_codex":0.00066686183,"about_ca_system_score_gemma":0.0013916634,"threshold_uncertainty_score":0.019667864},"labels":[],"label_agreement":null},{"id":"W4400976991","doi":"10.1145/3681784","title":"A Self-Distilled Learning to Rank Model for <i>Ad Hoc</i> Retrieval","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Outlier; Generalizability theory; Artificial intelligence; Rank (graph theory); Sample (material); Learning to rank; Machine learning; Feature (linguistics); Information retrieval; Ranking (information retrieval); Statistics; Mathematics","score_opus":0.022097195304718847,"score_gpt":0.2622081396623819,"score_spread":0.24011094435766303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400976991","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":[0.014753549,0.000852929,0.9795155,0.00043258074,0.00008336522,0.00016136207,0.00043946266,0.002150202,0.0016110458],"genre_scores_gemma":[0.52346194,0.0011783661,0.45212927,0.00092991214,0.00055175315,0.00069161475,0.0030193888,0.00045317598,0.017584538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99724364,0.00095737237,0.00020381904,0.00059782644,0.00071908906,0.00027828224],"domain_scores_gemma":[0.9948891,0.0021867955,0.0005361429,0.0010814314,0.0011358592,0.00017075503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004008277,0.0015176053,0.0022195766,0.0015957123,0.0007240222,0.0025156396,0.0034445797,0.0021156743,0.0042232615],"category_scores_gemma":[0.01107359,0.00064119266,0.0013072111,0.0021445244,0.0014422025,0.0038047184,0.0017269761,0.0025088727,0.0040660826],"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":[0.0005397818,0.00040363052,0.0025485603,0.0004061915,0.00016166276,0.00016235339,0.00021857601,0.5418393,0.0058898544,0.033904653,0.014239771,0.39968568],"study_design_scores_gemma":[0.000024068588,0.0001545483,0.00019336397,0.000014481015,0.000024596486,0.0000670614,0.000018800043,0.9853395,0.0015764157,0.010879899,0.0016795461,0.000027688326],"about_ca_topic_score_codex":0.00611718,"about_ca_topic_score_gemma":0.0070167044,"teacher_disagreement_score":0.00611718,"about_ca_system_score_codex":0.00122069,"about_ca_system_score_gemma":0.0017323493,"threshold_uncertainty_score":0.021198034},"labels":[],"label_agreement":null},{"id":"W4401009456","doi":"10.1007/s10462-024-10853-9","title":"Knowledge transfer in lifelong machine learning: a systematic literature review","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Lifelong learning; Knowledge transfer; Transfer of learning; Artificial intelligence; Systematic review; Machine learning; Knowledge management; Psychology; MEDLINE; Pedagogy; Chemistry","score_opus":0.06351313169488046,"score_gpt":0.34313097841094625,"score_spread":0.2796178467160658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401009456","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":[0.0010656622,0.9964557,0.00069397263,0.000792024,0.00009775253,0.00018562452,0.00021437625,0.000014883854,0.00047990767],"genre_scores_gemma":[0.01981982,0.9757817,0.0022455605,0.00088521274,0.0001370731,0.00062832877,0.00033052394,0.0000112331945,0.00016047753],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9902776,0.0039940616,0.0027623863,0.00084817293,0.0018632634,0.00025445587],"domain_scores_gemma":[0.9041204,0.08081933,0.0069942796,0.0013672075,0.0061267796,0.00057203934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01414305,0.0011298868,0.0045639426,0.0135039985,0.0007911616,0.004055582,0.0024392642,0.002225152,0.0055413307],"category_scores_gemma":[0.07406946,0.00080667646,0.0043580476,0.011738176,0.0012076735,0.0054195416,0.0028186846,0.0018637654,0.00058769784],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014566553,0.00010383961,0.0015106017,0.6923883,0.004064528,0.0001422322,0.00063681667,0.0005993692,0.00012986278,0.0019861797,0.0047597494,0.29353288],"study_design_scores_gemma":[0.000092184695,0.00028509964,0.004097375,0.9298715,0.010881478,0.0003553747,0.00086443266,0.0005762621,0.00026733507,0.0025139195,0.0501255,0.000069390226],"about_ca_topic_score_codex":0.00579847,"about_ca_topic_score_gemma":0.013859099,"teacher_disagreement_score":0.01414305,"about_ca_system_score_codex":0.0043387758,"about_ca_system_score_gemma":0.017390773,"threshold_uncertainty_score":0.0747965},"labels":[],"label_agreement":null},{"id":"W4401009936","doi":"10.1145/3654522.3654541","title":"Unsupervised Adversarial Domain Adaptation for Estimating Occupancy and Recognizing Activities in Smart Buildings","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Occupancy; Domain adaptation; Computer science; Adversarial system; Adaptation (eye); Artificial intelligence; Domain (mathematical analysis); Machine learning; Engineering; Architectural engineering; Mathematics","score_opus":0.031106612786513808,"score_gpt":0.2757522656257859,"score_spread":0.24464565283927212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401009936","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":[0.036846567,0.00032427142,0.9602163,0.00020560488,0.000049874536,0.000034770055,0.00011310399,0.00053401943,0.0016755181],"genre_scores_gemma":[0.93024665,0.00028753138,0.06399912,0.00023699034,0.000065070126,0.00011152572,0.00048540987,0.00007675972,0.004490836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953103,0.00016374001,0.00001566837,0.00013975645,0.000085305124,0.000064552594],"domain_scores_gemma":[0.99912184,0.0005513731,0.00008771952,0.00009853799,0.000094218245,0.000046374138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072352524,0.0008328069,0.0007573203,0.00029028702,0.00020434726,0.00042686192,0.00096989144,0.0006837822,0.0011974792],"category_scores_gemma":[0.0021672775,0.0003257894,0.0006547902,0.00032224905,0.0010126628,0.0006711435,0.0010010989,0.0014110504,0.00034618282],"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":[0.0000752234,0.000034740166,0.0010865573,0.000028980578,0.00002639761,0.000059686205,0.00003808357,0.9647683,0.0015844575,0.0046241013,0.0009856366,0.026687974],"study_design_scores_gemma":[0.0000016213505,0.000007718627,0.00014325928,0.000002033928,0.0000019295298,0.000007577143,0.000003527596,0.9980008,0.00029763897,0.0013618115,0.00016919312,0.000002864856],"about_ca_topic_score_codex":0.004636819,"about_ca_topic_score_gemma":0.0036866106,"teacher_disagreement_score":0.004636819,"about_ca_system_score_codex":0.00060813996,"about_ca_system_score_gemma":0.00047234155,"threshold_uncertainty_score":0.009219646},"labels":[],"label_agreement":null},{"id":"W4401042486","doi":"10.18653/v1/2024.findings-naacl.44","title":"Source-Free Unsupervised Domain Adaptation for Question Answering via Prompt-Assisted Self-learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Adaptation (eye); Domain adaptation; Unsupervised learning; Question answering; Domain (mathematical analysis); Artificial intelligence; Psychology; Mathematics","score_opus":0.01662806158667236,"score_gpt":0.25161491799582675,"score_spread":0.2349868564091544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401042486","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023289256,0.0006280272,0.9675028,0.0001973645,0.00009242739,0.00012888727,0.00016692458,0.00697212,0.0010222368],"genre_scores_gemma":[0.5761985,0.00035574147,0.41641504,0.0009250272,0.00011485364,0.00040973327,0.001696785,0.00056400255,0.0033203084],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983203,0.0007627996,0.00006479437,0.0006072802,0.00017010086,0.000074807016],"domain_scores_gemma":[0.99523443,0.0025852513,0.0001919063,0.0011733186,0.00063351827,0.000181496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027216622,0.001248223,0.0010460443,0.00091109454,0.0005416656,0.0010796217,0.0023956501,0.0017611487,0.002398882],"category_scores_gemma":[0.010878141,0.0004878885,0.0010344135,0.00077549263,0.0010327297,0.0030732478,0.0030925823,0.0027613596,0.0016832855],"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":[0.00046789803,0.0007765954,0.0046410696,0.00052925193,0.00021287195,0.0002576768,0.0011407692,0.1477551,0.03823808,0.007820681,0.011884442,0.78627557],"study_design_scores_gemma":[0.000036961996,0.00015248546,0.000649758,0.000023922996,0.000027762515,0.00014171709,0.000117793774,0.9717539,0.009893425,0.013335989,0.0038386097,0.000027626707],"about_ca_topic_score_codex":0.0017029381,"about_ca_topic_score_gemma":0.0027244773,"teacher_disagreement_score":0.0027216622,"about_ca_system_score_codex":0.0007172679,"about_ca_system_score_gemma":0.0008972962,"threshold_uncertainty_score":0.014393747},"labels":[],"label_agreement":null},{"id":"W4401138841","doi":"10.1080/07038992.2024.2374788","title":"Active Reinforcement Learning for the Semantic Segmentation of Urban Images","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Segmentation; Computer science; Reinforcement learning; Machine learning; Image segmentation; Pattern recognition (psychology); Metric (unit); Selection (genetic algorithm)","score_opus":0.019137259734558876,"score_gpt":0.2551111093448136,"score_spread":0.23597384961025475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401138841","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":[0.07956551,0.00031331554,0.9165912,0.00023685218,0.000040550472,0.00008596922,0.00012113503,0.0015599644,0.0014854207],"genre_scores_gemma":[0.83735067,0.000100933845,0.1598458,0.00016650386,0.000026871408,0.00011822981,0.0003667585,0.00012594646,0.0018984253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995437,0.00015391859,0.000016382275,0.00015624006,0.00007334468,0.00005636079],"domain_scores_gemma":[0.9989003,0.0006382796,0.000116266405,0.00010851306,0.00016485798,0.00007187264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016203785,0.00084627216,0.0009252054,0.0007395155,0.00037724644,0.0007130451,0.0015503605,0.0009628903,0.0012330731],"category_scores_gemma":[0.003022886,0.00043343953,0.0006479748,0.00059002877,0.0010823363,0.0013718199,0.00094767957,0.0014551807,0.00030215012],"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":[0.0002048873,0.00014326225,0.0009230764,0.00005654253,0.00004931436,0.00006430909,0.00009679511,0.88727015,0.0062319725,0.0042460947,0.0011618881,0.09955168],"study_design_scores_gemma":[0.0000032627272,0.000012048485,0.00005659058,0.0000014645772,0.0000017959921,0.0000033923177,0.0000039644897,0.99755293,0.0009851926,0.0012760903,0.000101559344,0.000001783255],"about_ca_topic_score_codex":0.007747835,"about_ca_topic_score_gemma":0.008485347,"teacher_disagreement_score":0.007747835,"about_ca_system_score_codex":0.0013794407,"about_ca_system_score_gemma":0.0011507334,"threshold_uncertainty_score":0.015405476},"labels":[],"label_agreement":null},{"id":"W4401328745","doi":"10.1016/j.patrec.2024.08.003","title":"Enhancing zero-shot object detection with external knowledge-guided robust contrast learning","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Discriminative model; Artificial intelligence; Robustness (evolution); Contrast (vision); Adversarial system; Machine learning; Consistency (knowledge bases); Object detection; Pattern recognition (psychology)","score_opus":0.03665023461828474,"score_gpt":0.2559155425210343,"score_spread":0.2192653079027496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401328745","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":[0.03902503,0.00035617224,0.95769733,0.00012193514,0.000054442975,0.000035481902,0.000060078226,0.0009729127,0.0016766979],"genre_scores_gemma":[0.61292267,0.00034974402,0.38072658,0.00047096808,0.00008432179,0.000064506785,0.00052108377,0.0003232964,0.0045369077],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999463,0.00008024945,0.00001983419,0.00018034615,0.0001688144,0.000087819964],"domain_scores_gemma":[0.998882,0.00050517055,0.00007067168,0.00022517213,0.00024080479,0.000076169265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086614065,0.0009672693,0.0014439126,0.0008064774,0.00034170103,0.0012139145,0.0018662598,0.0014927768,0.0016811122],"category_scores_gemma":[0.0030440108,0.0004293384,0.00086007256,0.00054320035,0.00084497844,0.0017969126,0.0028412072,0.0015220215,0.0008144132],"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":[0.00083020725,0.0005593703,0.0019981365,0.00033950323,0.00030166746,0.00025418503,0.00015186248,0.110445656,0.19168925,0.00902101,0.0036136024,0.6807956],"study_design_scores_gemma":[0.00001682668,0.0001173643,0.0009301247,0.000012343579,0.000040962856,0.00018432132,0.000020550287,0.95122963,0.040049925,0.0063690157,0.0010091513,0.000019712534],"about_ca_topic_score_codex":0.0019871613,"about_ca_topic_score_gemma":0.0030742954,"teacher_disagreement_score":0.0019871613,"about_ca_system_score_codex":0.0004822952,"about_ca_system_score_gemma":0.00086262426,"threshold_uncertainty_score":0.005623877},"labels":[],"label_agreement":null},{"id":"W4401478512","doi":"10.5753/jbcs.2024.3966","title":"Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy","year":2024,"lang":"en","type":"article","venue":"Journal of the Brazilian Computer Society","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Institute for Catastrophic Loss Reduction","keywords":"Forgetting; Taxonomy (biology); Computer science; Artificial intelligence; Natural language processing; Psychology; Cognitive psychology; Biology; Ecology","score_opus":0.018465004560146547,"score_gpt":0.23907906175264582,"score_spread":0.22061405719249927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401478512","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":[0.016819932,0.560509,0.40372077,0.0051545408,0.00092117966,0.00030715184,0.00031342398,0.0007796199,0.011474445],"genre_scores_gemma":[0.27828902,0.55178475,0.15440418,0.0028150706,0.0040457495,0.0006271904,0.00094807293,0.00025775091,0.0068281954],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99767,0.00038663644,0.00032816947,0.0004768344,0.0009880908,0.0001502581],"domain_scores_gemma":[0.9924394,0.004491292,0.0007718326,0.00061672134,0.001452894,0.00022775772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00342165,0.0016320873,0.001542999,0.0048032408,0.0008306867,0.003442878,0.0030486784,0.0030684231,0.001288201],"category_scores_gemma":[0.011408672,0.0008044967,0.0010816343,0.0048369993,0.0030037074,0.007130983,0.0030146197,0.003705383,0.0006168146],"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":[0.00014407278,0.00019446682,0.007516153,0.004882589,0.00020656317,0.00031863688,0.00063023536,0.02268981,0.0012224972,0.10802023,0.009232185,0.8449426],"study_design_scores_gemma":[0.00005895892,0.0006487544,0.008507608,0.0049794223,0.0004914375,0.0034005793,0.00089941104,0.1806159,0.008454566,0.5790807,0.21239658,0.00046606446],"about_ca_topic_score_codex":0.0024093313,"about_ca_topic_score_gemma":0.0014963847,"teacher_disagreement_score":0.0048032408,"about_ca_system_score_codex":0.0018381121,"about_ca_system_score_gemma":0.0019526129,"threshold_uncertainty_score":0.018095613},"labels":[],"label_agreement":null},{"id":"W4401644781","doi":"10.3390/info15080491","title":"Beyond Supervised: The Rise of Self-Supervised Learning in Autonomous Systems","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":17,"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 Canada West","funders":"","keywords":"Supervised learning; Computer science; Artificial intelligence; Machine learning; Artificial neural network","score_opus":0.01014857225381052,"score_gpt":0.22289965936423545,"score_spread":0.21275108711042492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401644781","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00669687,0.0073987646,0.9783361,0.0031604164,0.00020783076,0.00008608756,0.00011500382,0.00044975046,0.0035491623],"genre_scores_gemma":[0.44650772,0.013667565,0.5306554,0.0021827766,0.002341179,0.00044875397,0.0005920449,0.00048520073,0.003119429],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931851,0.0029338158,0.00029534832,0.0016933817,0.0017355491,0.00015674869],"domain_scores_gemma":[0.9668463,0.025025265,0.0013500635,0.0036090675,0.002574977,0.0005943977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009209053,0.001467333,0.0023680506,0.0019866251,0.0009480168,0.004382318,0.0028515998,0.0031941752,0.0012536698],"category_scores_gemma":[0.03058317,0.0009722595,0.0010827235,0.0017948381,0.0060741617,0.008920788,0.0036375436,0.006250967,0.0006532871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023287965,0.00027781352,0.006730206,0.0017374652,0.00053392467,0.000231992,0.0008760837,0.27670646,0.0033166667,0.27971187,0.011594016,0.41805068],"study_design_scores_gemma":[0.000019073253,0.00011804429,0.00083188625,0.00016569509,0.00003351188,0.00009790371,0.000085711166,0.6592934,0.0017567712,0.32877496,0.0087615065,0.0000615175],"about_ca_topic_score_codex":0.0020861132,"about_ca_topic_score_gemma":0.0016042389,"teacher_disagreement_score":0.009209053,"about_ca_system_score_codex":0.0020380614,"about_ca_system_score_gemma":0.0019000145,"threshold_uncertainty_score":0.048702717},"labels":[],"label_agreement":null},{"id":"W4401667261","doi":"10.54097/f09tdt83","title":"Adaptive Neural Network Architectures for Cross-Domain Generalization","year":2024,"lang":"en","type":"article","venue":"Jisuanji shenghuojia.","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of British Columbia","funders":"","keywords":"Computer science; Modular design; Robustness (evolution); Adaptability; Artificial intelligence; Artificial neural network; Benchmark (surveying); Machine learning; Domain (mathematical analysis)","score_opus":0.026714083933541872,"score_gpt":0.3048156728266197,"score_spread":0.27810158889307784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401667261","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":[0.036540896,0.0011094873,0.95912755,0.00027970594,0.00006892817,0.00004767531,0.000057941466,0.00094758224,0.0018202185],"genre_scores_gemma":[0.82641643,0.00081540295,0.16817595,0.000367927,0.00010662023,0.00015596327,0.0003600702,0.00012502122,0.0034765697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955744,0.000106162,0.00003035518,0.0001876459,0.00007483253,0.000043509917],"domain_scores_gemma":[0.9991566,0.00033097094,0.00010619667,0.00019052137,0.00017827965,0.000037377926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013584961,0.0012547153,0.0009853612,0.00078389276,0.0004298929,0.0007670486,0.0018193532,0.0012037617,0.0013309213],"category_scores_gemma":[0.0029907795,0.00042801604,0.0009518638,0.0008094839,0.0007954547,0.001832663,0.0015223426,0.0019921702,0.0004599311],"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":[0.000071869115,0.00009341962,0.001143262,0.00007506693,0.00013507229,0.000090199974,0.00008331965,0.7802943,0.0075641293,0.007230053,0.0018649834,0.20135422],"study_design_scores_gemma":[0.0000025542054,0.000018517645,0.00013912068,0.0000039757015,0.000009547269,0.000015380212,0.0000055093815,0.99438745,0.0008297632,0.0042871544,0.0002960092,0.0000050553067],"about_ca_topic_score_codex":0.0037625795,"about_ca_topic_score_gemma":0.003298366,"teacher_disagreement_score":0.0037625795,"about_ca_system_score_codex":0.0009158371,"about_ca_system_score_gemma":0.0006086389,"threshold_uncertainty_score":0.0074813366},"labels":[],"label_agreement":null},{"id":"W4401751019","doi":"10.1109/isbi56570.2024.10635661","title":"ConvLoRA and AdaBN Based Domain Adaptation via Self-Training","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":12,"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":"Science Foundation Ireland","keywords":"Computer science; Domain adaptation; Adaptation (eye); Training (meteorology); Domain (mathematical analysis); Artificial intelligence; Psychology; Mathematics","score_opus":0.02466396835769601,"score_gpt":0.24120256334023524,"score_spread":0.21653859498253922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401751019","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":[0.015609183,0.0004772262,0.9602674,0.00016351529,0.0002158655,0.00013372253,0.0004370341,0.01845664,0.00423943],"genre_scores_gemma":[0.2255633,0.00043265958,0.7466102,0.00083916093,0.00011094041,0.00051159476,0.0044030617,0.0025460785,0.018982943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952364,0.00007121607,0.000022322467,0.00022960192,0.00009383155,0.000059468537],"domain_scores_gemma":[0.99935097,0.00013332577,0.00004667436,0.00025344832,0.00016828322,0.00004725743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008363385,0.001454864,0.0007824711,0.00074227154,0.00041578838,0.0008219147,0.0024250827,0.0010727497,0.0069893617],"category_scores_gemma":[0.0021637236,0.00055281736,0.0010271677,0.000739172,0.00065463386,0.0014586651,0.0017183127,0.0028909855,0.005414817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025994092,0.00036007128,0.00164663,0.0003004881,0.00018624024,0.00015345073,0.00015402943,0.14128226,0.043207474,0.010557867,0.043354094,0.7585375],"study_design_scores_gemma":[0.000029862316,0.00007734926,0.0005626771,0.00002343777,0.000022552354,0.00012845235,0.000036728965,0.96041274,0.019429807,0.0070008035,0.012247964,0.000027625687],"about_ca_topic_score_codex":0.0056706313,"about_ca_topic_score_gemma":0.01164263,"teacher_disagreement_score":0.0069893617,"about_ca_system_score_codex":0.00080610596,"about_ca_system_score_gemma":0.00097151037,"threshold_uncertainty_score":0.02338171},"labels":[],"label_agreement":null},{"id":"W4401793915","doi":"10.1038/s41586-024-07711-7","title":"Loss of plasticity in deep continual learning","year":2024,"lang":"en","type":"article","venue":"Nature","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of Alberta","funders":"Alliance de recherche numérique du Canada; DeepMind; Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Backpropagation; Artificial intelligence; Deep learning; Computer science; Artificial neural network; Plasticity; Machine learning; Materials science","score_opus":0.006799586821035106,"score_gpt":0.25775251507210367,"score_spread":0.2509529282510686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401793915","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":[0.276455,0.0013109031,0.7120678,0.0017787114,0.00014225532,0.00008965746,0.00017906226,0.0012611137,0.00671547],"genre_scores_gemma":[0.96484727,0.00029730835,0.03210729,0.00018104346,0.00003200169,0.000073387,0.0000941281,0.000093371105,0.0022741563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954623,0.000109075176,0.00003099977,0.000109650166,0.00014468301,0.000059361995],"domain_scores_gemma":[0.9974291,0.0013874354,0.00033111978,0.00042244533,0.0002603901,0.00016950791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001588821,0.0004950511,0.00060967647,0.00037497235,0.0004251392,0.0009851509,0.0014111821,0.0009236539,0.0015752284],"category_scores_gemma":[0.009431691,0.00038119775,0.00041102836,0.0003443801,0.0023052897,0.0028454103,0.0018393699,0.0021819335,0.00028872341],"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":[0.000272138,0.00021364041,0.0047072824,0.0002769724,0.00008280717,0.00040969835,0.00029773972,0.76041937,0.018324152,0.10250829,0.0030000326,0.10948784],"study_design_scores_gemma":[0.000011580104,0.00008608866,0.00083713257,0.000020334126,0.000008124692,0.00012089309,0.000022683405,0.9184197,0.0034010122,0.07601148,0.0010482955,0.000012654243],"about_ca_topic_score_codex":0.0012694243,"about_ca_topic_score_gemma":0.0012913395,"teacher_disagreement_score":0.001588821,"about_ca_system_score_codex":0.0011126318,"about_ca_system_score_gemma":0.00055938907,"threshold_uncertainty_score":0.008402586},"labels":[],"label_agreement":null},{"id":"W4401808768","doi":"10.1007/s00521-024-10353-5","title":"Vision transformers in domain adaptation and domain generalization: a study of robustness","year":2024,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":42,"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 Victoria","funders":"","keywords":"Computational Science and Engineering; Robustness (evolution); Computer science; Domain adaptation; Transformer; Artificial intelligence; Generalization; Machine learning; Mathematics; Mathematical analysis; Electrical engineering","score_opus":0.021588301028221075,"score_gpt":0.2907351512044086,"score_spread":0.26914685017618756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401808768","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":[0.082511686,0.0014729429,0.9085649,0.0009133916,0.00005955627,0.00006658419,0.000081101876,0.0003048817,0.0060249856],"genre_scores_gemma":[0.9361645,0.0014455187,0.058516134,0.00028399582,0.00022086463,0.00009518413,0.00013394252,0.00022182889,0.0029180336],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99687445,0.0014275173,0.00012851122,0.00076559174,0.00057230797,0.00023164143],"domain_scores_gemma":[0.94596493,0.043502297,0.0029675146,0.005080357,0.0014666243,0.0010183094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009021955,0.0009979897,0.0016713836,0.0018477732,0.0008080732,0.0029098422,0.0026564936,0.0024510263,0.0025012777],"category_scores_gemma":[0.055491757,0.0008134139,0.0015685749,0.001224274,0.0060719284,0.007208166,0.005137098,0.004178667,0.00023106651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043078398,0.00012594373,0.002016329,0.0002845829,0.0002959507,0.00023863708,0.00045737592,0.27576107,0.0080580935,0.65267575,0.0013929504,0.058262542],"study_design_scores_gemma":[0.000025900208,0.0001263572,0.00066970795,0.00002636117,0.00004665111,0.00015184093,0.00007184129,0.6448909,0.0026257788,0.35086042,0.000475162,0.00002907671],"about_ca_topic_score_codex":0.0019402945,"about_ca_topic_score_gemma":0.0006911887,"teacher_disagreement_score":0.009021955,"about_ca_system_score_codex":0.0014938839,"about_ca_system_score_gemma":0.000925709,"threshold_uncertainty_score":0.04771322},"labels":[],"label_agreement":null},{"id":"W4402029541","doi":"10.1007/978-3-031-70368-3_12","title":"MEGA: Multi-encoder GNN Architecture for Stronger Task Collaboration and Generalization","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Computer science; Generalization; Task (project management); Mega-; Architecture; Encoder; Artificial intelligence; Computer architecture; Operating system; Systems engineering; Mathematics; Engineering; Geography","score_opus":0.018507009773168,"score_gpt":0.2667492491617782,"score_spread":0.24824223938861023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402029541","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":[0.0223875,0.0013136107,0.9459505,0.00042137547,0.0007492441,0.00009916169,0.0008333422,0.01903089,0.00921434],"genre_scores_gemma":[0.36023325,0.00079742965,0.5962997,0.0012185812,0.00024408981,0.00029667516,0.003950902,0.0017258278,0.03523348],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972814,0.00004241382,0.000012594232,0.00012367204,0.000042442065,0.00005075219],"domain_scores_gemma":[0.9995529,0.00011800095,0.000016138963,0.00014901304,0.00010917894,0.000054842367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000804652,0.001178621,0.00077576685,0.00045323776,0.00038471306,0.0008324499,0.0022087533,0.0017452212,0.01147475],"category_scores_gemma":[0.00161875,0.0006270688,0.00059039553,0.00057696056,0.00041154315,0.0016851914,0.0018995843,0.002736398,0.0056025614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000513469,0.00031632473,0.0006082743,0.00022323291,0.00018229455,0.00015460252,0.00010020995,0.075728394,0.040544346,0.013516268,0.041982736,0.8261298],"study_design_scores_gemma":[0.000049790724,0.0001334067,0.00046770537,0.00004932011,0.000059176,0.00013275475,0.00002541798,0.94994706,0.021096155,0.01664781,0.011361539,0.000029820392],"about_ca_topic_score_codex":0.0071594794,"about_ca_topic_score_gemma":0.015282138,"teacher_disagreement_score":0.01147475,"about_ca_system_score_codex":0.0006565749,"about_ca_system_score_gemma":0.0009315441,"threshold_uncertainty_score":0.03838688},"labels":[],"label_agreement":null},{"id":"W4402074987","doi":"10.1007/978-3-031-70352-2_3","title":"Direct-Effect Risk Minimization for Domain Generalization","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 Waterloo","funders":"","keywords":"Computer science; Generalization; Minification; Domain (mathematical analysis); Algorithm; Artificial intelligence; Mathematics; Programming language","score_opus":0.013472994446978713,"score_gpt":0.25342637749075975,"score_spread":0.23995338304378103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402074987","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":[0.0020211905,0.00060251035,0.9959681,0.00013682693,0.0000358572,0.000017048038,0.000049277474,0.00026492562,0.0009042669],"genre_scores_gemma":[0.27634415,0.0026924005,0.6864902,0.0005804331,0.00037776027,0.00047048635,0.0012079517,0.0010883034,0.030748304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928695,0.0002656422,0.000031391697,0.00020000541,0.00015771788,0.00005824709],"domain_scores_gemma":[0.99823964,0.0012013493,0.000053437358,0.0002650575,0.0001736717,0.00006687886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020525975,0.001332383,0.0022361288,0.0006442797,0.000420378,0.0009968275,0.0023529842,0.0021331566,0.0048389393],"category_scores_gemma":[0.0048025614,0.000728655,0.0013867451,0.0007553056,0.0012830066,0.0021540471,0.0030922869,0.00342067,0.0012720429],"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":[0.00017089443,0.00016319788,0.000409207,0.00042672362,0.00022026978,0.00011447253,0.00010346566,0.57364124,0.0057291375,0.0891049,0.015457508,0.314459],"study_design_scores_gemma":[0.000007680212,0.000033627402,0.00011466189,0.000018336614,0.000017511307,0.00003869918,0.000009664135,0.9515548,0.000934619,0.04584633,0.0014143394,0.000009743575],"about_ca_topic_score_codex":0.0033170984,"about_ca_topic_score_gemma":0.0029910624,"teacher_disagreement_score":0.0048389393,"about_ca_system_score_codex":0.0010332513,"about_ca_system_score_gemma":0.0009881557,"threshold_uncertainty_score":0.016187906},"labels":[],"label_agreement":null},{"id":"W4402351527","doi":"10.1109/ijcnn60899.2024.10651064","title":"Attentional Feature Fusion for Few-Shot Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Shot (pellet); Feature (linguistics); Artificial intelligence; Computer science; Fusion; Pattern recognition (psychology); Computer vision; Materials science","score_opus":0.030924389986827165,"score_gpt":0.2920590439144756,"score_spread":0.26113465392764845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402351527","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":[0.03397077,0.0010254841,0.9604641,0.0002464784,0.00007062026,0.00011088787,0.00023595535,0.002756762,0.0011189781],"genre_scores_gemma":[0.7970488,0.00041736182,0.19708087,0.000550314,0.0001714411,0.00023991716,0.0013560666,0.00021585965,0.002919491],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988789,0.00022769687,0.000059248465,0.0004626942,0.00024220182,0.00012917281],"domain_scores_gemma":[0.99823546,0.000806517,0.00014490313,0.00035192407,0.00034488138,0.00011632627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022230197,0.0013588202,0.0019668872,0.0020543553,0.00070662674,0.00095720595,0.0030919793,0.0016694695,0.0021529652],"category_scores_gemma":[0.005713292,0.00046499918,0.0013865022,0.001394334,0.001146581,0.0031061838,0.0020939838,0.002236663,0.0007691742],"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":[0.000434437,0.00057443284,0.0039115967,0.00028502708,0.00023193816,0.00021067669,0.0002635823,0.27781057,0.01803895,0.009547963,0.0075435117,0.6811474],"study_design_scores_gemma":[0.000009932956,0.00007130188,0.00055015495,0.000010792833,0.000021332506,0.00005187164,0.000017618537,0.98379356,0.0032976214,0.011427919,0.0007313023,0.00001656182],"about_ca_topic_score_codex":0.006459807,"about_ca_topic_score_gemma":0.0063256454,"teacher_disagreement_score":0.006459807,"about_ca_system_score_codex":0.0014745825,"about_ca_system_score_gemma":0.0009581512,"threshold_uncertainty_score":0.012844384},"labels":[],"label_agreement":null},{"id":"W4402352463","doi":"10.1109/ijcnn60899.2024.10651212","title":"Prototype Completion With Knowledge Distillation and Gate Recurrent Unit for Few-Shot Classification","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Chongqing Municipal Education Commission; Ministry of Natural Resources","keywords":"Distillation; Computer science; Shot (pellet); One shot; Unit (ring theory); Artificial intelligence; Engineering; Materials science; Mathematics; Chromatography; Mechanical engineering; Chemistry; Mathematics education","score_opus":0.11112555234627662,"score_gpt":0.33917948597227576,"score_spread":0.22805393362599913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402352463","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":[0.051812045,0.0011563458,0.93971866,0.00024567323,0.00015303156,0.00013703773,0.00026827236,0.004598466,0.0019104672],"genre_scores_gemma":[0.73582166,0.00045218488,0.2554315,0.0004185741,0.00010918746,0.00026437698,0.0014534232,0.00018737698,0.005861786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996123,0.000050114413,0.000020881871,0.00017716004,0.000084413325,0.00005521161],"domain_scores_gemma":[0.9994679,0.00016264635,0.000047342262,0.00014418794,0.00013147795,0.00004643269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061025255,0.0009366999,0.0012958057,0.00073500804,0.00045680118,0.0007043143,0.0028390693,0.0011987706,0.002900884],"category_scores_gemma":[0.0022705952,0.00039417428,0.0006764186,0.0008829437,0.00068856904,0.0019944867,0.0011771288,0.0016897346,0.0009453646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030134938,0.0002933846,0.0012434871,0.00020366782,0.00010405688,0.0002070338,0.00015804078,0.14608541,0.021149756,0.0077043604,0.0071780323,0.8153714],"study_design_scores_gemma":[0.0000104604405,0.00007818427,0.00019494476,0.00000645275,0.000016560145,0.000051254403,0.000015813106,0.98849946,0.006115902,0.0041363835,0.0008615581,0.000012950544],"about_ca_topic_score_codex":0.006954084,"about_ca_topic_score_gemma":0.00857119,"teacher_disagreement_score":0.006954084,"about_ca_system_score_codex":0.00079777226,"about_ca_system_score_gemma":0.00096852577,"threshold_uncertainty_score":0.013827205},"labels":[],"label_agreement":null},{"id":"W4402669930","doi":"10.18653/v1/2024.repl4nlp-1.4","title":"Learning from Others: Similarity-based Regularization for Mitigating Dataset Bias.","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Azrieli Foundation; Open Philanthropy Project","keywords":"Regularization (linguistics); Computer science; Similarity (geometry); Artificial intelligence; Machine learning; Pattern recognition (psychology)","score_opus":0.05805738761050586,"score_gpt":0.28918708587406916,"score_spread":0.2311296982635633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402669930","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":[0.045348138,0.0010251626,0.94925016,0.0006073146,0.00009152698,0.00012334167,0.00021306859,0.0021459546,0.0011953426],"genre_scores_gemma":[0.6096964,0.0004534109,0.38198093,0.0013873924,0.00029173965,0.00034546718,0.0016514247,0.0005424036,0.0036508362],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967307,0.001524291,0.00014952682,0.0008198307,0.0006012089,0.0001745149],"domain_scores_gemma":[0.9911032,0.0044728634,0.0008406614,0.0024608,0.00074263004,0.00037981305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059951814,0.0014293813,0.001496255,0.0015268392,0.00097148586,0.0013438066,0.0030987374,0.0026500206,0.0012615157],"category_scores_gemma":[0.019719787,0.0005706814,0.0013042067,0.0013167866,0.0021243456,0.0031030271,0.004323107,0.00370564,0.0007082182],"study_design_candidate":"bench_or_experimental","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":[0.00074055075,0.0007614876,0.012645876,0.0005753226,0.0007827969,0.00032496723,0.00090611563,0.32853538,0.027274987,0.028979955,0.018260073,0.58021253],"study_design_scores_gemma":[0.000028546441,0.000119360775,0.0007957047,0.00002559531,0.000044223165,0.00011113494,0.000044596203,0.97194314,0.004850235,0.020466033,0.0015463803,0.000025021574],"about_ca_topic_score_codex":0.0020624127,"about_ca_topic_score_gemma":0.0037286866,"teacher_disagreement_score":0.0059951814,"about_ca_system_score_codex":0.0010010154,"about_ca_system_score_gemma":0.0011439049,"threshold_uncertainty_score":0.031705916},"labels":[],"label_agreement":null},{"id":"W4402702975","doi":"10.1109/cvpr52733.2024.02270","title":"Overcoming Generic Knowledge Loss with Selective Parameter Update","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science","score_opus":0.01906503757162171,"score_gpt":0.2608357847861564,"score_spread":0.24177074721453468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402702975","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":[0.05227155,0.0011555711,0.9320368,0.00069552404,0.00015341032,0.00015006681,0.00043020843,0.009461883,0.0036450156],"genre_scores_gemma":[0.76417834,0.00046478622,0.22539687,0.0012990242,0.00020132416,0.0002759263,0.0016563775,0.0008046279,0.005722721],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998596,0.00038021948,0.000096861186,0.0004950553,0.00026597115,0.0001658256],"domain_scores_gemma":[0.9953075,0.0020739308,0.0002763957,0.0017365098,0.00044397623,0.00016157591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022091612,0.0020454046,0.0021016211,0.0010312118,0.0006913715,0.0015023336,0.0037619807,0.0026976229,0.0034827855],"category_scores_gemma":[0.0137451915,0.00087467604,0.0010875392,0.00096028566,0.0013606048,0.004306939,0.003639687,0.003385501,0.0017788225],"study_design_candidate":"bench_or_experimental","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":[0.00043518803,0.00035218545,0.0025913087,0.00031700332,0.0002036818,0.00031526285,0.000286848,0.3827482,0.010637793,0.009529193,0.015330647,0.5772527],"study_design_scores_gemma":[0.000032534183,0.000056190715,0.00027270298,0.000018151346,0.000025945166,0.00008861555,0.000035296027,0.9802855,0.0032112857,0.014692562,0.0012632661,0.000017947268],"about_ca_topic_score_codex":0.005487362,"about_ca_topic_score_gemma":0.008665223,"teacher_disagreement_score":0.005487362,"about_ca_system_score_codex":0.0010977864,"about_ca_system_score_gemma":0.0017316543,"threshold_uncertainty_score":0.011683285},"labels":[],"label_agreement":null},{"id":"W4402716033","doi":"10.1109/cvpr52733.2024.02706","title":"AETTA: Label-Free Accuracy Estimation for Test-Time Adaptation","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Adaptation (eye); Test (biology); Artificial intelligence","score_opus":0.031050314501115648,"score_gpt":0.29223958757541596,"score_spread":0.2611892730743003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402716033","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015109974,0.0010330048,0.963748,0.0002768056,0.00035928734,0.00018160019,0.0005645652,0.01694198,0.0017847497],"genre_scores_gemma":[0.34315234,0.0005800838,0.6392511,0.00086807273,0.00031861753,0.0006178209,0.0049970187,0.0030520542,0.007162803],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99605715,0.0010118078,0.00027729955,0.001275142,0.0010797252,0.0002990005],"domain_scores_gemma":[0.9884012,0.004237333,0.00078454375,0.0030046343,0.003155837,0.00041655917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053975,0.0026753652,0.0019902117,0.0020309659,0.0010711653,0.002220113,0.0044648307,0.0027296024,0.0039037757],"category_scores_gemma":[0.027860593,0.0008006105,0.0014268652,0.0012507436,0.0011890619,0.0035956998,0.0039148773,0.005116476,0.00394757],"study_design_candidate":"bench_or_experimental","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":[0.0007014354,0.00047999705,0.008374008,0.0004166387,0.0005050944,0.0001926921,0.00036596804,0.18297112,0.022980396,0.0068725236,0.032206323,0.7439338],"study_design_scores_gemma":[0.00004122563,0.00011530775,0.0016648355,0.00003730529,0.00004387412,0.00013080628,0.000053158918,0.97583085,0.011473428,0.006659276,0.0038962339,0.00005375031],"about_ca_topic_score_codex":0.0082618715,"about_ca_topic_score_gemma":0.009533938,"teacher_disagreement_score":0.0082618715,"about_ca_system_score_codex":0.0013566279,"about_ca_system_score_gemma":0.00220138,"threshold_uncertainty_score":0.028545022},"labels":[],"label_agreement":null},{"id":"W4402716232","doi":"10.1109/cvpr52733.2024.00724","title":"Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Class (philosophy); Training (meteorology); Artificial intelligence; Machine learning; Physics","score_opus":0.04356958574054237,"score_gpt":0.2866890175643665,"score_spread":0.24311943182382412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402716232","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047886156,0.000522227,0.94496083,0.00043976383,0.000060704228,0.0000594759,0.00017763778,0.002189188,0.0037039989],"genre_scores_gemma":[0.8329427,0.00036977488,0.1595374,0.00089782407,0.000082042396,0.0001535916,0.0007892183,0.0005426875,0.0046848464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999286,0.0002666867,0.00002520064,0.00019407293,0.00014113895,0.0000868839],"domain_scores_gemma":[0.99735945,0.0015423569,0.00014810044,0.00063027116,0.00019487702,0.00012493091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020245952,0.0010971621,0.0009148089,0.00044800394,0.0003680824,0.0009922978,0.0016454738,0.001057365,0.0025620828],"category_scores_gemma":[0.0067561865,0.00046149633,0.00067408325,0.00039027148,0.0012526446,0.0021301382,0.0023346706,0.0028314847,0.00079213176],"study_design_candidate":"bench_or_experimental","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":[0.00034209224,0.00018872535,0.002882702,0.00014867811,0.00013254081,0.0002381904,0.00026684688,0.7816965,0.018354584,0.027929667,0.0066366987,0.1611827],"study_design_scores_gemma":[0.000013173944,0.000029205412,0.00025156874,0.000014516071,0.000010103058,0.000050998424,0.0000123579675,0.9807283,0.0031518664,0.014978055,0.00075067324,0.000009059649],"about_ca_topic_score_codex":0.0020285,"about_ca_topic_score_gemma":0.0038114602,"teacher_disagreement_score":0.0025620828,"about_ca_system_score_codex":0.0006725073,"about_ca_system_score_gemma":0.00064203684,"threshold_uncertainty_score":0.0107072},"labels":[],"label_agreement":null},{"id":"W4402727081","doi":"10.1109/cvpr52733.2024.00581","title":"NC-TTT: A Noise Constrastive Approach for Test-Time Training","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Noise (video); Test (biology); Artificial intelligence","score_opus":0.03436123937450501,"score_gpt":0.2617218560681615,"score_spread":0.22736061669365648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727081","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020053418,0.00054447557,0.970637,0.00018723847,0.00012745999,0.00014647013,0.00030947782,0.005816508,0.002177871],"genre_scores_gemma":[0.4291165,0.00039211125,0.5531404,0.0008318453,0.00019404537,0.0007300244,0.004161476,0.001788664,0.009644979],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986255,0.00037256788,0.00007005866,0.00045352857,0.00034581465,0.00013241361],"domain_scores_gemma":[0.9966852,0.0014149325,0.0001975293,0.0009307361,0.0005530392,0.0002185047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020649552,0.0018658992,0.0014922459,0.00095729757,0.0006963677,0.0009819588,0.004183849,0.0022016587,0.0042283176],"category_scores_gemma":[0.009196036,0.0006471829,0.0012593067,0.0010329137,0.0011366805,0.002139394,0.0029081753,0.0036839515,0.0020807756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078270625,0.0004661702,0.003247338,0.00025586662,0.00021592145,0.00024117189,0.00017538425,0.24841413,0.027822912,0.0075061657,0.015165804,0.6957064],"study_design_scores_gemma":[0.000023127375,0.00016152342,0.00051427586,0.000018028562,0.000022554043,0.00011754889,0.000024960433,0.98246896,0.009122153,0.0051524783,0.0023542454,0.000020122483],"about_ca_topic_score_codex":0.005042889,"about_ca_topic_score_gemma":0.008375632,"teacher_disagreement_score":0.005042889,"about_ca_system_score_codex":0.0011183119,"about_ca_system_score_gemma":0.0017200207,"threshold_uncertainty_score":0.014145136},"labels":[],"label_agreement":null},{"id":"W4402727719","doi":"10.1109/cvpr52733.2024.02215","title":"Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; University of British Columbia","funders":"","keywords":"Shot (pellet); Computer science; Computer vision; Artificial intelligence; Segmentation; Scale (ratio); Image segmentation; One shot; Computer graphics (images); Engineering; Geography; Cartography; Materials science","score_opus":0.07399787078156803,"score_gpt":0.3490467313407939,"score_spread":0.27504886055922584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727719","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":[0.040370673,0.0006125884,0.94686806,0.0003359489,0.00009641905,0.00014080736,0.00025528253,0.009321446,0.001998662],"genre_scores_gemma":[0.6438556,0.0004135065,0.34692657,0.00061545166,0.00015043606,0.00020731051,0.0013231848,0.00085074396,0.0056573027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928087,0.00019034784,0.000029708894,0.0003071386,0.00011801193,0.00007399372],"domain_scores_gemma":[0.99782807,0.0010736437,0.00012625496,0.0005034887,0.00029308413,0.00017548418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015506351,0.0013731879,0.0011384446,0.00080470677,0.00043260714,0.0011334249,0.0024775583,0.001545818,0.0044631585],"category_scores_gemma":[0.0063546672,0.0005649937,0.00077674736,0.0006546748,0.0010140443,0.0034959172,0.0027881586,0.0026429095,0.001515194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090942055,0.00044957674,0.002367383,0.0003925957,0.00008919631,0.00031184213,0.0005326797,0.16599521,0.061568793,0.014716406,0.0067880405,0.74587893],"study_design_scores_gemma":[0.000032950273,0.00016974413,0.00043119496,0.000015555761,0.000021358292,0.00007734564,0.00007300904,0.96518373,0.012351027,0.019949628,0.0016747243,0.00001969315],"about_ca_topic_score_codex":0.0024684654,"about_ca_topic_score_gemma":0.003696404,"teacher_disagreement_score":0.0044631585,"about_ca_system_score_codex":0.00086834707,"about_ca_system_score_gemma":0.0010660829,"threshold_uncertainty_score":0.014930785},"labels":[],"label_agreement":null},{"id":"W4402727877","doi":"10.1109/cvpr52733.2024.00322","title":"ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Eclipse; Panopticon; Computer science; Segmentation; Artificial intelligence; Computer vision; Image segmentation; Astronomy","score_opus":0.009472611361995722,"score_gpt":0.2623042185625421,"score_spread":0.25283160720054637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727877","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":[0.044309694,0.0012133917,0.9088921,0.00038201894,0.00023629986,0.00017933646,0.0009755057,0.03941548,0.0043962],"genre_scores_gemma":[0.41737628,0.0005003272,0.5640634,0.00097505876,0.00010657897,0.00031957228,0.0058405893,0.0032540292,0.0075642215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933845,0.00009268424,0.000032452554,0.000336039,0.00010798098,0.00009246997],"domain_scores_gemma":[0.99910635,0.00030004233,0.000056822882,0.00033032015,0.0001218557,0.00008460725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010564906,0.0017690817,0.0013323443,0.00097526493,0.00057453424,0.0015237153,0.004089151,0.0022756865,0.005616395],"category_scores_gemma":[0.0034830784,0.00087124365,0.0012528874,0.0009830011,0.0010702886,0.0038510184,0.0036312633,0.0031671843,0.0023655982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007282212,0.0003924405,0.0022533224,0.0004911142,0.00021368368,0.00030505378,0.00029910845,0.23077874,0.027733836,0.011652063,0.031385947,0.69376653],"study_design_scores_gemma":[0.00004775085,0.00008119549,0.0002862522,0.00002503425,0.00001929121,0.00008730565,0.00004904068,0.97604054,0.007941368,0.01157305,0.0038274797,0.000021584661],"about_ca_topic_score_codex":0.004940008,"about_ca_topic_score_gemma":0.010663326,"teacher_disagreement_score":0.005616395,"about_ca_system_score_codex":0.0011290691,"about_ca_system_score_gemma":0.0011994262,"threshold_uncertainty_score":0.018788695},"labels":[],"label_agreement":null},{"id":"W4402833258","doi":"10.1109/nss/mic/rtsd57108.2024.10657664","title":"Anomaly Detection in Brain Tumor Imaging: Few-Shot Learning with Generative Models and Knowledge Transfer","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Generative grammar; Artificial intelligence; Neuroimaging; One shot; Anomaly detection; Transfer of learning; Generative model; Natural language processing; Neuroscience; Psychology; Engineering","score_opus":0.024730785055590898,"score_gpt":0.25893208065620693,"score_spread":0.23420129560061603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402833258","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":[0.041740842,0.0004251915,0.95587176,0.000452964,0.0000447115,0.000034371296,0.000050539882,0.0005566897,0.00082281214],"genre_scores_gemma":[0.90081674,0.0003168873,0.095867164,0.00036923296,0.00010250247,0.000059539543,0.00023960159,0.000105915526,0.002122357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953306,0.00015833249,0.000015485653,0.00014474294,0.0000988989,0.000049549406],"domain_scores_gemma":[0.9981483,0.0012624556,0.00017775189,0.00017520685,0.00014651986,0.00008974808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015917866,0.00070010504,0.0008291099,0.0007523732,0.0002578006,0.0007656523,0.001531719,0.0012218801,0.0007909845],"category_scores_gemma":[0.004773654,0.000503402,0.00080879853,0.0004186847,0.0011562416,0.0012259621,0.0013329745,0.0020299715,0.0002752101],"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":[0.0001257138,0.000096646305,0.0025580262,0.000075194395,0.00009715948,0.00017558462,0.00012020936,0.88358074,0.005479777,0.011275993,0.00148159,0.0949334],"study_design_scores_gemma":[0.0000016054439,0.000011611344,0.00014940013,0.0000026334385,0.0000033296105,0.000026141977,0.0000033283152,0.9948126,0.00058603834,0.004308179,0.00009114724,0.0000040496916],"about_ca_topic_score_codex":0.0031781194,"about_ca_topic_score_gemma":0.0028618292,"teacher_disagreement_score":0.0031781194,"about_ca_system_score_codex":0.0008631414,"about_ca_system_score_gemma":0.00054959,"threshold_uncertainty_score":0.008418262},"labels":[],"label_agreement":null},{"id":"W4402916274","doi":"10.1109/cvprw63382.2024.00271","title":"MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised Domain Adaptation in Semantic Segmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Prior probability; Segmentation; Domain adaptation; Artificial intelligence; Motion (physics); Adaptation (eye); Domain (mathematical analysis); Computer vision; Bayesian probability; Mathematics","score_opus":0.024167807843967475,"score_gpt":0.26476860656157797,"score_spread":0.2406007987176105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402916274","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":[0.018244868,0.00031556396,0.9781154,0.000101185644,0.00004468884,0.00006265025,0.00012730774,0.0017242695,0.0012641025],"genre_scores_gemma":[0.3317161,0.00048628432,0.66180325,0.0003478358,0.00010138095,0.00020988677,0.0013919969,0.00054111856,0.0034020976],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954575,0.00010485122,0.000017747028,0.00019099278,0.00008142119,0.00005920186],"domain_scores_gemma":[0.9994386,0.00020515847,0.00006176923,0.0001447595,0.000099067765,0.00005061899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087705324,0.0009762094,0.0007503565,0.0011785856,0.00045371955,0.0007286553,0.0012467591,0.000882672,0.0014943122],"category_scores_gemma":[0.0019598335,0.00039350113,0.000819276,0.0008690166,0.0007791014,0.001651835,0.001609741,0.0012650989,0.0008261588],"study_design_candidate":"bench_or_experimental","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":[0.0003377904,0.0002939516,0.002236712,0.00024128916,0.0001269061,0.00018219229,0.0003377173,0.20873535,0.09262685,0.015153645,0.0072552897,0.67247236],"study_design_scores_gemma":[0.000010933691,0.000054827124,0.0006253303,0.000011675862,0.000012769827,0.00006667985,0.00004131896,0.97611016,0.012393159,0.0075767287,0.0030819136,0.000014458523],"about_ca_topic_score_codex":0.0036834788,"about_ca_topic_score_gemma":0.0053049587,"teacher_disagreement_score":0.0036834788,"about_ca_system_score_codex":0.00049383746,"about_ca_system_score_gemma":0.0010121496,"threshold_uncertainty_score":0.0073240995},"labels":[],"label_agreement":null},{"id":"W4403067294","doi":"10.1007/978-3-031-72114-4_4","title":"A Task-Conditional Mixture-of-Experts Model for Missing Modality Segmentation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Roche (Canada)","funders":"","keywords":"Computer science; Modality (human–computer interaction); Task (project management); Artificial intelligence; Segmentation; Natural language processing","score_opus":0.030554325349422425,"score_gpt":0.2882219134609802,"score_spread":0.25766758811155777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403067294","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042489455,0.0008282908,0.9921673,0.00024604896,0.00010810004,0.000046321613,0.0002934719,0.0012793107,0.00078222237],"genre_scores_gemma":[0.31083784,0.0015982313,0.6621997,0.0010408879,0.00053623103,0.0005654494,0.003700319,0.0012934813,0.018227851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889463,0.00035215143,0.00005367121,0.00040351573,0.00013415149,0.00016185339],"domain_scores_gemma":[0.9979103,0.0013149728,0.00008738076,0.00022510481,0.0003429572,0.00011912639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037130427,0.001269775,0.0028802236,0.0011697175,0.00067318603,0.0017734886,0.005384708,0.004288377,0.005278126],"category_scores_gemma":[0.00607487,0.0015994878,0.0026228838,0.0018320986,0.0010466255,0.0024352067,0.002444583,0.0048749777,0.0036050621],"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":[0.0010463204,0.00025905104,0.0006175844,0.00035437162,0.0004089808,0.00018833534,0.0003078248,0.536215,0.008802104,0.023312772,0.014873042,0.41361448],"study_design_scores_gemma":[0.000012623122,0.000022414239,0.00010452612,0.000017002005,0.000030027484,0.00004039977,0.000008380647,0.9896337,0.00071438245,0.008662827,0.0007389755,0.000014776021],"about_ca_topic_score_codex":0.01507706,"about_ca_topic_score_gemma":0.02050618,"teacher_disagreement_score":0.01507706,"about_ca_system_score_codex":0.0013882278,"about_ca_system_score_gemma":0.0020163427,"threshold_uncertainty_score":0.029978573},"labels":[],"label_agreement":null},{"id":"W4403067538","doi":"10.1007/978-3-031-72114-4_65","title":"Unsupervised Domain Adaptation Using Soft-Labeled Contrastive Learning with Reversed Monte Carlo Method for Cardiac Image Segmentation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Domain adaptation; Monte Carlo method; Segmentation; Artificial intelligence; Adaptation (eye); Domain (mathematical analysis); Image segmentation; Unsupervised learning; Image (mathematics); Pattern recognition (psychology); Computer vision; Mathematics; Optics; Statistics","score_opus":0.02146121407947744,"score_gpt":0.2756469047573688,"score_spread":0.2541856906778914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403067538","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004340738,0.00017810363,0.9944378,0.000045653887,0.000018170847,0.000023741486,0.000031441712,0.0005422356,0.00038205946],"genre_scores_gemma":[0.1432272,0.0003066195,0.8529358,0.00020779348,0.00005646072,0.00013610753,0.00043710493,0.00043156897,0.002261332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955076,0.00014161284,0.000023239392,0.00013567608,0.00009504082,0.00005363102],"domain_scores_gemma":[0.9990507,0.000528342,0.00005574863,0.00014099298,0.0001726109,0.000051678137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013207066,0.0006977454,0.0012616033,0.00092301564,0.0004769367,0.001027132,0.0019290377,0.0015018936,0.0017993716],"category_scores_gemma":[0.002151376,0.00067629997,0.0013136399,0.00085510133,0.0008353883,0.0010301218,0.0017458575,0.0017833961,0.00079677964],"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":[0.0003569727,0.00021340263,0.0009852757,0.00021096268,0.00017935538,0.00013912136,0.00015780971,0.4764526,0.037414722,0.014871553,0.004516644,0.46450168],"study_design_scores_gemma":[0.0000040765085,0.000010593935,0.00008634517,0.000004444679,0.000007848479,0.000029614652,0.0000040052864,0.99464816,0.0024665324,0.002373263,0.00035791434,0.000007134538],"about_ca_topic_score_codex":0.0043408195,"about_ca_topic_score_gemma":0.006370273,"teacher_disagreement_score":0.0043408195,"about_ca_system_score_codex":0.000730996,"about_ca_system_score_gemma":0.0013339828,"threshold_uncertainty_score":0.00863111},"labels":[],"label_agreement":null},{"id":"W4403487119","doi":"10.3233/faia240840","title":"Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning","year":2024,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of British Columbia; Canadian Institute for Advanced Research","funders":"","keywords":"Reset (finance); Layer (electronics); Computer science; Business; Materials science; Nanotechnology","score_opus":0.0365648664025473,"score_gpt":0.27681991040646725,"score_spread":0.24025504400391995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403487119","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11233884,0.001742212,0.8665035,0.00045275057,0.0002860818,0.00007992383,0.0001424386,0.00624377,0.012210523],"genre_scores_gemma":[0.73356766,0.00060118496,0.25325358,0.00033541812,0.00008789019,0.00007495297,0.0003808125,0.00050626945,0.011192259],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997727,0.00004264146,0.000011150838,0.00009095491,0.00005361721,0.000028810175],"domain_scores_gemma":[0.99919194,0.00029603633,0.00005657738,0.00030683374,0.000090936504,0.000057771125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006521959,0.00075273355,0.0005775696,0.00035675938,0.00027499947,0.0009792476,0.001698352,0.00080868904,0.0046310322],"category_scores_gemma":[0.003296777,0.00032239317,0.00048352423,0.0004092527,0.0007386129,0.0028701322,0.0015163594,0.0019146584,0.0014119941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001521699,0.0001925489,0.0014783728,0.00016263087,0.000078107754,0.000119589284,0.00026688163,0.09616663,0.028689276,0.016068088,0.006166341,0.85045946],"study_design_scores_gemma":[0.000018359886,0.0002148568,0.0008428165,0.000049159993,0.00005051312,0.00019970746,0.00006116491,0.9432183,0.017683545,0.031392932,0.0062396443,0.00002910473],"about_ca_topic_score_codex":0.0014605834,"about_ca_topic_score_gemma":0.0018236049,"teacher_disagreement_score":0.0046310322,"about_ca_system_score_codex":0.00045776885,"about_ca_system_score_gemma":0.00040899235,"threshold_uncertainty_score":0.01549238},"labels":[],"label_agreement":null},{"id":"W4403518771","doi":"10.3233/faia240769","title":"Transfer Learning Can Introduce Bias","year":2024,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Ontario Tech University","funders":"","keywords":"Transfer of learning; Task (project management); Computer science; Multi-task learning; Artificial intelligence; Machine learning; Inductive transfer; Negative transfer; Knowledge transfer; Cognitive psychology; Knowledge management; Psychology; Robot learning; Engineering; Medicine","score_opus":0.05401077904522368,"score_gpt":0.26775770969097645,"score_spread":0.2137469306457528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403518771","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020927195,0.0074259285,0.92224765,0.004503963,0.0009513345,0.0002034584,0.00012418246,0.0015298212,0.04208643],"genre_scores_gemma":[0.57640857,0.009416082,0.34041628,0.0062097264,0.0013853415,0.0007312127,0.00047192862,0.0011881609,0.06377268],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972114,0.0011122806,0.00014848431,0.0006338182,0.000739701,0.00015439012],"domain_scores_gemma":[0.9841823,0.012203781,0.00041011657,0.0023888703,0.00067564705,0.00013919448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007052904,0.0012067471,0.0011398271,0.0006492956,0.00057868246,0.0026890798,0.002025363,0.0021086973,0.0073992657],"category_scores_gemma":[0.02528781,0.00063861476,0.0012326544,0.0007304348,0.0032512748,0.0052049933,0.0032821624,0.0040611364,0.002511706],"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":[0.00019222472,0.00014962727,0.002628452,0.0011676383,0.00033479967,0.00040533894,0.0010392789,0.0774852,0.008410203,0.36619857,0.015080822,0.52690786],"study_design_scores_gemma":[0.00005237868,0.00020559583,0.0014478045,0.00034538461,0.00013530783,0.0008100551,0.00014323885,0.20206487,0.011148487,0.7224476,0.06111836,0.00008097033],"about_ca_topic_score_codex":0.0009332471,"about_ca_topic_score_gemma":0.0009916864,"teacher_disagreement_score":0.0073992657,"about_ca_system_score_codex":0.0015424753,"about_ca_system_score_gemma":0.0011247032,"threshold_uncertainty_score":0.037299752},"labels":[],"label_agreement":null},{"id":"W4403827631","doi":"10.1016/j.neucom.2024.128755","title":"Interpretable few-shot learning with online attribute selection","year":2024,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Compute Canada","keywords":"Selection (genetic algorithm); Computer science; Artificial intelligence; Shot (pellet); Machine learning; Feature selection; Pattern recognition (psychology); One shot","score_opus":0.021226220234720206,"score_gpt":0.26416931991214637,"score_spread":0.24294309967742617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403827631","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":[0.014224076,0.0007428398,0.98350865,0.00027031076,0.000076998236,0.00004360215,0.00010137959,0.0005793607,0.00045271384],"genre_scores_gemma":[0.685902,0.0007511053,0.30587983,0.00066033285,0.00038843797,0.00024650927,0.0013242493,0.00023412741,0.0046134153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831676,0.00068322103,0.00009239049,0.0005249157,0.00025793086,0.00012482946],"domain_scores_gemma":[0.9955461,0.0031288774,0.00018190187,0.0005892067,0.00039219187,0.00016158965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025987998,0.0012842659,0.0026889096,0.0013905149,0.00082606985,0.0016126167,0.0031552897,0.002557267,0.0021901473],"category_scores_gemma":[0.009154156,0.0008050715,0.0013339956,0.0013466137,0.0012212918,0.0031276941,0.0026119691,0.003449797,0.00071998156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064310717,0.00081785064,0.0029336272,0.00046847606,0.0005386961,0.0003999334,0.0003510039,0.43342432,0.010804311,0.025932234,0.009919412,0.513767],"study_design_scores_gemma":[0.000009940713,0.000030564937,0.00020755279,0.000010497064,0.000016919015,0.00003930054,0.000014128685,0.9788826,0.00094662164,0.019557348,0.00027252577,0.000012064809],"about_ca_topic_score_codex":0.0026875136,"about_ca_topic_score_gemma":0.0032642004,"teacher_disagreement_score":0.0031552897,"about_ca_system_score_codex":0.00077689835,"about_ca_system_score_gemma":0.0009445498,"threshold_uncertainty_score":0.013743997},"labels":[],"label_agreement":null},{"id":"W4403887899","doi":"10.1007/978-3-031-72949-2_7","title":"Adapt Without Forgetting: Distill Proximity from Dual Teachers in Vision-Language Models","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Forgetting; Dual (grammatical number); Artificial intelligence; Human–computer interaction; Computer vision; Natural language processing; Computer graphics (images); Cognitive science; Cognitive psychology; Linguistics; Psychology","score_opus":0.019252993986826096,"score_gpt":0.2673125237435382,"score_spread":0.2480595297567121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403887899","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065316446,0.0014680141,0.9263698,0.00040832438,0.00019495853,0.000074102,0.0003446231,0.0034821471,0.0023416283],"genre_scores_gemma":[0.742251,0.00062657497,0.24519522,0.0005460202,0.00018203842,0.00013600363,0.0015581097,0.0005876771,0.008917452],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991179,0.00018018643,0.000030404313,0.0004427138,0.00010841069,0.00012040838],"domain_scores_gemma":[0.99875915,0.0006833407,0.0000661131,0.00022674563,0.00014560309,0.000119038654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010779266,0.0011972369,0.0015434758,0.0009029231,0.0005633571,0.0012635624,0.002578453,0.0022845382,0.002989102],"category_scores_gemma":[0.004306933,0.0008170553,0.0011270478,0.0010225544,0.000671179,0.0034470602,0.0032687539,0.0034533232,0.0016574741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010016833,0.0004371642,0.0026021674,0.00021343985,0.00019306495,0.00023736118,0.00045697752,0.1649893,0.011463991,0.006075841,0.0077968487,0.80453223],"study_design_scores_gemma":[0.000024862864,0.000107033484,0.00040516682,0.000019548708,0.000037803282,0.00006605578,0.00006126378,0.985113,0.0037225399,0.009553115,0.00087256724,0.000016964688],"about_ca_topic_score_codex":0.009232092,"about_ca_topic_score_gemma":0.013592068,"teacher_disagreement_score":0.009232092,"about_ca_system_score_codex":0.00071339105,"about_ca_system_score_gemma":0.0010633082,"threshold_uncertainty_score":0.01835674},"labels":[],"label_agreement":null},{"id":"W4403888028","doi":"10.1007/978-3-031-72907-2_26","title":"Preventing Catastrophic Forgetting Through Memory Networks in Continuous Detection","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of British Columbia","funders":"","keywords":"Forgetting; Computer science; Artificial intelligence; Cognitive psychology","score_opus":0.01371415293937284,"score_gpt":0.24015338899674418,"score_spread":0.22643923605737135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403888028","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05943124,0.0016989768,0.93530226,0.00028165107,0.00019724034,0.000040437317,0.00006379703,0.0016377933,0.0013466005],"genre_scores_gemma":[0.8756933,0.0006541676,0.11729147,0.00033475974,0.00018728063,0.000064107786,0.00014920766,0.00017102812,0.005454621],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950004,0.00008169,0.000027080869,0.00018977633,0.00010323383,0.0000982634],"domain_scores_gemma":[0.9953667,0.0031602243,0.00029797884,0.0005360617,0.00044146442,0.00019757957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012404952,0.0008986309,0.0015195415,0.00063322316,0.00046448573,0.00093952386,0.0024399466,0.0017799032,0.0019160003],"category_scores_gemma":[0.006830894,0.000589158,0.00046312844,0.00050300703,0.00095571514,0.0023904457,0.0019429374,0.0022244935,0.00052120734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007855153,0.0003146296,0.0019753256,0.00028836538,0.00016143317,0.0002545561,0.0001569259,0.3178259,0.018021675,0.012538568,0.0063774358,0.64129966],"study_design_scores_gemma":[0.00001052848,0.0000727793,0.00020988553,0.000012161923,0.000025467567,0.00008210226,0.000009881069,0.9866067,0.0049916296,0.0076735686,0.00029582324,0.000009467236],"about_ca_topic_score_codex":0.0030280466,"about_ca_topic_score_gemma":0.0036540749,"teacher_disagreement_score":0.0030280466,"about_ca_system_score_codex":0.00062917906,"about_ca_system_score_gemma":0.0007776094,"threshold_uncertainty_score":0.006560445},"labels":[],"label_agreement":null},{"id":"W4403998237","doi":"10.1007/978-3-031-72691-0_19","title":"Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of Toronto; Concordia University","funders":"","keywords":"Computer science; STREAMS; Adaptation (eye); Data stream mining; Test (biology); Data mining; Real-time computing; Operating system","score_opus":0.023398947009851134,"score_gpt":0.2651782063399808,"score_spread":0.24177925933012967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403998237","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":[0.0035576872,0.0002933858,0.9928878,0.000087186745,0.00007150492,0.000029125966,0.00013221386,0.0023207497,0.00062034803],"genre_scores_gemma":[0.3365031,0.00067099504,0.6459501,0.0004400495,0.00035911083,0.00037450372,0.00263753,0.0022157298,0.010848869],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988053,0.00030600242,0.00007780289,0.0004135211,0.0002613466,0.00013605361],"domain_scores_gemma":[0.9967648,0.0020134402,0.000118728916,0.0005461108,0.00040701107,0.00014999342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021925734,0.0012807205,0.001661235,0.0008488459,0.00052885327,0.0010132128,0.0023058106,0.0015641811,0.0056731296],"category_scores_gemma":[0.008006084,0.00078012806,0.00083168864,0.0014617027,0.0008090983,0.0023658876,0.0026334517,0.0029878062,0.003936073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005676935,0.00018482831,0.000787127,0.00016162313,0.000113443224,0.00015940856,0.00011541193,0.15901342,0.016183576,0.0089360215,0.010373102,0.8034042],"study_design_scores_gemma":[0.000018403112,0.00003676737,0.00034975802,0.000010185482,0.000014421178,0.00007027286,0.000019715773,0.9809779,0.0047874786,0.011969364,0.0017301552,0.000015546831],"about_ca_topic_score_codex":0.0051371804,"about_ca_topic_score_gemma":0.0060305977,"teacher_disagreement_score":0.0056731296,"about_ca_system_score_codex":0.00072676275,"about_ca_system_score_gemma":0.0012986771,"threshold_uncertainty_score":0.018978536},"labels":[],"label_agreement":null},{"id":"W4404094952","doi":"10.1016/j.patcog.2024.111139","title":"Forget to Learn (F2L): Circumventing plasticity–stability trade-off in continuous unsupervised domain adaptation","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"University of Toronto","funders":"","keywords":"Stability (learning theory); Adaptation (eye); Computer science; Plasticity; Domain (mathematical analysis); Artificial intelligence; Psychology; Machine learning; Mathematics; Neuroscience; Materials science","score_opus":0.042374325340961826,"score_gpt":0.25720100137256824,"score_spread":0.2148266760316064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404094952","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":[0.04258831,0.0005847792,0.95374423,0.00022571062,0.00007898043,0.000038063972,0.00005318778,0.0017175033,0.0009691935],"genre_scores_gemma":[0.7831028,0.0002692754,0.21192083,0.0004255924,0.00012393514,0.00015022892,0.00017632013,0.00047356644,0.003357442],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995859,0.00009968412,0.000023628985,0.00014146233,0.000090269576,0.000059030026],"domain_scores_gemma":[0.99744093,0.0015539036,0.00013217636,0.0005173349,0.00021407353,0.0001415821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015703292,0.0009053279,0.0011517873,0.0005002777,0.00041881786,0.00066156377,0.0021046065,0.0019055741,0.0018274871],"category_scores_gemma":[0.0062380247,0.00041697736,0.0005592854,0.000494205,0.0012776386,0.0019297161,0.0023192326,0.0019355686,0.00042900763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005734121,0.00039942793,0.002340453,0.00031829943,0.00020649549,0.00025182357,0.00022662662,0.29919276,0.048494343,0.019524392,0.005169004,0.623303],"study_design_scores_gemma":[0.000019103823,0.00010314073,0.00028773586,0.000008167577,0.000014832328,0.00006377356,0.000009982467,0.98447824,0.0054063736,0.009215134,0.00038124225,0.000012376315],"about_ca_topic_score_codex":0.0023315805,"about_ca_topic_score_gemma":0.0038065787,"teacher_disagreement_score":0.0023315805,"about_ca_system_score_codex":0.0004672993,"about_ca_system_score_gemma":0.0007818744,"threshold_uncertainty_score":0.008304775},"labels":[],"label_agreement":null},{"id":"W4404199495","doi":"10.1007/978-3-031-73010-8_5","title":"Local and Global Flatness for Federated Domain Generalization","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"Canadian Institute for Advanced Research; Carleton University","funders":"","keywords":"Flatness (cosmology); Computer science; Generalization; Domain (mathematical analysis); Theoretical computer science; Artificial intelligence; Mathematics; Mathematical analysis","score_opus":0.01593425425367483,"score_gpt":0.25953670311870664,"score_spread":0.2436024488650318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404199495","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":[0.025999727,0.0005147872,0.9684123,0.00023215273,0.00003820074,0.00005496365,0.00019717847,0.0013273619,0.0032233966],"genre_scores_gemma":[0.6197952,0.0007319556,0.36446998,0.00047776446,0.00014385484,0.00017734352,0.0015811854,0.0006338315,0.011988927],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986998,0.00031491855,0.00009433773,0.00048625687,0.00025766692,0.00014708114],"domain_scores_gemma":[0.9955166,0.001834263,0.00012123703,0.00195834,0.000368099,0.00020144093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002913954,0.0007283138,0.0014858957,0.0011140386,0.0007594592,0.0012897704,0.002179689,0.0012675612,0.004832748],"category_scores_gemma":[0.007598671,0.00062054355,0.0014492653,0.00096906343,0.0019590727,0.0050627813,0.005214404,0.0035672178,0.00081186707],"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":[0.0005649991,0.000255765,0.002259209,0.0003447084,0.00017546728,0.0003228393,0.0005876648,0.17916523,0.016731696,0.27464056,0.010940581,0.51401126],"study_design_scores_gemma":[0.000015839209,0.00007122382,0.00070979545,0.00003408269,0.000030728163,0.00013680471,0.00007744762,0.69444007,0.004287118,0.29807806,0.0020989068,0.000019936873],"about_ca_topic_score_codex":0.0044405228,"about_ca_topic_score_gemma":0.0044413554,"teacher_disagreement_score":0.004832748,"about_ca_system_score_codex":0.001222868,"about_ca_system_score_gemma":0.00094775896,"threshold_uncertainty_score":0.016167164},"labels":[],"label_agreement":null},{"id":"W4404212170","doi":"10.1016/j.mlwa.2024.100605","title":"A survey on knowledge distillation: Recent advancements","year":2024,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Seneca Polytechnic; Toronto Metropolitan University","funders":"","keywords":"Distillation; Data science; Biochemical engineering; Computer science; Chromatography; Chemistry; Engineering","score_opus":0.02519060871989153,"score_gpt":0.30461929710202534,"score_spread":0.2794286883821338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404212170","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":[0.0049835737,0.8778492,0.09896214,0.003019816,0.0008336218,0.00007893807,0.00036314907,0.00047212173,0.01343751],"genre_scores_gemma":[0.030497598,0.93262225,0.030464206,0.001194909,0.001287488,0.00009008491,0.0007487396,0.00016360395,0.002931135],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913245,0.00016929326,0.000105227096,0.00021013542,0.0003137519,0.00006929306],"domain_scores_gemma":[0.99687386,0.0022390613,0.0001394236,0.00025022376,0.00041251778,0.00008494782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018054078,0.0013096137,0.0014686106,0.0020570974,0.00055662025,0.0023998853,0.0018736316,0.0012860885,0.0064185956],"category_scores_gemma":[0.0065260315,0.00076187705,0.00089511997,0.0041994476,0.0011854282,0.006242658,0.0024082656,0.0026614,0.002148803],"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":[0.00011369443,0.00010957183,0.0009173707,0.009191626,0.000109216606,0.00010064758,0.00017248199,0.0110047385,0.0020854678,0.055252068,0.019495366,0.90144765],"study_design_scores_gemma":[0.000030368245,0.00040841036,0.0015101468,0.0062903976,0.0002193052,0.00092039065,0.00036003406,0.056404456,0.008755756,0.08688942,0.8380458,0.00016551658],"about_ca_topic_score_codex":0.001463397,"about_ca_topic_score_gemma":0.0014738082,"teacher_disagreement_score":0.0064185956,"about_ca_system_score_codex":0.0008364075,"about_ca_system_score_gemma":0.0017641758,"threshold_uncertainty_score":0.021472335},"labels":[],"label_agreement":null},{"id":"W4404592444","doi":"10.1016/j.neucom.2024.128944","title":"A semantic consistent object detection model for domain adaptation based on mixed-class distribution metrics","year":2024,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Ministry of Agriculture","funders":"Department of Agriculture of Guangdong Province","keywords":"Computer science; Class (philosophy); Artificial intelligence; Domain adaptation; Object (grammar); Adaptation (eye); Domain (mathematical analysis); Pattern recognition (psychology); Data mining; Mathematics; Classifier (UML)","score_opus":0.034102290832782094,"score_gpt":0.2605860811525112,"score_spread":0.2264837903197291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404592444","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":[0.00619884,0.00021147811,0.99260277,0.0001364416,0.000029066889,0.000034265235,0.00006342474,0.00046347955,0.0002602545],"genre_scores_gemma":[0.4689909,0.00055772293,0.5238376,0.0007131317,0.00018970411,0.00035244602,0.0011098904,0.00040338442,0.0038452342],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982052,0.00041552648,0.000099643206,0.0007607164,0.00038320213,0.00013585495],"domain_scores_gemma":[0.99819,0.00072190835,0.00013502558,0.00029335186,0.0005529653,0.00010668972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030079342,0.0012451842,0.002187348,0.0019038626,0.00070757425,0.0016682738,0.0037524856,0.0023854396,0.0014018741],"category_scores_gemma":[0.0057773204,0.0007036689,0.0015839904,0.0015616645,0.0011443268,0.0033609949,0.0028556718,0.0030725757,0.000705089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004331203,0.00048746858,0.0027719182,0.00021836816,0.00039599725,0.00022110643,0.00022762308,0.43246996,0.016219126,0.036661625,0.0073598158,0.50253385],"study_design_scores_gemma":[0.0000039270617,0.000015393129,0.00015461876,0.000004447008,0.00001322772,0.000021388674,0.000005085098,0.9920225,0.0007794013,0.006710423,0.0002622489,0.0000072449925],"about_ca_topic_score_codex":0.008396978,"about_ca_topic_score_gemma":0.008294199,"teacher_disagreement_score":0.008396978,"about_ca_system_score_codex":0.0014127917,"about_ca_system_score_gemma":0.0015357615,"threshold_uncertainty_score":0.016696215},"labels":[],"label_agreement":null},{"id":"W4404663485","doi":"10.1101/2024.11.24.625066","title":"Brain Feature Maps Reveal Progressive Animal-Feature Representations in the Ventral Stream","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Zoo","funders":"","keywords":"Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Computer science; Biology; Neuroscience; Communication; Psychology","score_opus":0.014364414275114697,"score_gpt":0.2558875734606244,"score_spread":0.2415231591855097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404663485","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9796468,0.000072717885,0.019092618,0.00007724975,0.000008015993,0.000005550992,0.0002821973,0.00021148766,0.00060325506],"genre_scores_gemma":[0.9936225,0.00003898874,0.005605588,0.0000123750715,0.000004848284,0.0000056830268,0.0002028401,0.00002628765,0.00048084534],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99994385,0.000006571661,0.0000022406336,0.00001675919,0.000015248286,0.00001525441],"domain_scores_gemma":[0.99976236,0.000079525606,0.0000404771,0.000034804543,0.00005066838,0.000032152646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000116626376,0.0001465412,0.0002573826,0.0005331498,0.0001295589,0.00045722985,0.00021015143,0.00025424923,0.0010005712],"category_scores_gemma":[0.00085766043,0.0001358116,0.00017578196,0.00043841655,0.0003042418,0.00044920688,0.000366235,0.00038789414,0.00016551337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025502162,0.000044560133,0.01764246,0.000058377453,0.00004950878,0.00021715333,0.00023443402,0.005867592,0.915552,0.0015418197,0.0005373069,0.057999756],"study_design_scores_gemma":[0.00002341078,0.00021017063,0.5862559,0.000021273861,0.00006245525,0.00078655744,0.0003369486,0.17653532,0.2237524,0.010063251,0.0019126639,0.000039610062],"about_ca_topic_score_codex":0.0012460557,"about_ca_topic_score_gemma":0.00144891,"teacher_disagreement_score":0.0012460557,"about_ca_system_score_codex":0.00021821314,"about_ca_system_score_gemma":0.00016351521,"threshold_uncertainty_score":0.0033472776},"labels":[],"label_agreement":null},{"id":"W4404673562","doi":"10.1007/978-3-031-73024-5_4","title":"Modality Translation for Object Detection Adaptation Without Forgetting Prior Knowledge","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Forgetting; Modality (human–computer interaction); Adaptation (eye); Translation (biology); Artificial intelligence; Object (grammar); Computer vision; Natural language processing; Cognitive psychology; Neuroscience; Psychology","score_opus":0.03939968520406524,"score_gpt":0.289039440942685,"score_spread":0.2496397557386198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404673562","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":[0.0046317205,0.0006351242,0.9887311,0.0000796042,0.00019618082,0.000045561304,0.00017269282,0.002920177,0.002587891],"genre_scores_gemma":[0.2193916,0.0017430285,0.75077814,0.00074677943,0.0003811678,0.00030179726,0.0019812866,0.0012859986,0.023390163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999508,0.000089512796,0.00002115981,0.00022084553,0.00010829552,0.0000520612],"domain_scores_gemma":[0.9994516,0.00019803617,0.000017399989,0.0001935044,0.000110171604,0.000029252878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081650267,0.0010316082,0.0010789894,0.0006896309,0.00036745274,0.0007536505,0.0015528869,0.0013623436,0.009123525],"category_scores_gemma":[0.0017420723,0.0004130073,0.0013105477,0.00084281457,0.0005593062,0.0016837087,0.0018137288,0.0019609334,0.005821993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028146582,0.00017666657,0.00026713277,0.00023657602,0.00013491746,0.00016934081,0.000092523274,0.017652791,0.075285956,0.00639309,0.011047838,0.88826174],"study_design_scores_gemma":[0.000038183305,0.00023324153,0.0015495411,0.0000576105,0.00018554664,0.0008773965,0.00007445923,0.82830036,0.09992912,0.04741041,0.021267312,0.00007673327],"about_ca_topic_score_codex":0.001411709,"about_ca_topic_score_gemma":0.0018788403,"teacher_disagreement_score":0.009123525,"about_ca_system_score_codex":0.00031643055,"about_ca_system_score_gemma":0.0004291334,"threshold_uncertainty_score":0.030521274},"labels":[],"label_agreement":null},{"id":"W4404822922","doi":"10.1007/978-3-031-77915-2_9","title":"TRAPL: Transformer-Based Patch Learning for Enhancing Semantic Representations Using Aggregated Features to Estimate Patch-Class Distribution","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Transformer; Class (philosophy); Artificial intelligence; Natural language processing; Electrical engineering; Engineering; Voltage","score_opus":0.02167470190889631,"score_gpt":0.3072406585293771,"score_spread":0.2855659566204808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404822922","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":[0.005871331,0.0003927403,0.98268837,0.00008308402,0.00011243895,0.000069593036,0.0005719926,0.009361499,0.00084894785],"genre_scores_gemma":[0.12972258,0.0008055507,0.8516264,0.000449009,0.00019275556,0.0002506334,0.005552824,0.0020591833,0.009341131],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943596,0.00008180949,0.000020021605,0.00022264736,0.00017870235,0.00006086457],"domain_scores_gemma":[0.99941874,0.00016606292,0.00003462512,0.00018840504,0.00014150976,0.00005060915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000945197,0.0013572449,0.001794704,0.0012619596,0.00036831424,0.0011652141,0.0025112699,0.0014370873,0.0079756975],"category_scores_gemma":[0.0019448394,0.0005632618,0.0012263795,0.0015885956,0.0006531379,0.0025679793,0.0025425437,0.0022928198,0.004775646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003400991,0.00019197685,0.00037094785,0.00020622663,0.00012493198,0.000109825385,0.00006347501,0.024210585,0.03667871,0.0043611117,0.028910091,0.90443194],"study_design_scores_gemma":[0.000041150433,0.00014208881,0.0005467988,0.000018348917,0.000058385023,0.00017606639,0.00003165632,0.9564882,0.022779595,0.012208324,0.007480168,0.000029240813],"about_ca_topic_score_codex":0.0048130085,"about_ca_topic_score_gemma":0.006423992,"teacher_disagreement_score":0.0079756975,"about_ca_system_score_codex":0.00063059194,"about_ca_system_score_gemma":0.0006635572,"threshold_uncertainty_score":0.026681364},"labels":[],"label_agreement":null},{"id":"W4404884731","doi":"10.1007/978-3-031-78128-5_13","title":"Label-Expanded Feature Debiasing for Single Domain Generalization","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 Calgary","funders":"","keywords":"Debiasing; Computer science; Generalization; Feature (linguistics); Domain (mathematical analysis); Artificial intelligence; Pattern recognition (psychology); Mathematics; Cognitive science","score_opus":0.028908520223616504,"score_gpt":0.26736242401646265,"score_spread":0.23845390379284614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404884731","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":[0.012326049,0.0006018895,0.98138946,0.00018612594,0.00012029026,0.00005742245,0.00019788454,0.0034307975,0.001690093],"genre_scores_gemma":[0.3434995,0.0006149193,0.6382054,0.000608609,0.0001952234,0.0001824553,0.0021249827,0.0009901912,0.013578712],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999303,0.00013173839,0.000039701175,0.00028015344,0.00015174017,0.00009370264],"domain_scores_gemma":[0.9985012,0.00048980775,0.000052623425,0.00068097987,0.00021426594,0.00006121173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015116703,0.0008873275,0.001627618,0.0008509719,0.0006961381,0.00068470166,0.0022995828,0.0016247026,0.0065658786],"category_scores_gemma":[0.0032868674,0.00042289327,0.0011401061,0.000983424,0.00088146585,0.0022841806,0.0027557178,0.002984273,0.002274062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003200295,0.00015747109,0.0004460162,0.00014562793,0.0000829789,0.00011895885,0.000109896784,0.060222194,0.021713987,0.012783551,0.015407514,0.8884917],"study_design_scores_gemma":[0.000020493604,0.00006432614,0.00038673944,0.000020878702,0.000031705204,0.00011382964,0.000038589926,0.95591736,0.010983486,0.028562328,0.0038398604,0.00002035432],"about_ca_topic_score_codex":0.0040341443,"about_ca_topic_score_gemma":0.0058699055,"teacher_disagreement_score":0.0065658786,"about_ca_system_score_codex":0.0006025433,"about_ca_system_score_gemma":0.0007709897,"threshold_uncertainty_score":0.021965027},"labels":[],"label_agreement":null},{"id":"W4404889959","doi":"10.1007/978-3-031-78347-0_10","title":"Task Consistent Prototype Learning for Incremental Few-Shot Semantic Segmentation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Computer science; Shot (pellet); Task (project management); Segmentation; Artificial intelligence; Natural language processing","score_opus":0.030534175458269763,"score_gpt":0.2784786636380164,"score_spread":0.24794448817974662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404889959","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":[0.018465558,0.0011903105,0.9723709,0.00016314798,0.00014694393,0.00015708203,0.00048139857,0.005372774,0.0016518743],"genre_scores_gemma":[0.33424369,0.00083297736,0.64925677,0.00046328537,0.00018200935,0.0003900594,0.0055654934,0.0011953587,0.007870291],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988986,0.0002058325,0.000063819905,0.00052686234,0.0001765251,0.0001283929],"domain_scores_gemma":[0.9979844,0.0010706378,0.000068475805,0.00050179573,0.00026845973,0.000106137646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001654243,0.0015089163,0.00299791,0.0012663793,0.0007344983,0.0016106082,0.0050521526,0.0027495604,0.0063158846],"category_scores_gemma":[0.004105632,0.0011623175,0.0015650376,0.0016913725,0.00088339974,0.0035454351,0.002768737,0.0029487312,0.0028062821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061807607,0.00038946694,0.00048508463,0.00043519153,0.00020899215,0.00018217078,0.0001912981,0.062306628,0.028040932,0.005765131,0.014809577,0.88656735],"study_design_scores_gemma":[0.00002865459,0.00011227245,0.00038326986,0.000024798243,0.000049237868,0.00014255646,0.00006884257,0.9686923,0.009506321,0.018980546,0.0019870852,0.000024185565],"about_ca_topic_score_codex":0.0054465905,"about_ca_topic_score_gemma":0.009130627,"teacher_disagreement_score":0.0063158846,"about_ca_system_score_codex":0.0009876809,"about_ca_system_score_gemma":0.0014679129,"threshold_uncertainty_score":0.021128774},"labels":[],"label_agreement":null},{"id":"W4405209164","doi":"10.1007/978-3-031-78189-6_14","title":"Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Forgetting; Facial expression; Facial expression recognition; Artificial intelligence; Speech recognition; Expression (computer science); Facial recognition system; Emotion recognition; Pattern recognition (psychology); Cognitive psychology; Psychology; Programming language","score_opus":0.03571808751926522,"score_gpt":0.24465650354762858,"score_spread":0.20893841602836336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405209164","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":[0.05184837,0.0010741857,0.94321024,0.00019176104,0.00020642369,0.000051968134,0.0001483988,0.0023038213,0.0009648316],"genre_scores_gemma":[0.7512845,0.0011858138,0.2381349,0.0005755278,0.00018252998,0.00013432706,0.0010133212,0.00043410706,0.007054938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939966,0.00011049954,0.000037632846,0.00017152591,0.00017843972,0.00010214325],"domain_scores_gemma":[0.9981674,0.0010175981,0.00010924524,0.00032533924,0.00030874147,0.000071639995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011543428,0.0012401774,0.001579491,0.00036500426,0.0003334542,0.00070029654,0.0016541714,0.0010131598,0.0022660897],"category_scores_gemma":[0.003810307,0.00054007745,0.0008643772,0.0004626768,0.0004153811,0.0014982817,0.0014846991,0.002218524,0.0010820837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000654781,0.00033883302,0.0016532793,0.00019956879,0.00020751794,0.00031612362,0.00015044502,0.11136519,0.058296,0.0021799325,0.008045918,0.8165924],"study_design_scores_gemma":[0.000009855628,0.00010773544,0.00072979217,0.000009714423,0.0000496287,0.00016112273,0.000024771283,0.97986174,0.01600292,0.0023748986,0.0006536271,0.000014202679],"about_ca_topic_score_codex":0.0036493526,"about_ca_topic_score_gemma":0.006033637,"teacher_disagreement_score":0.0036493526,"about_ca_system_score_codex":0.00043369827,"about_ca_system_score_gemma":0.000708938,"threshold_uncertainty_score":0.0075808167},"labels":[],"label_agreement":null},{"id":"W4405632792","doi":"10.1126/sciadv.adp6040","title":"Domain adaptation in small-scale and heterogeneous biological datasets","year":2024,"lang":"en","type":"review","venue":"Science Advances","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Adaptation (eye); Computer science; Domain (mathematical analysis); Domain adaptation; Context (archaeology); Scale (ratio); Data science; Strengths and weaknesses; Key (lock); Transfer of learning; Artificial intelligence; Machine learning; Biology; Cartography; Geography","score_opus":0.07884730637706774,"score_gpt":0.35540091661467876,"score_spread":0.27655361023761105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405632792","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":[0.009225873,0.5019472,0.47159863,0.0038735126,0.0009096273,0.00024082218,0.0005625815,0.00093808136,0.010703639],"genre_scores_gemma":[0.10986282,0.6011518,0.27395138,0.0032410133,0.0011981906,0.00069271534,0.0031868462,0.00031792704,0.0063974024],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99894994,0.0003686838,0.00007836496,0.0002858058,0.00027338078,0.00004386378],"domain_scores_gemma":[0.9967355,0.0024110589,0.00013586789,0.00028582432,0.00036729843,0.00006453278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004011762,0.0008158759,0.0011481603,0.0016838915,0.00029680427,0.0014523338,0.001837479,0.0011265132,0.00127334],"category_scores_gemma":[0.007935051,0.00029948613,0.0013007146,0.002089133,0.00093719404,0.0021358968,0.0015783507,0.0019939542,0.0010289056],"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":[0.000050034523,0.00007628604,0.0014731336,0.0040429467,0.0002948566,0.00012214377,0.00014837681,0.024661418,0.0030283087,0.022293061,0.013141917,0.9306675],"study_design_scores_gemma":[0.00006441883,0.00029724807,0.007430738,0.0047818855,0.0005029363,0.0024434666,0.0005816431,0.15894021,0.013687205,0.21833454,0.5927099,0.00022587329],"about_ca_topic_score_codex":0.0017006415,"about_ca_topic_score_gemma":0.0015406646,"teacher_disagreement_score":0.004011762,"about_ca_system_score_codex":0.00068365247,"about_ca_system_score_gemma":0.0012654647,"threshold_uncertainty_score":0.021216512},"labels":[],"label_agreement":null},{"id":"W4406064005","doi":"10.1016/j.ipm.2024.104058","title":"A semantic framework for enhancing pseudo-relevance feedback with soft negative sampling and contrastive learning","year":2025,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"York University; Western University","funders":"Natural Science Foundation of Hubei Province; National Natural Science Foundation of China; Hubei Provincial Department of Education","keywords":"Relevance (law); Computer science; Sampling (signal processing); Natural language processing; Psychology; Artificial intelligence; Linguistics; Political science; Philosophy; Computer vision","score_opus":0.01007160982246063,"score_gpt":0.2650852752459006,"score_spread":0.25501366542343995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406064005","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00780751,0.00045299248,0.9900129,0.00018581584,0.00004396931,0.000092995986,0.000039214112,0.00046249625,0.00090206694],"genre_scores_gemma":[0.51739275,0.0007098721,0.4755385,0.0009079593,0.00039763976,0.0005924335,0.00035155463,0.0002511662,0.0038581055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99683505,0.0012619254,0.00016209448,0.0006086099,0.000974288,0.00015804477],"domain_scores_gemma":[0.99575716,0.0023345768,0.00033312148,0.00047831627,0.0009427979,0.00015411558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044020307,0.0014819158,0.0015744241,0.0018647132,0.0006183576,0.0013836945,0.0028088347,0.0019689868,0.0019162166],"category_scores_gemma":[0.014884603,0.0004850941,0.0010070604,0.0013497324,0.0020781376,0.00406354,0.0022692862,0.0019346606,0.00084801414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066842523,0.000746136,0.002450791,0.00054478226,0.00015069422,0.00040080372,0.0006525857,0.26016402,0.035631023,0.08589139,0.006482641,0.60621667],"study_design_scores_gemma":[0.00004206631,0.00017451422,0.00021599873,0.000020077492,0.000024296869,0.000117054085,0.000022156628,0.96844155,0.003313478,0.0259334,0.0016681773,0.000027168211],"about_ca_topic_score_codex":0.002380281,"about_ca_topic_score_gemma":0.0023931956,"teacher_disagreement_score":0.0044020307,"about_ca_system_score_codex":0.0013397621,"about_ca_system_score_gemma":0.0014929418,"threshold_uncertainty_score":0.023280442},"labels":[],"label_agreement":null},{"id":"W4406613830","doi":"10.1109/smc54092.2024.10831103","title":"Sim-to-Real Domain Adaptation for Deformation Classification","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Domain adaptation; Adaptation (eye); Deformation (meteorology); Computer science; Domain (mathematical analysis); Artificial intelligence; Pattern recognition (psychology); Mathematics; Geology; Physics; Mathematical analysis; Optics","score_opus":0.04901453807375024,"score_gpt":0.3007437758793745,"score_spread":0.2517292378056243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406613830","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":[0.041334804,0.00017098084,0.94798154,0.00015298484,0.00013695292,0.00014268869,0.0003090688,0.007772383,0.0019985845],"genre_scores_gemma":[0.52533287,0.00017274976,0.4656907,0.00046586822,0.0000847214,0.00031469026,0.0029626412,0.0007905663,0.004185158],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999313,0.0001454187,0.000025618014,0.00027460314,0.00016223371,0.00007907273],"domain_scores_gemma":[0.99889874,0.00025312693,0.00008398463,0.0004632531,0.00022096407,0.000079835474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001620413,0.001017311,0.00070658466,0.00072499295,0.00044061954,0.0007455627,0.0020774058,0.0010600449,0.003757248],"category_scores_gemma":[0.0033359113,0.00039287552,0.0007859175,0.0006312329,0.0009034438,0.0013736986,0.0019197663,0.0018095821,0.0018487595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004030017,0.00069469644,0.0047194753,0.00013714346,0.00013115442,0.00019136565,0.00015503663,0.45937976,0.033608004,0.0054403963,0.014903711,0.4802363],"study_design_scores_gemma":[0.0000073012334,0.00003746081,0.0005325748,0.000004237462,0.000004443351,0.0000659259,0.000025912685,0.9872398,0.006885644,0.0033484679,0.0018384653,0.000009815276],"about_ca_topic_score_codex":0.0029253755,"about_ca_topic_score_gemma":0.0037906254,"teacher_disagreement_score":0.003757248,"about_ca_system_score_codex":0.0007380265,"about_ca_system_score_gemma":0.00073998293,"threshold_uncertainty_score":0.012569189},"labels":[],"label_agreement":null},{"id":"W4406840514","doi":"10.48550/arxiv.2501.14048","title":"SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Oak Ridge Institute for Science and Education; Workforce Development for Teachers and Scientists; University of Toronto; FAS Division of Science, Harvard University; High Energy Physics; U.S. Department of Energy; Office of Science; Harvard University; National Science Foundation","keywords":"Equivariant map; Domain adaptation; Adaptation (eye); Domain (mathematical analysis); Computer science; Image (mathematics); Artificial neural network; Artificial intelligence; Mathematics; Pure mathematics; Psychology; Neuroscience; Mathematical analysis","score_opus":0.051518553395654254,"score_gpt":0.29049143361949015,"score_spread":0.2389728802238359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406840514","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015208898,0.00016044673,0.9821852,0.00010544776,0.00004738904,0.000033112425,0.000051014453,0.0013073713,0.0009010196],"genre_scores_gemma":[0.44707385,0.00020315369,0.5468288,0.00039104858,0.00005507109,0.00020587405,0.0006698509,0.0004212653,0.004150994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958044,0.00013479176,0.000025113235,0.00012408767,0.00009671167,0.00003872529],"domain_scores_gemma":[0.9992423,0.00029395332,0.000077067125,0.00017410346,0.00015483946,0.0000577758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010862966,0.00092603365,0.00076718273,0.0005665303,0.0004151232,0.00076079456,0.0016781266,0.0009833823,0.0020634749],"category_scores_gemma":[0.003286908,0.0004042404,0.00092230004,0.000505545,0.00079408084,0.0010810049,0.0016109938,0.0024693378,0.00088520197],"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":[0.00015027223,0.00010350273,0.0020806335,0.00009126909,0.00010481791,0.00011600205,0.00015171406,0.6782467,0.011665872,0.014735538,0.0043032942,0.28825033],"study_design_scores_gemma":[0.000002991745,0.00001347789,0.00006378145,0.0000025059312,0.0000019385436,0.000011149081,0.0000068399486,0.99478984,0.001162772,0.0033935285,0.00054662063,0.000004475945],"about_ca_topic_score_codex":0.0026903872,"about_ca_topic_score_gemma":0.0039931866,"teacher_disagreement_score":0.0026903872,"about_ca_system_score_codex":0.0008036803,"about_ca_system_score_gemma":0.00079803687,"threshold_uncertainty_score":0.0069030523},"labels":[],"label_agreement":null},{"id":"W4407173320","doi":"10.1101/2025.01.31.635690","title":"NGSTroubleFinder: A tool for detection and quantification of contamination and kinship across human NGS data","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Centre","funders":"","keywords":"Contamination; Kinship; Geography; Biology; Sociology; Anthropology; Ecology","score_opus":0.05001443210657115,"score_gpt":0.2924693571364162,"score_spread":0.24245492502984506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407173320","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01572864,0.0007623643,0.69628656,0.000871447,0.00046497048,0.0004347918,0.086632974,0.19208531,0.006732858],"genre_scores_gemma":[0.090316765,0.0004990039,0.7371665,0.0015232918,0.00018580211,0.0021736696,0.096818745,0.060537897,0.01077829],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946861,0.0010395265,0.00046451413,0.0016272147,0.0018463366,0.0003363274],"domain_scores_gemma":[0.9865143,0.0078855185,0.0013483193,0.0027367743,0.0009962955,0.0005187888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008580937,0.0017717058,0.0018712237,0.005622643,0.0028786326,0.0040405425,0.0033437905,0.0015701125,0.04215105],"category_scores_gemma":[0.032848027,0.0019751969,0.0018518418,0.004225913,0.0017013743,0.0027654357,0.007465288,0.0034425193,0.020014143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094606745,0.00013381764,0.026531182,0.004014181,0.0008354994,0.0013844884,0.0017873085,0.01832129,0.025068985,0.019375822,0.608176,0.29342547],"study_design_scores_gemma":[0.0004514653,0.00013497101,0.023522712,0.0012888701,0.00040883693,0.0031698418,0.0006235761,0.182182,0.089115635,0.12480782,0.5737046,0.00058968825],"about_ca_topic_score_codex":0.003876276,"about_ca_topic_score_gemma":0.0065173167,"teacher_disagreement_score":0.04215105,"about_ca_system_score_codex":0.001192196,"about_ca_system_score_gemma":0.0044269343,"threshold_uncertainty_score":0.14100933},"labels":[],"label_agreement":null},{"id":"W4407475891","doi":"10.1109/dicta63115.2024.00107","title":"Attention Based Simple Primitives for Open-World Compositional Zero-Shot Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Simple (philosophy); Computer science; Zero (linguistics); Shot (pellet); Artificial intelligence; Materials science","score_opus":0.05881952603098508,"score_gpt":0.3351968587941862,"score_spread":0.27637733276320114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407475891","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":[0.033789072,0.0004619797,0.9582948,0.00030227916,0.00007862145,0.00017278956,0.00035482427,0.004010151,0.0025355185],"genre_scores_gemma":[0.67063755,0.00033885712,0.3188521,0.0005846462,0.00008553894,0.00038342288,0.0024299826,0.00043069368,0.00625727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923825,0.00020184876,0.0000373205,0.00026086342,0.0001671141,0.0000946751],"domain_scores_gemma":[0.99837685,0.00087548513,0.000078248326,0.0003770868,0.00017735161,0.000115046554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016599497,0.0012248853,0.0013509903,0.0009417394,0.0006349186,0.0012128981,0.00466741,0.0018556463,0.005508853],"category_scores_gemma":[0.0053869793,0.0005953171,0.001229098,0.0008990492,0.0017211868,0.0040529324,0.0034812782,0.0033416487,0.0014887382],"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":[0.0007718497,0.00054171047,0.00217187,0.00045394636,0.0001437095,0.0002842172,0.0003298528,0.49262264,0.018459707,0.046275448,0.008825867,0.42911917],"study_design_scores_gemma":[0.000012460463,0.000053940636,0.00011713021,0.000010794322,0.000008145604,0.000031052048,0.000018356299,0.97590655,0.0023387189,0.020756021,0.00073935825,0.000007422091],"about_ca_topic_score_codex":0.0049327887,"about_ca_topic_score_gemma":0.0073686326,"teacher_disagreement_score":0.005508853,"about_ca_system_score_codex":0.0014273198,"about_ca_system_score_gemma":0.0012818993,"threshold_uncertainty_score":0.018428922},"labels":[],"label_agreement":null},{"id":"W4408597313","doi":"10.2139/ssrn.5186050","title":"Lifelong Learning Using a Dynamically Growing Tree of Sub-Networks for Domain Generalization in Video Object Segmentation","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Generalization; Segmentation; Object (grammar); Computer science; Tree (set theory); Domain (mathematical analysis); Artificial intelligence; Lifelong learning; Computer vision; Machine learning; Pattern recognition (psychology); Mathematics; Psychology","score_opus":0.011659947535212462,"score_gpt":0.26932455947198913,"score_spread":0.2576646119367767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408597313","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":[0.058132418,0.0006374958,0.93928206,0.00023872397,0.00004502462,0.000059984442,0.00009827663,0.0008045554,0.000701333],"genre_scores_gemma":[0.6834639,0.0005010046,0.3117129,0.0002983791,0.00009540468,0.00016803725,0.0007119879,0.00025226208,0.002796115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954337,0.00013092493,0.00002735707,0.00016486156,0.00007220769,0.000061172796],"domain_scores_gemma":[0.9978422,0.001310472,0.00013004994,0.00024616407,0.00031990622,0.00015126728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014594593,0.0006816039,0.001321768,0.0012497177,0.0006963826,0.0007669061,0.001905243,0.0022579525,0.0015552442],"category_scores_gemma":[0.004192613,0.0006612376,0.0010433756,0.0011007525,0.00077553175,0.0023807872,0.001879904,0.002236481,0.000556811],"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":[0.00025432193,0.00028158753,0.0031543572,0.00014549978,0.00012960953,0.00016168578,0.00029059147,0.58611125,0.014050703,0.011974705,0.004216951,0.37922877],"study_design_scores_gemma":[0.000002294612,0.000015869706,0.00010507944,0.0000034894715,0.0000046598734,0.000010852193,0.000006665482,0.9961778,0.00048715807,0.0030708772,0.000112958165,0.000002281293],"about_ca_topic_score_codex":0.0061536566,"about_ca_topic_score_gemma":0.009301538,"teacher_disagreement_score":0.0061536566,"about_ca_system_score_codex":0.0009241487,"about_ca_system_score_gemma":0.0008980228,"threshold_uncertainty_score":0.0122356415},"labels":[],"label_agreement":null},{"id":"W4408792032","doi":"10.1109/tgrs.2025.3553094","title":"DDCI: Unsupervised Domain Adaptation for Remote Sensing Images Based on Diffusion Causal Distillation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of Ottawa","funders":"Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Domain adaptation; Remote sensing; Adaptation (eye); Domain (mathematical analysis); Distillation; Diffusion; Artificial intelligence; Pattern recognition (psychology); Data mining; Geology; Mathematics; Optics","score_opus":0.01787171581595275,"score_gpt":0.2585296765652897,"score_spread":0.24065796074933693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408792032","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":[0.025266692,0.0005112403,0.96903795,0.00031689153,0.0000937974,0.00007361898,0.00022701321,0.0030469454,0.0014259011],"genre_scores_gemma":[0.6342376,0.0006363777,0.35697994,0.0006583234,0.00016023093,0.0002773061,0.0020006907,0.0005308785,0.0045186356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.999647,0.00007524552,0.000013970529,0.00013428082,0.00007927943,0.00005020216],"domain_scores_gemma":[0.9993717,0.00024937617,0.00007745571,0.00014222611,0.00010996546,0.000049340073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008323269,0.0010195358,0.00076468755,0.00078845257,0.00039666047,0.0008222642,0.0020460773,0.000879276,0.0011821644],"category_scores_gemma":[0.002316736,0.0004007698,0.0010677782,0.0009401073,0.0009029773,0.0014142279,0.0020156845,0.002560196,0.00042914564],"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":[0.00018104343,0.00022177148,0.0030188416,0.00015414447,0.00017958984,0.00018545364,0.00021374243,0.6456585,0.023587434,0.015781956,0.009272776,0.30154482],"study_design_scores_gemma":[0.0000071833206,0.000014141674,0.00020402703,0.0000041711514,0.0000055899595,0.00002039001,0.000009134818,0.99253523,0.0024174722,0.0038979335,0.00087590865,0.000008841839],"about_ca_topic_score_codex":0.009708092,"about_ca_topic_score_gemma":0.009652697,"teacher_disagreement_score":0.009708092,"about_ca_system_score_codex":0.00093342096,"about_ca_system_score_gemma":0.0012365163,"threshold_uncertainty_score":0.019303203},"labels":[],"label_agreement":null},{"id":"W4409063143","doi":"10.3390/electronics14071419","title":"Statistically Informed Multimodal (Domain Adaptation by Transfer) Learning Framework: A Domain Adaptation Use-Case for Industrial Human–Robot Communication","year":2025,"lang":"en","type":"article","venue":"Electronics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Victoria; University of New Brunswick","funders":"","keywords":"Adaptation (eye); Domain adaptation; Domain (mathematical analysis); Transfer of learning; Human–computer interaction; Computer science; Human–robot interaction; Robot; Artificial intelligence; Knowledge management; Psychology; Neuroscience; Mathematics","score_opus":0.04483257512739393,"score_gpt":0.3062595910961061,"score_spread":0.2614270159687122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409063143","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":[0.0067731016,0.00019474562,0.9908518,0.00025267535,0.00002781387,0.00003941486,0.000041694435,0.00067243935,0.0011463702],"genre_scores_gemma":[0.50820124,0.0004454291,0.48451698,0.0005281951,0.00013923376,0.00035801646,0.0004024026,0.00028553687,0.0051228935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865204,0.00062097685,0.000051361403,0.000338522,0.00023700242,0.00010014567],"domain_scores_gemma":[0.9975925,0.0012658319,0.00017712088,0.00051242684,0.00029413038,0.00015801832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002936628,0.0008416072,0.000701024,0.0005893787,0.00052958145,0.0011819195,0.001807862,0.0018599656,0.0027394108],"category_scores_gemma":[0.0063564316,0.00034530045,0.0008191336,0.00064097764,0.0015578119,0.0018307616,0.0028553104,0.0026345379,0.0009662736],"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":[0.0002602915,0.00032471016,0.0019014596,0.00019731221,0.00013019421,0.00047493534,0.000635244,0.6124586,0.014190478,0.04539005,0.0045973086,0.31943947],"study_design_scores_gemma":[0.000008681976,0.00008589577,0.00026957164,0.000014895461,0.0000082286515,0.000081613194,0.00005392749,0.9649587,0.0031900732,0.028946212,0.0023634837,0.000018642639],"about_ca_topic_score_codex":0.0019548752,"about_ca_topic_score_gemma":0.0017251341,"teacher_disagreement_score":0.002936628,"about_ca_system_score_codex":0.0009876408,"about_ca_system_score_gemma":0.0011474966,"threshold_uncertainty_score":0.015530586},"labels":[],"label_agreement":null},{"id":"W4409262199","doi":"10.1109/wacv61041.2025.00158","title":"Cross-Task Affinity Learning for Multitask Dense Scene Predictions","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Multi-task learning; Task (project management); Artificial intelligence; Engineering","score_opus":0.021626284204140027,"score_gpt":0.3092194186197871,"score_spread":0.28759313441564704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409262199","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":[0.088392615,0.0007448148,0.90000004,0.00044403365,0.00014688417,0.00013733518,0.00032677868,0.006408965,0.003398427],"genre_scores_gemma":[0.8370986,0.00022925029,0.15429763,0.0004716767,0.00009203193,0.00014774347,0.0010955605,0.00040209052,0.006165391],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991873,0.00017477194,0.00003288333,0.00028405292,0.00016462516,0.00015634469],"domain_scores_gemma":[0.9983144,0.00070550706,0.00012632324,0.00042416598,0.00028198585,0.00014775671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015667783,0.001571716,0.0012832645,0.0007023086,0.0007136937,0.0008432882,0.0027224983,0.0016564747,0.004137354],"category_scores_gemma":[0.005125557,0.0007637568,0.0007466521,0.00088314764,0.00071351306,0.002971561,0.0028403858,0.0026027272,0.0015206446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005697671,0.0005821739,0.003830271,0.00022053566,0.000155575,0.0002350872,0.00023296874,0.37931225,0.020719849,0.008186072,0.012843567,0.5731119],"study_design_scores_gemma":[0.000011570395,0.000039452298,0.00027179386,0.0000037962748,0.00000934668,0.000029001023,0.000019088968,0.99110186,0.0030469443,0.0048221457,0.0006377147,0.000007132591],"about_ca_topic_score_codex":0.009314174,"about_ca_topic_score_gemma":0.016557291,"teacher_disagreement_score":0.009314174,"about_ca_system_score_codex":0.00132034,"about_ca_system_score_gemma":0.0015318992,"threshold_uncertainty_score":0.018519938},"labels":[],"label_agreement":null},{"id":"W4409316441","doi":"10.1016/j.asoc.2025.113026","title":"Joint hierarchical multi-granularity adaptive embedding discriminative learning for unsupervised domain adaptation","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 Alberta","funders":"National Natural Science Foundation of China","keywords":"Granularity; Discriminative model; Computer science; Embedding; Joint (building); Adaptation (eye); Domain adaptation; Artificial intelligence; Domain (mathematical analysis); Unsupervised learning; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.04079140832107272,"score_gpt":0.29555535614220974,"score_spread":0.25476394782113704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409316441","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":[0.011230099,0.00040627774,0.98695856,0.000088287394,0.00004001417,0.00002479868,0.000084659194,0.0006734539,0.0004937784],"genre_scores_gemma":[0.58979803,0.0007449813,0.40181777,0.00042493077,0.00013157805,0.00020361839,0.0013762151,0.00039713355,0.005105765],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938416,0.00016695418,0.00003181618,0.00022081207,0.00011642449,0.00007981488],"domain_scores_gemma":[0.9988875,0.00052205176,0.00007393941,0.00027928562,0.000166354,0.000070956514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000958059,0.0007645433,0.001421732,0.00082378637,0.00041610617,0.0006715626,0.0016373846,0.0010712154,0.0018656098],"category_scores_gemma":[0.0029464778,0.0004092523,0.00089969626,0.0014116317,0.0006919206,0.0015350707,0.002306559,0.0019492334,0.00086573965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038312198,0.0004479304,0.0015613803,0.00023837935,0.00021291478,0.00016187511,0.00017377,0.32537565,0.034988724,0.014761326,0.008468067,0.61322683],"study_design_scores_gemma":[0.0000055091427,0.000021838532,0.00022669134,0.0000046906894,0.000009955136,0.000024969922,0.000011439753,0.992145,0.0017051873,0.0054200278,0.00041706453,0.000007625614],"about_ca_topic_score_codex":0.003565081,"about_ca_topic_score_gemma":0.0065306746,"teacher_disagreement_score":0.003565081,"about_ca_system_score_codex":0.0004867043,"about_ca_system_score_gemma":0.0007928069,"threshold_uncertainty_score":0.007088661},"labels":[],"label_agreement":null},{"id":"W4409335063","doi":"10.1080/03081079.2025.2479156","title":"Multi-category sensitive image recognition based on RefCA-EfficientNetV2","year":2025,"lang":"en","type":"article","venue":"International Journal of General Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.026158127709960864,"score_gpt":0.2950142729584655,"score_spread":0.26885614524850465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409335063","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":[0.02780612,0.0005127624,0.9588836,0.00023776313,0.00023686237,0.00018077201,0.0005179857,0.00812233,0.0035018264],"genre_scores_gemma":[0.40008,0.00037276113,0.5838021,0.00070202106,0.00017598101,0.00031172487,0.003857798,0.00078622065,0.009911421],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989974,0.000115638846,0.00004005919,0.00041178762,0.00028228536,0.00015292304],"domain_scores_gemma":[0.99913186,0.0001392079,0.000059759728,0.00023984225,0.0003751347,0.000054136286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010184156,0.0011453812,0.0015999835,0.0020083312,0.000647298,0.0014488085,0.0028607177,0.001853554,0.0042300085],"category_scores_gemma":[0.002497627,0.00048539037,0.0012082551,0.001380778,0.0006888,0.0019520108,0.0018630833,0.0015879392,0.00232479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023010116,0.0003186283,0.0019793632,0.00017170931,0.00016266805,0.00017982612,0.00013516117,0.10453493,0.036228437,0.007214989,0.018666385,0.8301778],"study_design_scores_gemma":[0.00000981776,0.000039659568,0.00068241573,0.000011059901,0.000016494298,0.00010507321,0.0000321086,0.983411,0.0076843593,0.004659309,0.003327748,0.00002092054],"about_ca_topic_score_codex":0.012330761,"about_ca_topic_score_gemma":0.01838375,"teacher_disagreement_score":0.012330761,"about_ca_system_score_codex":0.0008908177,"about_ca_system_score_gemma":0.0014369704,"threshold_uncertainty_score":0.024518013},"labels":[],"label_agreement":null},{"id":"W4409347668","doi":"10.1609/aaai.v39i20.35413","title":"SimProF: A Simple Probabilistic Framework for Unsupervised Domain Adaptation","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Tianjin City; National Natural Science Foundation of China","keywords":"Simple (philosophy); Computer science; Adaptation (eye); Probabilistic logic; Domain adaptation; Domain (mathematical analysis); Artificial intelligence; Psychology; Mathematics; Neuroscience","score_opus":0.08333992167115802,"score_gpt":0.32239999611772824,"score_spread":0.23906007444657024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409347668","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":[0.0010105073,0.000056269007,0.9980673,0.000036311714,0.000008570386,0.000023679102,0.00003745344,0.00044330352,0.00031652604],"genre_scores_gemma":[0.18000719,0.00035199322,0.8144501,0.000290891,0.000120260265,0.0004618512,0.00075040373,0.00053481205,0.0030325083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983364,0.00058948563,0.000070123526,0.00044507024,0.0004744682,0.00008450223],"domain_scores_gemma":[0.9976767,0.0010101498,0.00017757068,0.000655658,0.0003664698,0.0001135644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030536563,0.0010916736,0.0010864779,0.0016357189,0.0006867174,0.0013158564,0.0031634218,0.0015275875,0.002605759],"category_scores_gemma":[0.006781782,0.00079065893,0.0016234631,0.0011206768,0.0014444608,0.0028072312,0.0040110606,0.0025731188,0.0013111782],"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":[0.00019937597,0.00020092944,0.0016636886,0.00022246696,0.00025931318,0.00019003327,0.0001910047,0.4988038,0.013954266,0.1028757,0.006611373,0.37482804],"study_design_scores_gemma":[0.000010112969,0.000035545512,0.00021962212,0.000008911286,0.000010107705,0.00008615507,0.000009586572,0.959968,0.0021383245,0.03454989,0.0029464667,0.000017254364],"about_ca_topic_score_codex":0.0017691869,"about_ca_topic_score_gemma":0.0029609124,"teacher_disagreement_score":0.0031634218,"about_ca_system_score_codex":0.00088256603,"about_ca_system_score_gemma":0.0012800174,"threshold_uncertainty_score":0.016149461},"labels":[],"label_agreement":null},{"id":"W4409358257","doi":"10.1016/j.eswa.2025.127569","title":"Knowledge-enhanced prototypical network with graph structure and semantic information interaction for low-shot joint spoken language understanding","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 Waterloo","funders":"","keywords":"Computer science; Joint (building); Natural language processing; Graph; Spoken language; Artificial intelligence; Shot (pellet); Semantic network; Knowledge graph; Theoretical computer science","score_opus":0.0211581728894171,"score_gpt":0.2812500811196482,"score_spread":0.2600919082302311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409358257","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":[0.0430896,0.00026029357,0.95271087,0.00014141259,0.000050480383,0.000041447554,0.00018944999,0.001519953,0.001996517],"genre_scores_gemma":[0.75110334,0.00021576401,0.24159418,0.00017674718,0.000046186786,0.00010074748,0.0010590586,0.00025754704,0.005446477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997309,0.00005923276,0.000010041274,0.00011986567,0.000045007837,0.000035002264],"domain_scores_gemma":[0.9993981,0.0003247531,0.00003012234,0.00008664828,0.00011174402,0.000048625912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046433986,0.00058845285,0.0006527705,0.0006788261,0.00046251167,0.00043910774,0.0012123625,0.0011169077,0.0027491355],"category_scores_gemma":[0.001773774,0.0003008532,0.00052220764,0.0006127832,0.0004203117,0.0018657838,0.0012207179,0.0010090527,0.0006711934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054747675,0.0003571097,0.0010479479,0.00021285134,0.00013733467,0.00046153375,0.0004626948,0.40198743,0.054526493,0.016775332,0.005264988,0.5182189],"study_design_scores_gemma":[0.000004007469,0.000018912468,0.00014174521,0.0000025942682,0.0000096152435,0.000027507824,0.00002213062,0.9916385,0.0024647322,0.0053125042,0.0003519685,0.0000057400625],"about_ca_topic_score_codex":0.008637247,"about_ca_topic_score_gemma":0.013405905,"teacher_disagreement_score":0.008637247,"about_ca_system_score_codex":0.00046622366,"about_ca_system_score_gemma":0.00063123833,"threshold_uncertainty_score":0.017173946},"labels":[],"label_agreement":null},{"id":"W4409363315","doi":"10.1609/aaai.v39i19.34247","title":"Open-Set Cross-Network Node Classification via Unknown-Excluded Adversarial Graph Domain Alignment","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Hainan University; National Natural Science Foundation of China","keywords":"Adversarial system; Node (physics); Computer science; Graph; Set (abstract data type); Theoretical computer science; Mathematics; Combinatorics; Artificial intelligence; Physics","score_opus":0.09075551443125945,"score_gpt":0.3440935025057437,"score_spread":0.25333798807448427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409363315","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":[0.055103634,0.0005144623,0.9386492,0.0004198981,0.00013862706,0.00013569396,0.0002045417,0.0016880463,0.003145901],"genre_scores_gemma":[0.8255001,0.00028154135,0.16369997,0.0006735799,0.00010571503,0.0002507266,0.0014702022,0.00029116127,0.0077271117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986785,0.00040940262,0.000041770138,0.0005274878,0.00020681297,0.00013597925],"domain_scores_gemma":[0.99722254,0.0012689393,0.00029934864,0.00061582914,0.00044436663,0.00014897276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00195477,0.0015832394,0.0012218847,0.00086224533,0.0006945126,0.0010493791,0.0033517675,0.0018814604,0.00234106],"category_scores_gemma":[0.004984686,0.00050551054,0.0010672158,0.00085323106,0.0014453268,0.0028332246,0.0027609013,0.0029402787,0.00093195646],"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":[0.00022895067,0.0001923917,0.0035411923,0.00010690354,0.000117071795,0.00024203163,0.00019472862,0.7956955,0.0040704045,0.014712847,0.008245152,0.17265284],"study_design_scores_gemma":[0.000005191708,0.000021064794,0.00016852343,0.000005524047,0.000008031235,0.000028101571,0.00001610558,0.99254334,0.0010588819,0.0056251753,0.0005145829,0.0000055228775],"about_ca_topic_score_codex":0.0037222055,"about_ca_topic_score_gemma":0.004466624,"teacher_disagreement_score":0.0037222055,"about_ca_system_score_codex":0.0015621352,"about_ca_system_score_gemma":0.000783702,"threshold_uncertainty_score":0.011334121},"labels":[],"label_agreement":null},{"id":"W4409363537","doi":"10.1609/aaai.v39i19.34197","title":"Federated Unsupervised Domain Generalization Using Global and Local Alignment of Gradients","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalization; Domain (mathematical analysis); Computer science; Artificial intelligence; Mathematics","score_opus":0.05622118777486132,"score_gpt":0.3013968096862158,"score_spread":0.24517562191135447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409363537","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":[0.03066895,0.00018895969,0.9651487,0.00017810978,0.00002822633,0.00006887903,0.00009988396,0.0028062698,0.0008120563],"genre_scores_gemma":[0.60999316,0.00017410493,0.38488021,0.00044761607,0.00007233657,0.00017952936,0.0008901658,0.00038879953,0.0029740343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987679,0.00037130032,0.00005606331,0.00048746594,0.00021075786,0.00010647605],"domain_scores_gemma":[0.9971547,0.0008667086,0.00023706132,0.0012667475,0.00033507132,0.00013971582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017762876,0.001118866,0.0018271279,0.0011654211,0.0008127584,0.0012758973,0.002325345,0.0014421523,0.0010910991],"category_scores_gemma":[0.0055434443,0.0005575306,0.0012098415,0.001262146,0.0014676313,0.0033796467,0.002857584,0.0023150041,0.00074064004],"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":[0.0002647138,0.00044593227,0.0061667105,0.00013736381,0.00026615366,0.00032077235,0.00048421236,0.52022403,0.018020395,0.016132437,0.0075459844,0.4299914],"study_design_scores_gemma":[0.000011409589,0.000034388453,0.00036230686,0.0000067772053,0.0000106419775,0.00008466012,0.000039092512,0.98020613,0.003651074,0.014700748,0.00087852357,0.000014237335],"about_ca_topic_score_codex":0.004002918,"about_ca_topic_score_gemma":0.0066066217,"teacher_disagreement_score":0.004002918,"about_ca_system_score_codex":0.00103836,"about_ca_system_score_gemma":0.0013118765,"threshold_uncertainty_score":0.00939405},"labels":[],"label_agreement":null},{"id":"W4409430152","doi":"10.1016/j.ins.2025.122194","title":"Diverse and feasible retrosynthesis using GFlowNets","year":2025,"lang":"en","type":"article","venue":"Information Sciences","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Narodowe Centrum Nauki; Fundacja na rzecz Nauki Polskiej","keywords":"Retrosynthetic analysis; Computer science; Chemistry","score_opus":0.041252676565881975,"score_gpt":0.3047082114465902,"score_spread":0.2634555348807082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409430152","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":[0.029888641,0.0005102172,0.960112,0.0002228855,0.00014342068,0.00006262796,0.00027048067,0.0020355545,0.0067541744],"genre_scores_gemma":[0.46561077,0.0003364863,0.52289426,0.00031517536,0.00014829944,0.000113815244,0.0015612858,0.0007139292,0.008305944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996013,0.00009844172,0.000016440159,0.00015705249,0.000075121134,0.000051661962],"domain_scores_gemma":[0.99910104,0.000491297,0.00003768207,0.00020141034,0.00009488117,0.00007366795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079859904,0.001439864,0.0010463872,0.00079628633,0.00070991565,0.001155494,0.0017108495,0.0016635972,0.008710885],"category_scores_gemma":[0.0028638805,0.00050821295,0.00078739726,0.0005405418,0.00077208446,0.0025597506,0.0023040266,0.0014416471,0.0022961774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012985796,0.00027420116,0.001231691,0.0004656652,0.00014636423,0.00068418914,0.00021984827,0.32636014,0.056787908,0.059175827,0.008596793,0.54475886],"study_design_scores_gemma":[0.000034120436,0.000075110824,0.00016809534,0.000029406247,0.000027090175,0.00011110551,0.000058279424,0.9348033,0.008997856,0.05264007,0.0030363046,0.000019307132],"about_ca_topic_score_codex":0.0017537462,"about_ca_topic_score_gemma":0.0045395996,"teacher_disagreement_score":0.008710885,"about_ca_system_score_codex":0.00039197193,"about_ca_system_score_gemma":0.0007526317,"threshold_uncertainty_score":0.02914083},"labels":[],"label_agreement":null},{"id":"W4409923782","doi":"10.1007/s10044-025-01474-1","title":"Enhancing out-of-distribution learning in computer vision through dominant feature masking","year":2025,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Masking (illustration); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Computer science; Distribution (mathematics); Computer vision; Mathematics; Art; Visual arts","score_opus":0.009145509653189155,"score_gpt":0.2835948923947317,"score_spread":0.2744493827415425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409923782","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":[0.024357181,0.0003950329,0.97427535,0.00009617378,0.000027191167,0.000014685988,0.000025380414,0.000308816,0.00050008466],"genre_scores_gemma":[0.6520466,0.0009927879,0.34204143,0.00029346725,0.00011274586,0.00005630875,0.00023563187,0.00021815852,0.00400283],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996414,0.00011559372,0.0000160023,0.00008258125,0.000098813354,0.000045611967],"domain_scores_gemma":[0.998429,0.0009991408,0.00008434075,0.00020598625,0.00020798449,0.00007355074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010862356,0.00059009733,0.000999097,0.00055079453,0.0002749397,0.00055961544,0.0010610992,0.00089241617,0.0008992647],"category_scores_gemma":[0.0034650532,0.000345875,0.000521689,0.0005542519,0.0006996478,0.0013957123,0.0013404161,0.001210928,0.00036698522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005651655,0.00034804197,0.0013089256,0.00030453302,0.00016233104,0.00015234415,0.00016855758,0.18786795,0.12605119,0.015459845,0.0027233455,0.6648877],"study_design_scores_gemma":[0.000008511918,0.00005985465,0.00055589597,0.000005251613,0.0000148792205,0.000079688514,0.000010452818,0.9779484,0.013042699,0.0077149374,0.00054918305,0.000010116495],"about_ca_topic_score_codex":0.002067066,"about_ca_topic_score_gemma":0.0023535788,"teacher_disagreement_score":0.002067066,"about_ca_system_score_codex":0.00037287758,"about_ca_system_score_gemma":0.0006197805,"threshold_uncertainty_score":0.005744636},"labels":[],"label_agreement":null},{"id":"W4410461119","doi":"10.1007/s11263-025-02422-6","title":"SimZSL: Zero-Shot Learning Beyond a Pre-defined Semantic Embedding Space","year":2025,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Embedding; Artificial intelligence; Computer science; Class (philosophy); Natural language processing; Zero (linguistics); Machine learning; Mathematics; Algorithm; Linguistics","score_opus":0.010404812826585286,"score_gpt":0.314376145100203,"score_spread":0.3039713322736177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410461119","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016103674,0.00021754642,0.9780297,0.00031533567,0.000046446585,0.00008615101,0.00029756036,0.003822786,0.0010807717],"genre_scores_gemma":[0.5831701,0.00026082605,0.404432,0.00090643275,0.00011939984,0.00034993739,0.0045434134,0.0005091301,0.0057086824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974482,0.00081152486,0.00014905365,0.00080909295,0.0005725369,0.0002096186],"domain_scores_gemma":[0.99627763,0.0018070267,0.00021403359,0.00085805444,0.0005898891,0.0002533046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036299217,0.0012920568,0.0018842726,0.0014062821,0.00075689936,0.0021842124,0.005296626,0.0023692518,0.006562229],"category_scores_gemma":[0.010114138,0.0007685727,0.0013270323,0.001367908,0.0023468125,0.005445147,0.0065929787,0.0037413358,0.0018534723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006776726,0.0005722789,0.0029652796,0.00038779186,0.00023200894,0.0002387103,0.00039515423,0.2954582,0.0065544625,0.043994978,0.014368327,0.6341551],"study_design_scores_gemma":[0.000018421406,0.00007356556,0.000157888,0.000014172176,0.000009444514,0.000025001056,0.000038365215,0.96462303,0.0023694488,0.031888526,0.0007684903,0.000013632888],"about_ca_topic_score_codex":0.006177464,"about_ca_topic_score_gemma":0.0060151224,"teacher_disagreement_score":0.006562229,"about_ca_system_score_codex":0.0016768276,"about_ca_system_score_gemma":0.0017365813,"threshold_uncertainty_score":0.021952868},"labels":[],"label_agreement":null},{"id":"W4410632526","doi":"10.22215/etd/2025-16363","title":"Cross Domain Model Adaptation and Generalization","year":2025,"lang":"en","type":"dissertation","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Generalization; Adaptation (eye); Domain adaptation; Computer science; Domain (mathematical analysis); Artificial intelligence; Mathematics; Psychology; Neuroscience; Mathematical analysis","score_opus":0.028140328426344556,"score_gpt":0.2906611719275853,"score_spread":0.2625208435012407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410632526","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026846392,0.0007141812,0.9674616,0.0003126195,0.00011998,0.000082042025,0.00016375537,0.0014487397,0.002850637],"genre_scores_gemma":[0.6788124,0.0015416675,0.30589232,0.00078918796,0.00016886769,0.00048430357,0.0019126486,0.0007861255,0.009612504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99850863,0.00043843003,0.00010393921,0.00055102503,0.0002872622,0.000110750814],"domain_scores_gemma":[0.9974222,0.0009049927,0.00012509234,0.0010971104,0.00039702395,0.000053627326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035991678,0.0015241911,0.0011184477,0.0007413409,0.00043764134,0.0015748439,0.0018150061,0.0012706388,0.0022918023],"category_scores_gemma":[0.008914527,0.0006412339,0.0014911843,0.00082516985,0.00090338907,0.002365361,0.003204356,0.0029945155,0.0014261472],"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":[0.00013631949,0.0001519052,0.0034201366,0.0001541156,0.00028009914,0.00017164183,0.0001703693,0.642702,0.0107361,0.015429808,0.0048621073,0.32178545],"study_design_scores_gemma":[0.000007249603,0.000048659025,0.0007722294,0.00002519481,0.000026724858,0.00008330666,0.000032672924,0.9772678,0.006143389,0.012010496,0.003565303,0.000016804373],"about_ca_topic_score_codex":0.0032381911,"about_ca_topic_score_gemma":0.0031071643,"teacher_disagreement_score":0.0035991678,"about_ca_system_score_codex":0.00084930233,"about_ca_system_score_gemma":0.0010441014,"threshold_uncertainty_score":0.019034445},"labels":[],"label_agreement":null},{"id":"W4410730191","doi":"10.1007/978-3-031-91585-7_7","title":"TF-OCM: Training-Free Optimal Community Matching for Domain Generalized Few-Shot Learning","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Shot (pellet); Matching (statistics); Domain (mathematical analysis); Artificial intelligence; Training (meteorology); Pattern recognition (psychology); Machine learning; Statistics; Mathematics","score_opus":0.04717910218694317,"score_gpt":0.28689223002154285,"score_spread":0.2397131278345997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410730191","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":[0.0054824958,0.0006639773,0.9823161,0.00014048607,0.0001443005,0.00016301984,0.0005280327,0.0091625275,0.0013991242],"genre_scores_gemma":[0.09107719,0.00038299797,0.89296806,0.00042086368,0.0001902233,0.00040190358,0.005019203,0.0017073337,0.007832242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810326,0.00037125748,0.0000864673,0.00077124743,0.0004420251,0.00022566359],"domain_scores_gemma":[0.99737144,0.0010359741,0.00009970574,0.0009001999,0.00040351762,0.00018906304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002124481,0.0021433798,0.0037822493,0.0026803582,0.0013393684,0.0016070832,0.0066725556,0.004517431,0.01178084],"category_scores_gemma":[0.008249316,0.0012046472,0.0019640538,0.0033303178,0.00122278,0.004397563,0.0044255746,0.0040093735,0.006750173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043220044,0.00031660986,0.00046128978,0.00033230495,0.00015339057,0.00012977258,0.00009771042,0.079664096,0.010099314,0.008647745,0.03559749,0.8640681],"study_design_scores_gemma":[0.000042131007,0.000055255343,0.0001375171,0.000019268333,0.00001944073,0.000089860114,0.000026962713,0.976994,0.0034600147,0.016495854,0.0026417174,0.00001798922],"about_ca_topic_score_codex":0.012844615,"about_ca_topic_score_gemma":0.01836996,"teacher_disagreement_score":0.012844615,"about_ca_system_score_codex":0.0014441533,"about_ca_system_score_gemma":0.002257684,"threshold_uncertainty_score":0.03941077},"labels":[],"label_agreement":null},{"id":"W4411153423","doi":"10.3390/brainsci15060618","title":"Prediction of Alzheimer’s Disease Based on Multi-Modal Domain Adaptation","year":2025,"lang":"en","type":"article","venue":"Brain Sciences","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Adaptation (eye); Modal; Disease; Alzheimer's disease; Neuroscience; Computer science; Psychology; Medicine; Materials science; Internal medicine","score_opus":0.07464844117617232,"score_gpt":0.30876621475838684,"score_spread":0.23411777358221453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411153423","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56415194,0.0028352488,0.4260717,0.0011156448,0.00023332832,0.00012712291,0.00096836215,0.0011672437,0.0033294328],"genre_scores_gemma":[0.9803004,0.00036725256,0.017731428,0.00016223233,0.0000874642,0.000042811076,0.00053413864,0.000015722528,0.00075857784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997645,0.000051954517,0.000014106595,0.0000956031,0.00003227166,0.000041574116],"domain_scores_gemma":[0.9992312,0.00035541278,0.000094278985,0.000054843018,0.00018585527,0.000078488745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008691968,0.00064073515,0.00055460096,0.0012145899,0.00021373812,0.0005328107,0.00053756917,0.00079245755,0.00097350386],"category_scores_gemma":[0.0017890773,0.00016633605,0.00079082587,0.00041851393,0.00034819558,0.00056307716,0.00052309764,0.0010035076,0.00033584522],"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":[0.0010091556,0.00072602136,0.082877815,0.00014885685,0.000364502,0.00067635824,0.00015029787,0.54995435,0.014650238,0.0018568411,0.0102928225,0.33729282],"study_design_scores_gemma":[0.000006471862,0.000024150637,0.0039044626,0.0000069920948,0.000016525879,0.00006730997,0.000014173887,0.99399006,0.0006393795,0.0011331197,0.00018886535,0.000008452451],"about_ca_topic_score_codex":0.004043205,"about_ca_topic_score_gemma":0.0027506144,"teacher_disagreement_score":0.004043205,"about_ca_system_score_codex":0.00038831957,"about_ca_system_score_gemma":0.000429616,"threshold_uncertainty_score":0.008039296},"labels":[],"label_agreement":null},{"id":"W4411300989","doi":"10.1007/978-981-96-8170-9_24","title":"Informed Augmentation Selection Improves Tabular Contrastive Learning","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Computer science; Selection (genetic algorithm); Artificial intelligence; Natural language processing; Machine learning","score_opus":0.010179958488680817,"score_gpt":0.2496584897264657,"score_spread":0.2394785312377849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411300989","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06082721,0.0027415599,0.9086886,0.00047680413,0.0006639208,0.00014143821,0.0009449347,0.012871312,0.012644298],"genre_scores_gemma":[0.54494643,0.0007881746,0.4209381,0.0011924605,0.00043898658,0.0002108224,0.004331745,0.0019523012,0.02520093],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929416,0.0002503576,0.000025955813,0.00024363835,0.000111871756,0.00007394272],"domain_scores_gemma":[0.9983525,0.00097377336,0.000044240733,0.00036275428,0.00019687475,0.000069793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001068862,0.0012755364,0.0012871792,0.0007991624,0.0005727453,0.0012969762,0.0021259584,0.0018528274,0.015046437],"category_scores_gemma":[0.00400625,0.0004574326,0.0008301831,0.0008879213,0.0006686855,0.0027024713,0.0020727897,0.002419416,0.004504938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010771464,0.00037504444,0.00059687294,0.00024115063,0.000104998944,0.0001313728,0.000075755095,0.08177976,0.024092332,0.00821818,0.028537782,0.8547696],"study_design_scores_gemma":[0.000056503657,0.0001401273,0.00024230931,0.000026163092,0.000037047026,0.00007582268,0.000022056738,0.9776328,0.008001551,0.010839663,0.002908824,0.000017065968],"about_ca_topic_score_codex":0.0023542852,"about_ca_topic_score_gemma":0.0049585006,"teacher_disagreement_score":0.015046437,"about_ca_system_score_codex":0.00052690954,"about_ca_system_score_gemma":0.00069162034,"threshold_uncertainty_score":0.050335407},"labels":[],"label_agreement":null},{"id":"W4411399482","doi":"10.1016/j.knosys.2025.113914","title":"Task-Oriented Dynamic Knowledge Distillation for Continuous Few-Shot Relation Extraction","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"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 Waterloo","funders":"Key Laboratory of Software Engineering of Yunnan Province; Yunnan Provincial Department of Education; Yunnan University; Natural Science Foundation of Yunnan Province","keywords":"Distillation; Shot (pellet); Relation (database); Extraction (chemistry); Task (project management); Computer science; One shot; Process engineering; Chromatography; Chemistry; Engineering; Data mining; Mechanical engineering; Systems engineering; Organic chemistry","score_opus":0.019268140357351107,"score_gpt":0.31105608434944537,"score_spread":0.2917879439920943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411399482","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":[0.021185843,0.0013559477,0.9671988,0.0003083563,0.00017647447,0.00013864592,0.0009327089,0.0069814282,0.0017218656],"genre_scores_gemma":[0.3967806,0.0009917635,0.58806676,0.0005836937,0.00024917137,0.00028982002,0.006171164,0.00070268114,0.006164302],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987888,0.00019906159,0.000085680746,0.0005723509,0.0002245128,0.00012955235],"domain_scores_gemma":[0.9975458,0.00131763,0.00011407156,0.0005583626,0.00033769873,0.00012639318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014288975,0.0016072287,0.0020550878,0.0024015491,0.0011019107,0.0017421966,0.0034841641,0.0022596568,0.0049123047],"category_scores_gemma":[0.005637031,0.0007311204,0.0013563042,0.002537045,0.00078672334,0.004077532,0.0036293042,0.002934243,0.0027894077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048528987,0.0004872973,0.0008215939,0.00044131128,0.00018640827,0.00029745663,0.00022845625,0.03666374,0.036635745,0.0048315576,0.014140107,0.904781],"study_design_scores_gemma":[0.000023943892,0.00008073277,0.0007102617,0.000038586277,0.00007295698,0.00018360266,0.000098333745,0.96057147,0.01835711,0.015491683,0.004331447,0.000039927523],"about_ca_topic_score_codex":0.0066099297,"about_ca_topic_score_gemma":0.011316085,"teacher_disagreement_score":0.0066099297,"about_ca_system_score_codex":0.0006983608,"about_ca_system_score_gemma":0.001764884,"threshold_uncertainty_score":0.016433358},"labels":[],"label_agreement":null},{"id":"W4411987910","doi":"10.1016/j.cviu.2025.104438","title":"Distribution-aware contrastive learning for domain adaptation in 3D LiDAR segmentation","year":2025,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Domain adaptation; Computer science; Segmentation; Lidar; Artificial intelligence; Domain (mathematical analysis); Adaptation (eye); Computer vision; Distribution (mathematics); Pattern recognition (psychology); Remote sensing; Geography; Mathematics; Psychology","score_opus":0.024854141151867452,"score_gpt":0.289053672136619,"score_spread":0.2641995309847515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411987910","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":[0.025092829,0.0003466595,0.97250396,0.00016989761,0.000037600355,0.00003870681,0.00008193889,0.0011493288,0.0005790884],"genre_scores_gemma":[0.64978653,0.00032317193,0.3456346,0.0005820003,0.0001207906,0.00018324424,0.0009680068,0.00046438913,0.0019372675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992003,0.00020949416,0.000036120084,0.00031161268,0.00014807467,0.00009447253],"domain_scores_gemma":[0.99850357,0.00077341363,0.00012765951,0.0002748419,0.00022333475,0.00009729502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016886605,0.0008679376,0.0013602369,0.0011912799,0.00057596795,0.0011102749,0.0022167948,0.0015481499,0.0012790771],"category_scores_gemma":[0.00437066,0.00059114554,0.0012521322,0.0011902807,0.0013095851,0.0021075166,0.0022247422,0.0023983526,0.00064583047],"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":[0.00029389106,0.00025697384,0.00268594,0.00017309404,0.00013209658,0.00014745898,0.00025803485,0.58509105,0.018815653,0.011252282,0.0054432997,0.37545025],"study_design_scores_gemma":[0.0000051246457,0.000014719431,0.00015929264,0.000004123026,0.000004079062,0.000022285509,0.00001059748,0.9934041,0.0016907452,0.004332898,0.00034673564,0.000005349858],"about_ca_topic_score_codex":0.0036688366,"about_ca_topic_score_gemma":0.0042527206,"teacher_disagreement_score":0.0036688366,"about_ca_system_score_codex":0.0012173158,"about_ca_system_score_gemma":0.0010645915,"threshold_uncertainty_score":0.008930564},"labels":[],"label_agreement":null},{"id":"W4411992375","doi":"10.1016/j.neucom.2025.130902","title":"Pyramid hierarchical envelope generation structure based collaborative semantic unsupervised domain adaptation","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 Alberta","funders":"Chongqing Science and Technology Foundation; National Natural Science Foundation of China","keywords":"Computer science; Pyramid (geometry); Artificial intelligence; Domain (mathematical analysis); Domain adaptation; Envelope (radar); Natural language processing; Pattern recognition (psychology); Adaptation (eye); Mathematics; Psychology","score_opus":0.01596334489507455,"score_gpt":0.249596508328977,"score_spread":0.23363316343390245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411992375","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":[0.01081224,0.000119599565,0.98647887,0.00007005422,0.00003693542,0.000038011687,0.00009016947,0.0010419142,0.0013121421],"genre_scores_gemma":[0.46676177,0.0002806349,0.5234785,0.00040535696,0.00007544389,0.00015362,0.0015453788,0.00049558654,0.006803702],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993723,0.00010761147,0.000025445144,0.00020687397,0.00019819336,0.00008950866],"domain_scores_gemma":[0.9993278,0.00015965311,0.00003108994,0.00021805766,0.00021737353,0.00004601062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077978615,0.0008207697,0.0011663846,0.0010052368,0.00057738554,0.00085699133,0.0018594081,0.0013257294,0.002926135],"category_scores_gemma":[0.0019281465,0.0004076481,0.0013476991,0.0011434866,0.0006944997,0.0017939722,0.0027543132,0.0015039565,0.0014305367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004372151,0.00042416205,0.0013607,0.00013847197,0.00020543535,0.00033627788,0.00024237362,0.2535291,0.07304687,0.017765999,0.009783483,0.64272994],"study_design_scores_gemma":[0.0000085756565,0.000028497088,0.00028881978,0.000004217266,0.000018159983,0.00006830707,0.000025218409,0.9826469,0.007509885,0.008338251,0.0010512865,0.00001180753],"about_ca_topic_score_codex":0.0035273822,"about_ca_topic_score_gemma":0.00512987,"teacher_disagreement_score":0.0035273822,"about_ca_system_score_codex":0.0004658112,"about_ca_system_score_gemma":0.0007801522,"threshold_uncertainty_score":0.00978893},"labels":[],"label_agreement":null},{"id":"W4412122873","doi":"10.1073/pnas.2502599122","title":"Asymptotic theory of in-context learning by linear attention","year":2025,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Perimeter Institute","funders":"National Science Foundation","keywords":"Learning curve; Computer science; Generalization; Memorization; Context (archaeology); Security token; Task (project management); Scaling; Mathematics; Artificial intelligence; Machine learning; Cognitive psychology; Psychology","score_opus":0.02662637391215992,"score_gpt":0.2931535029031697,"score_spread":0.2665271289910098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412122873","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097868204,0.00093965814,0.88227826,0.002060266,0.0000782901,0.00007408371,0.00017317217,0.001229233,0.015298737],"genre_scores_gemma":[0.92652315,0.000816491,0.058116626,0.00062691054,0.00014081059,0.0003424505,0.00024181565,0.00031000364,0.012881709],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99917454,0.00027867855,0.000035444875,0.00016538787,0.00020440444,0.00014152241],"domain_scores_gemma":[0.99336123,0.0046975734,0.0004984006,0.0006305302,0.00054151326,0.00027069906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022652638,0.0008363226,0.0010934898,0.0009698693,0.00062280544,0.0011406939,0.0022617532,0.0014907058,0.0050253873],"category_scores_gemma":[0.020375207,0.000712934,0.00077255734,0.0004195739,0.0024527758,0.0035094882,0.0019795152,0.002340637,0.0009802249],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012837313,0.00012301802,0.0028111048,0.00036216536,0.00009256365,0.00021609418,0.00045370078,0.41293567,0.0077135717,0.53445035,0.004441392,0.03627197],"study_design_scores_gemma":[0.000011078179,0.00003196657,0.00052353425,0.000019928328,0.000009508764,0.000052799598,0.000018683706,0.8617915,0.0005546387,0.13654213,0.00043050956,0.0000136296285],"about_ca_topic_score_codex":0.0050327554,"about_ca_topic_score_gemma":0.0040319897,"teacher_disagreement_score":0.0050327554,"about_ca_system_score_codex":0.0019914156,"about_ca_system_score_gemma":0.0010301233,"threshold_uncertainty_score":0.01681161},"labels":[],"label_agreement":null},{"id":"W4412583990","doi":"10.1007/978-3-031-89994-2_6","title":"Automating SBOM Generation with Zero-Shot Semantic Similarity","year":2025,"lang":"en","type":"book-chapter","venue":"Signals and communication technology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Queen's University","funders":"","keywords":"Shot (pellet); Zero (linguistics); Similarity (geometry); Semantic similarity; Computer science; Artificial intelligence; Natural language processing; Information retrieval; Materials science; Linguistics; Philosophy; Image (mathematics)","score_opus":0.030865354201255666,"score_gpt":0.2548504131153932,"score_spread":0.22398505891413753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412583990","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058884425,0.00020572235,0.9855261,0.00008000952,0.0002028364,0.0001128553,0.00028723947,0.005359285,0.0023375044],"genre_scores_gemma":[0.090526946,0.0002168677,0.89821607,0.00021189569,0.00010955274,0.00017516481,0.0032601352,0.0013999954,0.005883337],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992686,0.0001226414,0.000040285016,0.0002414104,0.00023322394,0.00009387436],"domain_scores_gemma":[0.9989844,0.00033403863,0.0000316245,0.00021020089,0.00037858935,0.00006114308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009209356,0.0014590897,0.0014296453,0.002137123,0.0007356986,0.0014050517,0.0021217987,0.00174535,0.011152619],"category_scores_gemma":[0.0032604113,0.0005835518,0.0016980214,0.0015490707,0.0006211784,0.0020886778,0.0027077806,0.0017802328,0.0075765816],"study_design_candidate":"bench_or_experimental","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":[0.00026505927,0.00015190533,0.00041589938,0.00020780627,0.00006877897,0.00015539219,0.00009623138,0.012418287,0.03480925,0.006871902,0.016648317,0.927891],"study_design_scores_gemma":[0.000039728824,0.00011413071,0.0005800935,0.000044460703,0.000054169624,0.00035271008,0.00011798855,0.92207015,0.04174918,0.023340527,0.011506887,0.000029909657],"about_ca_topic_score_codex":0.0034766046,"about_ca_topic_score_gemma":0.0058350516,"teacher_disagreement_score":0.011152619,"about_ca_system_score_codex":0.00050236855,"about_ca_system_score_gemma":0.0010481771,"threshold_uncertainty_score":0.03730923},"labels":[],"label_agreement":null},{"id":"W4412866368","doi":"10.1088/2632-2153/adf701","title":"SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks","year":2025,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"Fermilab; Oak Ridge Institute for Science and Education; National Science Foundation","keywords":"Equivariant map; Domain adaptation; Adaptation (eye); Computer science; Domain (mathematical analysis); Image (mathematics); Artificial neural network; Artificial intelligence; Pattern recognition (psychology); Mathematics; Pure mathematics; Psychology; Mathematical analysis; Neuroscience","score_opus":0.009021148630347827,"score_gpt":0.2581942054823836,"score_spread":0.24917305685203575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412866368","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029584581,0.00022668908,0.9671709,0.00014032998,0.0000667837,0.000036940495,0.000056608584,0.0015238029,0.0011933213],"genre_scores_gemma":[0.61525565,0.0001432516,0.3797003,0.0003662875,0.000050284318,0.00014363656,0.0004715333,0.0002579595,0.0036110368],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996043,0.00013652886,0.000024317731,0.0001071425,0.00008852438,0.00003916776],"domain_scores_gemma":[0.9991405,0.00035227006,0.00008072178,0.00016355058,0.00020109219,0.000061889354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010439359,0.0007399452,0.00071867974,0.0005103012,0.00035191738,0.0006827332,0.0015125277,0.0009576867,0.0020708297],"category_scores_gemma":[0.00295781,0.00034709045,0.000711995,0.0004771957,0.00074062735,0.0008703823,0.0013562435,0.0021009324,0.0006646972],"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":[0.00014663435,0.000104460676,0.0018396095,0.000069041394,0.00008210559,0.00009534294,0.00008984006,0.75590265,0.009875281,0.00956172,0.0032201773,0.21901315],"study_design_scores_gemma":[0.0000023701175,0.000011896142,0.00004872364,0.0000017452002,0.0000011853476,0.0000061481683,0.0000038216076,0.99717253,0.000758237,0.0017183269,0.0002723832,0.0000026770442],"about_ca_topic_score_codex":0.0027493185,"about_ca_topic_score_gemma":0.00357994,"teacher_disagreement_score":0.0027493185,"about_ca_system_score_codex":0.00072723493,"about_ca_system_score_gemma":0.0006773162,"threshold_uncertainty_score":0.006927669},"labels":[],"label_agreement":null},{"id":"W4412875458","doi":"10.1145/3711896.3736830","title":"Adaptive Conformal Prediction Intervals for Invariant Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Invariant (physics); Conformal map; Computer science; Artificial intelligence; Mathematics; Mathematical analysis","score_opus":0.023202530547740435,"score_gpt":0.26626511935752356,"score_spread":0.24306258880978313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412875458","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01688819,0.00022205133,0.98157847,0.0000992187,0.000020591506,0.000040474162,0.000058096273,0.00031264752,0.0007803414],"genre_scores_gemma":[0.74661773,0.00035725546,0.24957454,0.00027909293,0.00012082321,0.00036077067,0.0006815314,0.00030271395,0.0017054607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99782985,0.0006959934,0.00008686666,0.0006252676,0.0006223715,0.00013969833],"domain_scores_gemma":[0.9896347,0.006961298,0.00078038196,0.0013431403,0.0009262066,0.00035424664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043506837,0.0007738523,0.0012413604,0.0010628246,0.0005518148,0.0011545038,0.0022079528,0.0010630499,0.0024079117],"category_scores_gemma":[0.026784472,0.00041761086,0.0008284173,0.00072196795,0.0016106495,0.0021279783,0.0029171435,0.0026325302,0.00044379532],"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":[0.00023743084,0.00010356789,0.0033939844,0.00013019271,0.00007371949,0.00020571731,0.00029864445,0.71480435,0.004465884,0.11095473,0.0028715122,0.16246028],"study_design_scores_gemma":[0.000009229291,0.00003714332,0.00025227992,0.000009610792,0.000006625969,0.000035447,0.000010952814,0.96583396,0.0012805039,0.03199722,0.0005163168,0.000010671873],"about_ca_topic_score_codex":0.0012891772,"about_ca_topic_score_gemma":0.0008525403,"teacher_disagreement_score":0.0043506837,"about_ca_system_score_codex":0.0010502982,"about_ca_system_score_gemma":0.00084852695,"threshold_uncertainty_score":0.023008883},"labels":[],"label_agreement":null},{"id":"W4412876994","doi":"10.1145/3711896.3737215","title":"Enhancing Learned Knowledge in LoRA Adapters Through Efficient Contrastive Decoding on Ascend NPUs","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Decoding methods; Artificial intelligence; Telecommunications","score_opus":0.04932663034555771,"score_gpt":0.33058814480607246,"score_spread":0.2812615144605147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412876994","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072474994,0.00037425847,0.86239684,0.00042313576,0.0001820141,0.00020124849,0.0006236805,0.057227124,0.006096718],"genre_scores_gemma":[0.5096792,0.00017781508,0.47388554,0.000760158,0.00006488398,0.00046596106,0.0030467655,0.002530449,0.0093891965],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987882,0.00024895548,0.000075136224,0.0003212838,0.0003673738,0.0001991965],"domain_scores_gemma":[0.99822146,0.0006340254,0.00007282153,0.00046127182,0.0004560418,0.0001543562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012269422,0.001366289,0.0009206934,0.0006887771,0.00080381474,0.001586409,0.0024346064,0.0012653503,0.006539959],"category_scores_gemma":[0.0075987936,0.00055072946,0.00084681093,0.00079378055,0.0011181388,0.002738158,0.0033589737,0.0029328929,0.005654903],"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":[0.0011253349,0.0005719453,0.0038044,0.00020089523,0.00017773717,0.00090118055,0.000709113,0.2662006,0.027533585,0.01647332,0.033560015,0.64874184],"study_design_scores_gemma":[0.000028015944,0.00005734574,0.00017523713,0.0000081297185,0.000009023106,0.00005208026,0.00006317128,0.9787017,0.010543635,0.0070949225,0.0032476045,0.000019012045],"about_ca_topic_score_codex":0.0123286545,"about_ca_topic_score_gemma":0.019054102,"teacher_disagreement_score":0.0123286545,"about_ca_system_score_codex":0.0013136957,"about_ca_system_score_gemma":0.0021854255,"threshold_uncertainty_score":0.024513781},"labels":[],"label_agreement":null},{"id":"W4412889920","doi":"10.18653/v1/2025.acl-long.1245","title":"On Many-Shot In-Context Learning for Long-Context Evaluation","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction; National Science Foundation","keywords":"Shot (pellet); Computer science; Context (archaeology); One shot; Artificial intelligence; Human–computer interaction; Engineering; History","score_opus":0.046401308377111254,"score_gpt":0.32829550493279025,"score_spread":0.281894196555679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412889920","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":[0.3221135,0.023311106,0.5671146,0.0027407107,0.0021400258,0.0016041854,0.0062241876,0.056509387,0.018242337],"genre_scores_gemma":[0.71528786,0.0011789323,0.26137397,0.001590489,0.0002558958,0.0007735642,0.013126984,0.0016837517,0.0047285412],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9900283,0.005542515,0.00055555435,0.002091121,0.001310782,0.00047175036],"domain_scores_gemma":[0.9837307,0.010741123,0.00050955074,0.0023248277,0.0019297951,0.0007640757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011574237,0.0037881734,0.0020431934,0.0021963385,0.0016400386,0.0028719709,0.0041537597,0.0038550098,0.005146584],"category_scores_gemma":[0.040956568,0.00086518476,0.0013356279,0.0015185016,0.0014791742,0.006428629,0.0048405835,0.005017275,0.0027088302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022889022,0.0018358943,0.007783035,0.0015788755,0.00088684796,0.0004906967,0.00050211,0.29425606,0.014346913,0.0046943882,0.032118388,0.6392179],"study_design_scores_gemma":[0.00018129068,0.0012021487,0.0019448205,0.00014613327,0.00010088163,0.00025783025,0.00033243952,0.9721819,0.011655227,0.0075848233,0.0042999275,0.00011253321],"about_ca_topic_score_codex":0.016765285,"about_ca_topic_score_gemma":0.024253562,"teacher_disagreement_score":0.016765285,"about_ca_system_score_codex":0.0027174526,"about_ca_system_score_gemma":0.0022133316,"threshold_uncertainty_score":0.06121117},"labels":[],"label_agreement":null},{"id":"W4413108536","doi":"10.1109/tpami.2025.3593407","title":"Bringing Equity to Classification: Domain Generalization for Domain-Linked Classes","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Domain (mathematical analysis); Artificial intelligence; Generalizability theory; Generalization; Spurious relationship; Machine learning; Mathematics; Statistics","score_opus":0.04523036121757372,"score_gpt":0.33297388294216107,"score_spread":0.28774352172458734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413108536","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073422894,0.00084215665,0.92033666,0.0011099223,0.00009416308,0.00016453527,0.00023980344,0.0008587209,0.002931219],"genre_scores_gemma":[0.78941554,0.00039320317,0.20340352,0.001207026,0.00026000905,0.00027078623,0.0009849457,0.0002988166,0.0037662268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967493,0.001297877,0.00012658745,0.0011539826,0.000444447,0.00022798673],"domain_scores_gemma":[0.9887649,0.006092004,0.00075512205,0.0032472843,0.00062694424,0.0005138254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074721063,0.0014318745,0.0014973119,0.0013062155,0.0011977045,0.0020662958,0.0027674774,0.0024669406,0.0026808926],"category_scores_gemma":[0.018400636,0.00042728998,0.0011499708,0.0012065322,0.00259566,0.0053631715,0.00696619,0.0045629875,0.0006329987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008848303,0.0006777866,0.014371607,0.0003995318,0.00031440778,0.00025590745,0.00096666324,0.29370275,0.012313743,0.09885812,0.011761114,0.5654936],"study_design_scores_gemma":[0.00005368294,0.0001862369,0.0014317357,0.000048705366,0.000048216538,0.00011866558,0.00015063466,0.8498022,0.004816369,0.13979673,0.0035172796,0.000029637013],"about_ca_topic_score_codex":0.00230109,"about_ca_topic_score_gemma":0.0022607797,"teacher_disagreement_score":0.0074721063,"about_ca_system_score_codex":0.0017709496,"about_ca_system_score_gemma":0.0015367585,"threshold_uncertainty_score":0.039516807},"labels":[],"label_agreement":null},{"id":"W4413143844","doi":"10.1016/j.iswa.2025.200567","title":"LWR-Net: Learning without retraining for scalable multi-task adaptation and domain-agnostic generalisation","year":2025,"lang":"en","type":"article","venue":"Intelligent Systems with Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kensington Health","funders":"Australian Research Council","keywords":"Retraining; Scalability; Computer science; Domain adaptation; Adaptation (eye); Task (project management); Net (polyhedron); Domain (mathematical analysis); Artificial intelligence; Machine learning; Database; Engineering; Systems engineering; Mathematics; Psychology","score_opus":0.037884591088655155,"score_gpt":0.2838944364537177,"score_spread":0.24600984536506254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413143844","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":[0.022897573,0.001099691,0.9581904,0.00038950713,0.00021411527,0.000213563,0.00026593104,0.014367859,0.0023614925],"genre_scores_gemma":[0.45784634,0.0007581408,0.5248525,0.0014440457,0.00022330077,0.0009770925,0.002267144,0.0014425131,0.010188891],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880624,0.00020632798,0.00007869291,0.00048776713,0.0002482274,0.00017276149],"domain_scores_gemma":[0.9987452,0.0003951407,0.0001222658,0.00032149127,0.0003155889,0.00010029237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002679172,0.002723928,0.0017947297,0.00086177804,0.0006039462,0.0009591084,0.0046139434,0.0023207786,0.0030647612],"category_scores_gemma":[0.0045810407,0.0010795215,0.0018232829,0.00083971594,0.0009451448,0.0026319106,0.0032357296,0.0041739354,0.0022631157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003592131,0.00048712108,0.0014520616,0.00025275256,0.00023993808,0.00039303352,0.00018075775,0.43143976,0.019503249,0.0036598176,0.014011366,0.528021],"study_design_scores_gemma":[0.000023896979,0.00008634496,0.00019592988,0.000011891916,0.00002095377,0.00004469438,0.000013899864,0.99174255,0.0033709353,0.0030075032,0.0014639267,0.000017377613],"about_ca_topic_score_codex":0.008159791,"about_ca_topic_score_gemma":0.008983616,"teacher_disagreement_score":0.008159791,"about_ca_system_score_codex":0.0012606622,"about_ca_system_score_gemma":0.0015179781,"threshold_uncertainty_score":0.016224563},"labels":[],"label_agreement":null},{"id":"W4413146970","doi":"10.1109/cvpr52734.2025.00442","title":"Large Self-Supervised Models Bridge the Gap in Domain Adaptive Object Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Bridge (graph theory); Artificial intelligence; Domain (mathematical analysis); Object detection; Object (grammar); Computer vision; Domain adaptation; Pattern recognition (psychology); Mathematics","score_opus":0.027472650910546282,"score_gpt":0.255918588728954,"score_spread":0.22844593781840772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413146970","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":[0.07129277,0.002367748,0.89706385,0.0013855866,0.0003357116,0.00021425854,0.0011971113,0.018201156,0.007941906],"genre_scores_gemma":[0.50768834,0.0008415949,0.46161398,0.0017777467,0.00031196096,0.00041739494,0.008964051,0.001978101,0.016406747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986526,0.0003742269,0.000041323314,0.0006043248,0.00022229704,0.00010530343],"domain_scores_gemma":[0.99682105,0.0011842819,0.00015681714,0.0012656451,0.0004248049,0.0001473349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023947605,0.0021540278,0.0011425621,0.0010017377,0.0006992679,0.0013903705,0.004140406,0.0019743897,0.0028570355],"category_scores_gemma":[0.0059194528,0.0007144817,0.0010815827,0.0009947555,0.0014947612,0.0034102031,0.0031501225,0.004691271,0.0032936493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049675535,0.0006349073,0.00456193,0.00040064452,0.00025447676,0.0001769058,0.00034436223,0.3326016,0.012478093,0.015932467,0.046897072,0.58522075],"study_design_scores_gemma":[0.000026002539,0.000055605444,0.00034654583,0.000022769018,0.000013225324,0.000050905444,0.000036579306,0.9778878,0.003670772,0.013749298,0.0041273683,0.000013169392],"about_ca_topic_score_codex":0.0052884878,"about_ca_topic_score_gemma":0.0114399865,"teacher_disagreement_score":0.0052884878,"about_ca_system_score_codex":0.0014431415,"about_ca_system_score_gemma":0.0010656918,"threshold_uncertainty_score":0.0126648545},"labels":[],"label_agreement":null},{"id":"W4413156549","doi":"10.1109/cvpr52734.2025.00432","title":"DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Shot (pellet); Segmentation; Artificial intelligence; Image segmentation; Computer vision; Natural language processing","score_opus":0.02812949130331487,"score_gpt":0.3043512071728003,"score_spread":0.2762217158694854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413156549","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":[0.012386371,0.0006701423,0.9707774,0.00018809228,0.00010665583,0.00019166128,0.00087633694,0.013412193,0.0013910563],"genre_scores_gemma":[0.2165854,0.00047892894,0.7676689,0.000771476,0.00015203973,0.00043855354,0.0070232134,0.0023712716,0.004510233],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998657,0.00020681108,0.00007679447,0.00052982423,0.000358211,0.00017136887],"domain_scores_gemma":[0.9987748,0.00038466405,0.000109896115,0.00033855878,0.000268718,0.00012336798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014643547,0.0023180058,0.0022208928,0.002886806,0.00072318705,0.0020034753,0.0044562737,0.002273572,0.0051194066],"category_scores_gemma":[0.0039909435,0.00089404086,0.0021230672,0.0017628999,0.0013316706,0.0039640134,0.0039918325,0.0022761358,0.0027045463],"study_design_candidate":"bench_or_experimental","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":[0.00080879114,0.0003318598,0.00119822,0.0006010879,0.00020985879,0.00030742475,0.0004365319,0.09052221,0.06979772,0.0125508085,0.019643452,0.803592],"study_design_scores_gemma":[0.000040942923,0.000116241434,0.00035469286,0.00003405435,0.00003662743,0.0001134672,0.00009991362,0.9576171,0.017847309,0.017575642,0.006121578,0.00004245705],"about_ca_topic_score_codex":0.009481785,"about_ca_topic_score_gemma":0.015080792,"teacher_disagreement_score":0.009481785,"about_ca_system_score_codex":0.001763866,"about_ca_system_score_gemma":0.0020705045,"threshold_uncertainty_score":0.018853188},"labels":[],"label_agreement":null},{"id":"W4413158559","doi":"10.1109/cvpr52734.2025.02201","title":"ReNeg: Learning Negative Embedding with Reward Guidance","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Embedding; Artificial intelligence","score_opus":0.009214591520296031,"score_gpt":0.26736255207254545,"score_spread":0.2581479605522494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413158559","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025061244,0.000363441,0.95998424,0.00020505983,0.00013905484,0.00013697735,0.00027305208,0.011343882,0.0024929876],"genre_scores_gemma":[0.4716268,0.00027558405,0.50447977,0.0008863783,0.000113623384,0.00045482675,0.003033156,0.0026687304,0.016461167],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992638,0.00021754066,0.000026246706,0.00026570467,0.00015205944,0.00007454605],"domain_scores_gemma":[0.99886525,0.00049542874,0.000100514706,0.00027083952,0.00018798007,0.00008004504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013725059,0.00203519,0.0009683675,0.00051352236,0.00034138534,0.00087749714,0.002218976,0.001496612,0.0057193115],"category_scores_gemma":[0.005375795,0.00055250106,0.0006250544,0.00036316898,0.0008443218,0.0022000659,0.0017490883,0.0022404694,0.0028372705],"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":[0.00074269867,0.00051916164,0.0019114316,0.00027283156,0.000088751105,0.00031777495,0.00021990456,0.27432907,0.028920388,0.013675347,0.025837377,0.6531653],"study_design_scores_gemma":[0.000052003226,0.00013840244,0.0001730722,0.000016884602,0.000010373126,0.000069119335,0.00001923061,0.9785765,0.009754991,0.008472629,0.0026975665,0.00001924815],"about_ca_topic_score_codex":0.001730999,"about_ca_topic_score_gemma":0.0035175404,"teacher_disagreement_score":0.0057193115,"about_ca_system_score_codex":0.00067287235,"about_ca_system_score_gemma":0.0005971094,"threshold_uncertainty_score":0.019132972},"labels":[],"label_agreement":null},{"id":"W4413275632","doi":"10.1016/j.media.2025.103764","title":"BiasPruner: Mitigating bias transfer in continual learning for fair medical image analysis","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Victoria; University of British Columbia; University of British Columbia Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Transfer of learning; Computer science; Image (mathematics); Computer vision; Machine learning","score_opus":0.01483735090395529,"score_gpt":0.30610355023936614,"score_spread":0.29126619933541087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413275632","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":[0.051672444,0.000945165,0.9413605,0.00070824125,0.00008825964,0.00015845457,0.000119629,0.0037572484,0.0011901594],"genre_scores_gemma":[0.68810004,0.00029672144,0.305812,0.0010271718,0.00013503677,0.0003231574,0.00039303937,0.0006032144,0.003309681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856144,0.00051849824,0.00006800432,0.00044991923,0.00025713292,0.0001451055],"domain_scores_gemma":[0.9945111,0.0031031556,0.0004567592,0.0011750248,0.0004604066,0.00029357694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067257765,0.0013812851,0.0013589077,0.0010735231,0.00081517297,0.0011595306,0.0038775338,0.0022512742,0.0027268662],"category_scores_gemma":[0.01932089,0.00080351107,0.0010991755,0.00058965996,0.0018714142,0.0025004821,0.0033712413,0.0030109745,0.0006476389],"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":[0.00059583655,0.0003057526,0.0069820834,0.00021979574,0.00023957236,0.00028077658,0.00037710372,0.63441634,0.008366476,0.012528054,0.005162989,0.33052522],"study_design_scores_gemma":[0.000032295073,0.00006498246,0.00028344325,0.00001645044,0.000016465587,0.000047132628,0.000012311202,0.98503125,0.0019531518,0.012035465,0.0004966573,0.000010334103],"about_ca_topic_score_codex":0.004480644,"about_ca_topic_score_gemma":0.0066408454,"teacher_disagreement_score":0.0067257765,"about_ca_system_score_codex":0.001393576,"about_ca_system_score_gemma":0.0020083857,"threshold_uncertainty_score":0.035569727},"labels":[],"label_agreement":null},{"id":"W4413275917","doi":"10.1016/j.neunet.2025.107997","title":"Enabling generalized zero-shot learning towards unseen domains by intrinsic learning from redundant LLM semantics","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of Alberta","funders":"National Science Fund for Distinguished Young Scholars; State Key Laboratory of Industrial Control Technology; National Natural Science Foundation of China","keywords":"Zero (linguistics); Computer science; Artificial intelligence; Semantics (computer science); Shot (pellet); Mathematics; Theoretical computer science; Algorithm; Programming language; Materials science","score_opus":0.01962579135143407,"score_gpt":0.25618025371400704,"score_spread":0.23655446236257296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413275917","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":[0.020302778,0.0004581036,0.97567356,0.00026034412,0.00005555382,0.00004686795,0.00020391292,0.0020522824,0.00094662455],"genre_scores_gemma":[0.6409301,0.00059751305,0.3497105,0.00090259296,0.00015896835,0.00019523199,0.0022479414,0.0005957784,0.0046613044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989409,0.00031308387,0.00005382871,0.00038085718,0.00020191842,0.00010937881],"domain_scores_gemma":[0.998273,0.0007628365,0.00010341128,0.00053006364,0.00022247166,0.00010822042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015929426,0.0014187247,0.0018425365,0.0011791083,0.0006423618,0.001558447,0.0029094783,0.0021041448,0.0021922062],"category_scores_gemma":[0.0059204963,0.0006538613,0.0012061604,0.0010621808,0.0014580118,0.004382495,0.0050908816,0.0034842123,0.0011457037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007178357,0.00064450357,0.0020403555,0.0006898173,0.00031138287,0.00038141749,0.0004398713,0.24304861,0.053512536,0.033238955,0.012951269,0.65202343],"study_design_scores_gemma":[0.0000114095765,0.000049741775,0.00025108416,0.000026812799,0.000018706645,0.00006341371,0.000047551963,0.95793337,0.0069656507,0.033570413,0.0010444804,0.000017377843],"about_ca_topic_score_codex":0.003793533,"about_ca_topic_score_gemma":0.005843669,"teacher_disagreement_score":0.003793533,"about_ca_system_score_codex":0.0008989778,"about_ca_system_score_gemma":0.0010773682,"threshold_uncertainty_score":0.008424401},"labels":[],"label_agreement":null},{"id":"W4413407284","doi":"10.1016/j.neunet.2025.108017","title":"Rethinking softmax in incremental learning","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Softmax function; Artificial intelligence; Computer science; Incremental learning; Machine learning; Pattern recognition (psychology); Artificial neural network","score_opus":0.01647509025716623,"score_gpt":0.25348848940291946,"score_spread":0.23701339914575323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413407284","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":[0.018678054,0.0012731581,0.9764079,0.00048394423,0.00019040714,0.000037845737,0.0000835851,0.0016256679,0.001219473],"genre_scores_gemma":[0.59720904,0.0008454604,0.39320695,0.00083426,0.00025314986,0.00015165897,0.0004944314,0.0006008858,0.006404154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893326,0.00041294465,0.00006359604,0.0002995292,0.00021814168,0.000072495815],"domain_scores_gemma":[0.994749,0.003757162,0.00011804781,0.00073571416,0.00052941625,0.000110595494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002837794,0.0007609392,0.0013751659,0.00065409445,0.00052121904,0.0011748212,0.0029006675,0.0014986021,0.0029397977],"category_scores_gemma":[0.012616595,0.0006963342,0.00065800094,0.0006564548,0.0010194338,0.0038080937,0.0024730735,0.0031832051,0.0009064297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054061884,0.0003595269,0.0020804943,0.00027179447,0.00026385867,0.00012727044,0.00022803099,0.29459962,0.010597379,0.030520285,0.0072182952,0.6531927],"study_design_scores_gemma":[0.000008626929,0.00002795141,0.0001957527,0.000013346124,0.00001702724,0.000019329385,0.0000107827245,0.97772205,0.0020192438,0.01916606,0.00079043204,0.000009334394],"about_ca_topic_score_codex":0.0049173865,"about_ca_topic_score_gemma":0.008131136,"teacher_disagreement_score":0.0049173865,"about_ca_system_score_codex":0.00070181617,"about_ca_system_score_gemma":0.0010741676,"threshold_uncertainty_score":0.0150078535},"labels":[],"label_agreement":null},{"id":"W4413571590","doi":"10.1371/journal.pone.0329273","title":"PiCCL: A lightweight multiview contrastive learning framework for image classification","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Computer science; Artificial intelligence; Component (thermodynamics); Image (mathematics); Pattern recognition (psychology); Machine learning; Generalizability theory; Contextual image classification; Function (biology); Mathematics","score_opus":0.06086028497330736,"score_gpt":0.28994244486390897,"score_spread":0.2290821598906016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413571590","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":[0.0044938787,0.00023919436,0.98985785,0.00016917527,0.000046058303,0.00010144027,0.00017494109,0.0033523089,0.0015650222],"genre_scores_gemma":[0.18731348,0.00031972767,0.8017364,0.0008773411,0.00017212356,0.00046113244,0.0014794975,0.0008491122,0.0067911814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999411,0.0001241772,0.00001981841,0.00018137282,0.00019402632,0.000069562826],"domain_scores_gemma":[0.99929786,0.00017937868,0.00006935386,0.00017902168,0.00020482014,0.000069661895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014223122,0.0014991587,0.0010655548,0.0013890935,0.0005221982,0.0012737972,0.004384122,0.0015922725,0.0047137137],"category_scores_gemma":[0.0031330409,0.00055024755,0.00094181794,0.0008745131,0.0009214376,0.0024565158,0.0024986987,0.0029804292,0.0023872717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000282102,0.00036519748,0.00196114,0.00025974604,0.00022004975,0.00017422381,0.00015427473,0.24375415,0.03299911,0.032809403,0.027182782,0.65983784],"study_design_scores_gemma":[0.000018791588,0.00006241215,0.00014547247,0.000010867485,0.000009714349,0.000059038033,0.000009007166,0.9819478,0.004857096,0.010007153,0.0028611824,0.000011499606],"about_ca_topic_score_codex":0.0039821034,"about_ca_topic_score_gemma":0.00788115,"teacher_disagreement_score":0.0047137137,"about_ca_system_score_codex":0.0011483501,"about_ca_system_score_gemma":0.0012751409,"threshold_uncertainty_score":0.015768945},"labels":[],"label_agreement":null},{"id":"W4413681754","doi":"10.1007/s10994-025-06863-5","title":"Source-free domain adaptation requires penalized diversity","year":2025,"lang":"en","type":"article","venue":"Machine Learning","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; University of Calgary; Concordia University; Cybernet Systems Corporation (Canada)","funders":"Mitacs","keywords":"Adaptation (eye); Diversity (politics); Computer science; Domain adaptation; Mathematics; Artificial intelligence; Biology; Classifier (UML); Neuroscience","score_opus":0.016075978593558173,"score_gpt":0.24294979446534526,"score_spread":0.2268738158717871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413681754","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":[0.0097697275,0.00036888797,0.9882299,0.00021794831,0.00005393107,0.000030370735,0.00006740503,0.00046234005,0.00079949904],"genre_scores_gemma":[0.63421416,0.0009210391,0.35232732,0.0008679871,0.00034912824,0.00028442318,0.0012634493,0.0005836397,0.009188857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875474,0.00045525053,0.000056725745,0.0003581128,0.0002673377,0.00010790299],"domain_scores_gemma":[0.9951277,0.003202199,0.0001444498,0.0008399764,0.0004991388,0.00018650884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028249423,0.0010200245,0.0017684721,0.0007958926,0.00063582783,0.0011093793,0.002122542,0.002410615,0.0022258817],"category_scores_gemma":[0.01189956,0.0006362861,0.0009750563,0.00092812703,0.001209475,0.002835671,0.003921879,0.0034521928,0.0011064718],"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":[0.000574903,0.0002898262,0.0020324092,0.00034317223,0.00030445895,0.00037247743,0.00022181582,0.65294826,0.020583868,0.042549923,0.011184019,0.2685949],"study_design_scores_gemma":[0.0000134064685,0.000028967306,0.00022864524,0.000010034586,0.000013897302,0.000101735786,0.000014462515,0.97694415,0.002190093,0.019773576,0.0006679378,0.000012976459],"about_ca_topic_score_codex":0.0024230003,"about_ca_topic_score_gemma":0.0030560321,"teacher_disagreement_score":0.0028249423,"about_ca_system_score_codex":0.0006157635,"about_ca_system_score_gemma":0.0010257908,"threshold_uncertainty_score":0.014939904},"labels":[],"label_agreement":null},{"id":"W4413945025","doi":"10.1109/icra55743.2025.11128171","title":"ProDapt: Proprioceptive Adaptation Using Long-Term Memory Diffusion","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"California Institute of Technology; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Term (time); Adaptation (eye); Computer science; Proprioception; Diffusion; Long-term memory; Neuroscience; Psychology; Physics; Cognition","score_opus":0.030611000041066486,"score_gpt":0.28480088207800014,"score_spread":0.25418988203693366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413945025","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0151171405,0.00031077932,0.9813207,0.00014724118,0.00007272497,0.000038908165,0.000031710282,0.0016843076,0.0012765395],"genre_scores_gemma":[0.78397053,0.0003809326,0.20809206,0.00028855007,0.00008149593,0.00020838577,0.00017010496,0.0003432674,0.006464713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997415,0.00005669278,0.000018679077,0.000071788425,0.00008372005,0.000027624032],"domain_scores_gemma":[0.99896073,0.00053311494,0.000119173215,0.00017224297,0.00013905946,0.000075558106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009144124,0.0007497436,0.0010058896,0.00043568766,0.00042516843,0.000692848,0.0020303929,0.001182687,0.002076546],"category_scores_gemma":[0.0038587179,0.0003656306,0.0006583921,0.00034470856,0.0008245458,0.0015035212,0.0016480852,0.0015883091,0.00053366105],"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":[0.00017342938,0.00015137414,0.000915653,0.00014734898,0.00011854171,0.00016636236,0.00011457069,0.7860652,0.01508122,0.012204743,0.00215273,0.18270889],"study_design_scores_gemma":[0.000010415183,0.000034210017,0.000080640224,0.000003982917,0.000005544899,0.00002949192,0.0000033750296,0.9936312,0.001972299,0.0037696,0.00045205854,0.0000072980497],"about_ca_topic_score_codex":0.0034144255,"about_ca_topic_score_gemma":0.0026690287,"teacher_disagreement_score":0.0034144255,"about_ca_system_score_codex":0.00068488374,"about_ca_system_score_gemma":0.0006950914,"threshold_uncertainty_score":0.006946683},"labels":[],"label_agreement":null},{"id":"W4414094686","doi":"10.1002/cjs.70022","title":"Robust multitask feature learning with adaptive Huber regressions","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Public Health Ontario; York University; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustification; Multi-task learning; Feature selection; Outlier; Feature (linguistics); Robustness (evolution); Robust regression; Model selection; Inference","score_opus":0.022870670811128358,"score_gpt":0.22388401189151896,"score_spread":0.2010133410803906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414094686","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013554,0.00019194925,0.9853367,0.00014401982,0.000028072662,0.00004038229,0.00006705552,0.00034597656,0.00029187152],"genre_scores_gemma":[0.7374691,0.00027564284,0.25736982,0.00035730042,0.00023354962,0.00026237263,0.00056486705,0.00027256794,0.0031947468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962388,0.0018424011,0.00016949046,0.00095737644,0.00051200483,0.00027987512],"domain_scores_gemma":[0.9914004,0.0048475713,0.0010383204,0.0015531391,0.0009108838,0.00024961858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007713696,0.0017228002,0.0023370963,0.00135481,0.000598062,0.0012025755,0.002647908,0.0019467291,0.0014638465],"category_scores_gemma":[0.020234961,0.00065253786,0.001876982,0.0016396905,0.0016393852,0.0024170303,0.0021502064,0.0026865401,0.0006626611],"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":[0.00042021403,0.00022227633,0.0030104178,0.0001928807,0.00037622542,0.00023794499,0.00014014916,0.8440298,0.0063297483,0.028949844,0.0032607715,0.112829715],"study_design_scores_gemma":[0.000010857898,0.00002588914,0.00025347865,0.000003987794,0.00001034,0.000012203638,0.0000047243907,0.9902768,0.0006513823,0.008556965,0.00018325345,0.000010083016],"about_ca_topic_score_codex":0.003276084,"about_ca_topic_score_gemma":0.0024191984,"teacher_disagreement_score":0.007713696,"about_ca_system_score_codex":0.0010431281,"about_ca_system_score_gemma":0.0011718437,"threshold_uncertainty_score":0.040794373},"labels":[],"label_agreement":null},{"id":"W4414298519","doi":"10.1214/25-sts1002","title":"An Adaptive Transfer Learning Perspective on Classification in Nonstationary Environments","year":2025,"lang":"en","type":"article","venue":"Statistical Science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Perspective (graphical); Regret; Class (philosophy); Covariate; Transfer of learning; Sequence (biology); Gradient descent","score_opus":0.021870121039291233,"score_gpt":0.3174130453699864,"score_spread":0.2955429243306951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414298519","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":[0.013809484,0.00037276684,0.9820664,0.0013165056,0.000060764243,0.000050759874,0.000049519946,0.00014352429,0.0021302162],"genre_scores_gemma":[0.78058696,0.0009699054,0.20847371,0.00071264466,0.00059180503,0.00042213977,0.00021778379,0.00016006142,0.007865083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99755496,0.0012166962,0.00006138962,0.0005638466,0.00038961432,0.00021357046],"domain_scores_gemma":[0.9901259,0.0075979964,0.00070187496,0.00077728863,0.00049194606,0.00030504155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004770692,0.0013897801,0.0015807027,0.00095131714,0.00072059047,0.0017179169,0.0037767498,0.0029645888,0.0027420018],"category_scores_gemma":[0.017135656,0.00059517013,0.0010143395,0.0012384104,0.0036113432,0.004533934,0.002787115,0.0037918848,0.0005026301],"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":[0.000113590395,0.00013662342,0.0010304255,0.00009882203,0.00007897179,0.00021164565,0.00018694415,0.82536006,0.001713935,0.14316647,0.0010938399,0.026808677],"study_design_scores_gemma":[0.0000069296843,0.000033565753,0.0001332793,0.000005562288,0.0000045606103,0.000017004051,0.000012015975,0.92865074,0.0002638895,0.070572995,0.0002910832,0.000008355576],"about_ca_topic_score_codex":0.0027372804,"about_ca_topic_score_gemma":0.0014836872,"teacher_disagreement_score":0.004770692,"about_ca_system_score_codex":0.0020779595,"about_ca_system_score_gemma":0.0011026374,"threshold_uncertainty_score":0.02523017},"labels":[],"label_agreement":null},{"id":"W4414447121","doi":"10.1016/j.knosys.2025.114535","title":"Selective embedding for deep learning","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"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 Ottawa","funders":"","keywords":"Scalability; Deep learning; Robustness (evolution); Adaptability; Embedding; Raw data; Generalization; Artificial neural network; Feature engineering; Matching (statistics)","score_opus":0.016658846294248252,"score_gpt":0.2984068496853208,"score_spread":0.28174800339107253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414447121","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":[0.010519209,0.0007685523,0.9851149,0.00032239975,0.00006866658,0.000033781842,0.00016010298,0.0013886966,0.0016237344],"genre_scores_gemma":[0.5490177,0.0016616491,0.43897042,0.00045693584,0.00016072342,0.0003666502,0.0013783787,0.00039428216,0.007593286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996246,0.000088024346,0.000021023863,0.00011111499,0.00011853253,0.000036745136],"domain_scores_gemma":[0.9994648,0.00025382917,0.000045787936,0.00012282773,0.0000874147,0.000025343066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005628448,0.00096563954,0.0005323178,0.00052299217,0.00025406652,0.00064867426,0.0009074698,0.0007143779,0.0025552085],"category_scores_gemma":[0.002561575,0.0003486679,0.00044427702,0.0007205959,0.0006123004,0.0014554142,0.0013671279,0.0017480063,0.000884627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014008331,0.0001325014,0.0009392147,0.00027317216,0.00009395076,0.00010696687,0.00007994299,0.45071197,0.013778861,0.052210305,0.010600032,0.47093293],"study_design_scores_gemma":[0.000005960944,0.000029302233,0.00011499859,0.000011565006,0.000006433968,0.000020007537,0.000007258174,0.96837527,0.0040241308,0.024865253,0.0025330544,0.000006824447],"about_ca_topic_score_codex":0.0020617915,"about_ca_topic_score_gemma":0.0031771376,"teacher_disagreement_score":0.0025552085,"about_ca_system_score_codex":0.00069834746,"about_ca_system_score_gemma":0.0007371433,"threshold_uncertainty_score":0.008548081},"labels":[],"label_agreement":null},{"id":"W4414458632","doi":"10.1109/tcsvt.2025.3613262","title":"Leveraging Multi-View Images to Learn Domain-Invariant Discriminative Embeddings for Cross-View Geo-Localization","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for Central Universities of the Central South University; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Discriminative model; Robustness (evolution); Drone; Feature learning; Feature extraction; Pattern recognition (psychology); Task analysis; Feature (linguistics); Task (project management)","score_opus":0.034177720698644734,"score_gpt":0.3144990413032279,"score_spread":0.2803213206045832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414458632","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":[0.09418929,0.0010925987,0.8919757,0.00020127374,0.00015511841,0.00007570033,0.00067953835,0.008859335,0.0027713529],"genre_scores_gemma":[0.7497878,0.00060562644,0.2362005,0.00040935492,0.000086510074,0.00011344163,0.0066708424,0.00050802383,0.00561786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994628,0.00006734371,0.000019413437,0.00026079762,0.000111802634,0.000077899116],"domain_scores_gemma":[0.99943143,0.000092181916,0.00006106028,0.0002450164,0.00011992614,0.000050198178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004950626,0.0017438403,0.001110313,0.0007883133,0.00025651732,0.00078230107,0.00150389,0.00086329033,0.001789464],"category_scores_gemma":[0.0019843557,0.00043443873,0.0008619669,0.0009911248,0.0005336707,0.0020240778,0.001794036,0.0016520104,0.0017726313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000406969,0.00046024352,0.0054219365,0.00019948196,0.00022854541,0.00028766275,0.00015077188,0.1954533,0.039829884,0.004835064,0.014675815,0.7380504],"study_design_scores_gemma":[0.000030164494,0.00019667338,0.0015219185,0.000018643128,0.000036765945,0.00020985266,0.00006867185,0.9764578,0.012790607,0.0043911394,0.0042528063,0.000025036641],"about_ca_topic_score_codex":0.004165457,"about_ca_topic_score_gemma":0.007262393,"teacher_disagreement_score":0.004165457,"about_ca_system_score_codex":0.0004507893,"about_ca_system_score_gemma":0.00064049824,"threshold_uncertainty_score":0.008282423},"labels":[],"label_agreement":null},{"id":"W4414511714","doi":"10.1111/exsy.70141","title":"Traversal Learning Coordination for Lossless and Efficient Distributed Learning","year":2025,"lang":"en","type":"article","venue":"Expert Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; National Research Foundation","keywords":"Tree traversal; Lossless compression; Node (physics); Independent and identically distributed random variables; Federated learning; Distributed learning","score_opus":0.012037411736713724,"score_gpt":0.26649871420095983,"score_spread":0.2544613024642461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414511714","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":[0.010560363,0.000106621184,0.98712003,0.00019413949,0.00002534471,0.000034121076,0.000036351394,0.0012544635,0.0006685936],"genre_scores_gemma":[0.72955966,0.00010804424,0.26652372,0.0002700256,0.0000699841,0.00018771416,0.0002918362,0.0002563881,0.0027327088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975775,0.0008629299,0.00013037043,0.000648889,0.00052547903,0.00025488946],"domain_scores_gemma":[0.99445397,0.002430224,0.00039881564,0.0015816643,0.0008248271,0.00031047693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004678679,0.00080057164,0.0012189215,0.0006044504,0.0007246507,0.0017990754,0.003186681,0.0011851116,0.0025649641],"category_scores_gemma":[0.012663918,0.00050590147,0.0005075485,0.0010202957,0.0015881101,0.0033850956,0.0036729784,0.002296916,0.00078498526],"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":[0.00043536723,0.00027644556,0.0021336728,0.00010409873,0.000074988515,0.00012702878,0.00019397191,0.7304619,0.004164473,0.03769047,0.0054634265,0.21887419],"study_design_scores_gemma":[0.000017832479,0.00003844516,0.000059222228,0.0000029895684,0.000003550268,0.0000132241485,0.000012876289,0.9873372,0.0008142696,0.011146858,0.000550175,0.0000033293463],"about_ca_topic_score_codex":0.0041803974,"about_ca_topic_score_gemma":0.0045934035,"teacher_disagreement_score":0.004678679,"about_ca_system_score_codex":0.0017200707,"about_ca_system_score_gemma":0.0028421716,"threshold_uncertainty_score":0.024743557},"labels":[],"label_agreement":null},{"id":"W4414578107","doi":"10.48550/arxiv.2503.08398","title":"OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learning","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"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":"Relevance (law); Adaptation (eye); Bridge (graph theory); Range (aeronautics); Language model; Labrador Retriever","score_opus":0.04396895051009762,"score_gpt":0.28901486349468924,"score_spread":0.24504591298459164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414578107","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05675884,0.0031839756,0.8793621,0.00059849664,0.0002992599,0.00032757246,0.0007488697,0.05320252,0.005518323],"genre_scores_gemma":[0.52329105,0.0005197265,0.45804095,0.0012914081,0.00021716267,0.00028233978,0.003592399,0.0023286506,0.010436242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986879,0.00043316899,0.00005159133,0.00046013782,0.00022583305,0.00014138069],"domain_scores_gemma":[0.9984182,0.0008217914,0.00007772855,0.00038281013,0.00021199473,0.000087437744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020029338,0.0013961018,0.0014583444,0.0007781535,0.000594941,0.0015620199,0.0024274602,0.0023229623,0.0056827753],"category_scores_gemma":[0.0072197234,0.00048801219,0.00074737135,0.00067104434,0.00075728714,0.0028496913,0.0019565914,0.0022659332,0.004492944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007982314,0.00066444906,0.0019387711,0.00046412353,0.00020450678,0.0004261645,0.00026918467,0.312887,0.026236363,0.0061628297,0.031696778,0.6182516],"study_design_scores_gemma":[0.00006631641,0.0001923392,0.00034708367,0.000015159167,0.00003549746,0.00015025563,0.00006269341,0.97910106,0.00881214,0.0073511186,0.0038379987,0.000028416147],"about_ca_topic_score_codex":0.0036956486,"about_ca_topic_score_gemma":0.0068401555,"teacher_disagreement_score":0.0056827753,"about_ca_system_score_codex":0.000750491,"about_ca_system_score_gemma":0.0012791546,"threshold_uncertainty_score":0.019010723},"labels":[],"label_agreement":null},{"id":"W4414714920","doi":"10.2139/ssrn.5551786","title":"SODA: Stabilized Optimal Transport Based Domain Alignment for Domain Generalization via a Dynamic Feature Reservoir","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Generalization; Domain (mathematical analysis); Feature (linguistics); Convergence (economics); Stability (learning theory); Variance (accounting); Queue; Classification of discontinuities","score_opus":0.009430487744405984,"score_gpt":0.26299788774467897,"score_spread":0.253567400000273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414714920","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01167189,0.00011447362,0.9853951,0.00013571954,0.000073889,0.000035419078,0.00010229565,0.0016800781,0.00079109607],"genre_scores_gemma":[0.42379487,0.0001796467,0.5671876,0.000303058,0.000085654436,0.0002014287,0.0007430057,0.00062409526,0.006880671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971193,0.000056020483,0.00001499308,0.00010818144,0.000067645364,0.000041141004],"domain_scores_gemma":[0.9994603,0.00020525826,0.00003985056,0.0001540326,0.00007630463,0.00006434306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006586171,0.0007541907,0.0014827761,0.00056989404,0.00063476554,0.001009073,0.002059798,0.0018495875,0.003759223],"category_scores_gemma":[0.0019904512,0.0005213572,0.00078356644,0.00070228917,0.0010320457,0.0020805816,0.0034135527,0.0022278533,0.0013557741],"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":[0.000521726,0.00022771543,0.0008703273,0.00020930269,0.00013619299,0.00025133835,0.00017218977,0.5092623,0.047022708,0.059927322,0.011519174,0.36987963],"study_design_scores_gemma":[0.000006335968,0.000016897027,0.000031868414,0.0000025684114,0.0000031276452,0.000014293706,0.0000055104833,0.9901327,0.002022696,0.0073118377,0.00044718685,0.000005024688],"about_ca_topic_score_codex":0.0032145754,"about_ca_topic_score_gemma":0.0038155832,"teacher_disagreement_score":0.003759223,"about_ca_system_score_codex":0.00065790536,"about_ca_system_score_gemma":0.0012546488,"threshold_uncertainty_score":0.012575865},"labels":[],"label_agreement":null},{"id":"W4414798968","doi":"10.1109/tpami.2025.3614868","title":"Schedule-Robust Continual Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"","keywords":"Robustness (evolution); Schedule; Forgetting; Classifier (UML); Key (lock); Data-driven; Data stream; Data modeling","score_opus":0.020693717567211535,"score_gpt":0.2723205477773395,"score_spread":0.25162683021012794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414798968","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":[0.02378136,0.00042622563,0.9724778,0.00031743708,0.00006151452,0.000074001735,0.000107746986,0.0016392423,0.0011147851],"genre_scores_gemma":[0.8037277,0.00034733565,0.18933693,0.00049981574,0.00017998942,0.00028451305,0.0006623639,0.00039157088,0.004569705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987405,0.00030898149,0.00007356584,0.00047279528,0.00027372778,0.00013045671],"domain_scores_gemma":[0.9955987,0.0019618112,0.00045558767,0.0011004722,0.0006572567,0.00022611006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002951326,0.0011086067,0.0013351241,0.00075915275,0.0005392897,0.0008735246,0.0035258874,0.0012952036,0.001959342],"category_scores_gemma":[0.011449664,0.00060127117,0.0007772886,0.00072059565,0.0015277112,0.0024171362,0.002293254,0.0024978537,0.00076461036],"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":[0.00038946993,0.00022181806,0.0023545197,0.00021921477,0.00008124657,0.000115693365,0.0002082149,0.7504049,0.0050085844,0.014904742,0.004427169,0.22166447],"study_design_scores_gemma":[0.00001243036,0.00007401284,0.000112011374,0.000007439827,0.0000059327012,0.000026009979,0.000010577838,0.99086714,0.001032644,0.007277565,0.000566287,0.000007904299],"about_ca_topic_score_codex":0.0031137546,"about_ca_topic_score_gemma":0.0028048775,"teacher_disagreement_score":0.0035258874,"about_ca_system_score_codex":0.0010206802,"about_ca_system_score_gemma":0.0017228255,"threshold_uncertainty_score":0.015608251},"labels":[],"label_agreement":null},{"id":"W4414870974","doi":"10.21203/rs.3.rs-7653038/v1","title":"Efficient Unsupervised Domain Adaptation via Self-Supervised Vision Transformer and Synergistic Cross-Domain Alignment","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Queen's University; University of Windsor","funders":"","keywords":"Bottleneck; Domain adaptation; Transformer; Domain (mathematical analysis); Adaptation (eye); Feature (linguistics); Software deployment","score_opus":0.03697592961712569,"score_gpt":0.36164258630447016,"score_spread":0.32466665668734446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414870974","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008601657,0.00023115204,0.9884786,0.0000628598,0.000055441,0.000029240915,0.0000842816,0.0014252242,0.0010316052],"genre_scores_gemma":[0.33225212,0.00043517677,0.6578784,0.00034770175,0.00012008118,0.00013890897,0.0016021807,0.00075425574,0.006471241],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999134,0.00018442016,0.00003599939,0.0003512843,0.00019245956,0.00010182416],"domain_scores_gemma":[0.99904794,0.00022280855,0.00007501885,0.00035311794,0.00022514533,0.000076044285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008664441,0.0010654397,0.0015601618,0.00093915476,0.00051222276,0.0012006435,0.0019823585,0.0012759763,0.0025693548],"category_scores_gemma":[0.0024512773,0.00053378264,0.0010705731,0.0012796841,0.0006763274,0.0021107933,0.0028912702,0.0018355533,0.002476247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004599238,0.0004075488,0.001292503,0.0002513672,0.00022536793,0.00017104993,0.00015342164,0.11767653,0.10095778,0.011728034,0.012101417,0.7545751],"study_design_scores_gemma":[0.000016581287,0.0000611164,0.00047387122,0.000010085335,0.000029438486,0.00017634814,0.000042885553,0.9667932,0.02057587,0.009413419,0.002385616,0.000021659698],"about_ca_topic_score_codex":0.0020502196,"about_ca_topic_score_gemma":0.0035083797,"teacher_disagreement_score":0.0025693548,"about_ca_system_score_codex":0.0003667068,"about_ca_system_score_gemma":0.0010125061,"threshold_uncertainty_score":0.008595347},"labels":[],"label_agreement":null},{"id":"W4415051858","doi":"10.1145/3769733.3769740","title":"Report on the 3rd Workshop on NeuroPhysiological Approaches for Interactive Information Retrieval (NeuroPhysIIR 2025) at SIGIR CHIIR 2025","year":2025,"lang":"en","type":"article","venue":"ACM SIGIR Forum","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina; University of Toronto","funders":"","keywords":"Neurophysiology; Document retrieval; Question answering; Cognitive models of information retrieval","score_opus":0.049989618788633054,"score_gpt":0.2855186519445143,"score_spread":0.23552903315588125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415051858","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049103133,0.043188747,0.4227605,0.17727081,0.11818373,0.005365327,0.01607084,0.01056303,0.15749387],"genre_scores_gemma":[0.105588794,0.012896166,0.14696372,0.017418897,0.020001126,0.002892273,0.026688395,0.006214144,0.6613365],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9918441,0.0033471463,0.00027607745,0.0010343948,0.0025841726,0.0009140441],"domain_scores_gemma":[0.9795521,0.0053369324,0.00036866576,0.0014068395,0.00890393,0.0044315583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022629848,0.001898658,0.0013356336,0.0019706215,0.0026073484,0.006912769,0.0019824698,0.0036349143,0.075822234],"category_scores_gemma":[0.020929642,0.0006562681,0.0014274156,0.0013735299,0.0010960887,0.008330695,0.006561563,0.005822002,0.0355768],"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":[0.000538241,0.000379187,0.0007217113,0.0003070712,0.000057054913,0.00016461372,0.0012143037,0.0008154949,0.005215829,0.004649622,0.87554204,0.11039475],"study_design_scores_gemma":[0.00013067121,0.0005498704,0.0024370214,0.00031403123,0.000081702885,0.00023502723,0.0017841586,0.0043786326,0.0070303013,0.011253112,0.97167015,0.00013538438],"about_ca_topic_score_codex":0.0074275183,"about_ca_topic_score_gemma":0.011879203,"teacher_disagreement_score":0.075822234,"about_ca_system_score_codex":0.002140893,"about_ca_system_score_gemma":0.0041350224,"threshold_uncertainty_score":0.25365067},"labels":[],"label_agreement":null},{"id":"W4415204140","doi":"10.1007/978-3-032-08203-9_20","title":"Learning Structured Spatiotemporal Tasks with xLSTM Under Uncertainty: A Multi-task Approach","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Weighting; Benchmark (surveying); Task (project management); Scalability; Categorical variable; Bounding overwatch; Robotics; Variance (accounting); Reinforcement learning","score_opus":0.03748338588721219,"score_gpt":0.28239905072413085,"score_spread":0.24491566483691868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415204140","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011647278,0.0007816578,0.9855747,0.00033563504,0.000082651524,0.000026075506,0.00014601424,0.00064544793,0.0007604836],"genre_scores_gemma":[0.5852612,0.001313093,0.4033565,0.0005344564,0.00044741333,0.00019190852,0.0011852295,0.00033191056,0.007378302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995384,0.00012029053,0.00003197203,0.00018093396,0.00006728134,0.00006109018],"domain_scores_gemma":[0.99878496,0.00081984623,0.00008695951,0.00011978138,0.00012519816,0.00006328726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015268945,0.0011330319,0.0017383009,0.0005517795,0.0003803339,0.0011761654,0.0019793748,0.0022045455,0.002442248],"category_scores_gemma":[0.003815056,0.00075972127,0.0008888863,0.0011891695,0.00065140444,0.0025163514,0.0022730948,0.0026772895,0.0007319964],"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":[0.00029999486,0.00020309674,0.0007629694,0.0002923327,0.00020053363,0.00019650403,0.00015251727,0.57358897,0.01084844,0.019964013,0.0058469446,0.38764372],"study_design_scores_gemma":[0.000004298002,0.00002489841,0.00008404824,0.00000565015,0.0000076261717,0.00001550658,0.000006543284,0.9884925,0.00063997484,0.010442532,0.00027124016,0.0000052146765],"about_ca_topic_score_codex":0.003921579,"about_ca_topic_score_gemma":0.0033248605,"teacher_disagreement_score":0.003921579,"about_ca_system_score_codex":0.0006469391,"about_ca_system_score_gemma":0.00094049616,"threshold_uncertainty_score":0.008170128},"labels":[],"label_agreement":null},{"id":"W4415231940","doi":"10.2139/ssrn.5611717","title":"SODA: Stabilized Optimal Transport Based Domain Alignment for Domain Generalization via a Dynamic Feature Reservoir","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Generalization; Domain (mathematical analysis); Feature (linguistics); Stability (learning theory); Convergence (economics); Variance (accounting); Instability; Work (physics)","score_opus":0.009430487744405984,"score_gpt":0.26299788774467897,"score_spread":0.253567400000273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415231940","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01167189,0.00011447362,0.9853951,0.00013571954,0.000073889,0.000035419078,0.00010229565,0.0016800781,0.00079109607],"genre_scores_gemma":[0.42379487,0.0001796467,0.5671876,0.000303058,0.000085654436,0.0002014287,0.0007430057,0.00062409526,0.006880671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971193,0.000056020483,0.00001499308,0.00010818144,0.000067645364,0.000041141004],"domain_scores_gemma":[0.9994603,0.00020525826,0.00003985056,0.0001540326,0.00007630463,0.00006434306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006586171,0.0007541907,0.0014827761,0.00056989404,0.00063476554,0.001009073,0.002059798,0.0018495875,0.003759223],"category_scores_gemma":[0.0019904512,0.0005213572,0.00078356644,0.00070228917,0.0010320457,0.0020805816,0.0034135527,0.0022278533,0.0013557741],"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":[0.000521726,0.00022771543,0.0008703273,0.00020930269,0.00013619299,0.00025133835,0.00017218977,0.5092623,0.047022708,0.059927322,0.011519174,0.36987963],"study_design_scores_gemma":[0.000006335968,0.000016897027,0.000031868414,0.0000025684114,0.0000031276452,0.000014293706,0.0000055104833,0.9901327,0.002022696,0.0073118377,0.00044718685,0.000005024688],"about_ca_topic_score_codex":0.0032145754,"about_ca_topic_score_gemma":0.0038155832,"teacher_disagreement_score":0.003759223,"about_ca_system_score_codex":0.00065790536,"about_ca_system_score_gemma":0.0012546488,"threshold_uncertainty_score":0.012575865},"labels":[],"label_agreement":null},{"id":"W4415280187","doi":"10.1109/tpami.2025.3621631","title":"Vicinal Gaussian Transform: Rethinking Source-Free Domain Adaptation Through Source-Informed Label Consistency","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Vicinal; Covariance; Gaussian; Domain (mathematical analysis); Smoothness; Divergence (linguistics); Gaussian process; Wavelet transform","score_opus":0.029679032189561295,"score_gpt":0.28779789702884534,"score_spread":0.25811886483928403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415280187","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008926727,0.00014633939,0.9890476,0.00012869357,0.000046807236,0.000018305554,0.000057504138,0.00096910156,0.0006589425],"genre_scores_gemma":[0.35478556,0.00044872623,0.6377824,0.00056893507,0.000110886234,0.00012402218,0.0008693673,0.0011975549,0.004112634],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992926,0.0001860183,0.00003172268,0.00020921582,0.00021935563,0.00006113558],"domain_scores_gemma":[0.99836034,0.00050475704,0.00011720255,0.0005996568,0.00032576887,0.00009224432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017474156,0.0009905499,0.00083845184,0.0007178981,0.000433598,0.0013064457,0.0021116373,0.0012994342,0.0017810066],"category_scores_gemma":[0.0062229997,0.00038204744,0.0008840165,0.000883231,0.0014205445,0.0027005894,0.003120287,0.003136116,0.0013477877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002733989,0.00018644222,0.0024221607,0.00018592563,0.00014544166,0.0001690656,0.00045150012,0.38477883,0.047094494,0.044481207,0.0081787,0.51163286],"study_design_scores_gemma":[0.000012247738,0.000033036737,0.00018492517,0.000014659894,0.000009417401,0.000057008107,0.000032643784,0.9695836,0.0075678593,0.020063385,0.0024245158,0.000016810121],"about_ca_topic_score_codex":0.0026462122,"about_ca_topic_score_gemma":0.0036146634,"teacher_disagreement_score":0.0026462122,"about_ca_system_score_codex":0.00061991136,"about_ca_system_score_gemma":0.0012085624,"threshold_uncertainty_score":0.009241343},"labels":[],"label_agreement":null},{"id":"W4415357064","doi":"10.3390/vibration8040065","title":"Transfer Learning Approach for Estimating Modal Parameters of Robot Manipulators Using Minimal Experimental Data","year":2025,"lang":"en","type":"article","venue":"Vibration","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal; Université du Québec à Montréal; Natural Sciences and Engineering Research Council of Canada","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Workspace; Robot; Modal; Vibration; Modal analysis; Generalization; Stiffness; Process (computing); Payload (computing)","score_opus":0.1011574677182085,"score_gpt":0.32600242907462645,"score_spread":0.22484496135641796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415357064","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":[0.08983625,0.00027609224,0.9062888,0.00008595409,0.000029346602,0.00008512154,0.00021646662,0.001928761,0.0012531438],"genre_scores_gemma":[0.8900781,0.00011507222,0.105985016,0.00010632092,0.00003041176,0.00031644315,0.0009214974,0.00007193087,0.0023751578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967885,0.000066021246,0.000018903338,0.00012448643,0.000075319214,0.000036355363],"domain_scores_gemma":[0.99896586,0.00049529906,0.00013790576,0.00017024479,0.00019890255,0.000031835392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008007693,0.001328548,0.0004629276,0.00070269493,0.00026865926,0.00032229544,0.0010805854,0.000936819,0.0016565573],"category_scores_gemma":[0.0029701542,0.0004017896,0.0006648619,0.00038895648,0.00041889588,0.00073663314,0.00079052034,0.00094151776,0.0006722228],"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":[0.00021549445,0.00031019453,0.0042245095,0.0001509805,0.0000949489,0.00013956518,0.00008508811,0.6481206,0.020982573,0.00073595,0.0011684255,0.32377166],"study_design_scores_gemma":[0.0000053883846,0.000083528794,0.0010427806,0.0000053840386,0.000006566384,0.000017348346,0.000012828762,0.9945379,0.0030100625,0.001050374,0.00022143137,0.0000065178942],"about_ca_topic_score_codex":0.0036467058,"about_ca_topic_score_gemma":0.003514817,"teacher_disagreement_score":0.0036467058,"about_ca_system_score_codex":0.0004954578,"about_ca_system_score_gemma":0.00051629345,"threshold_uncertainty_score":0.0072509646},"labels":[],"label_agreement":null},{"id":"W4415513509","doi":"10.1016/j.engappai.2025.112910","title":"CGMAE: Self-supervised Masked Auto-Encoder with Cross-Graph node alignment for node classification","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Node (physics); Pattern recognition (psychology); Context (archaeology)","score_opus":0.023385004727657498,"score_gpt":0.2881942941159746,"score_spread":0.2648092893883171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415513509","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":[0.015044448,0.00082787324,0.9365824,0.00017730983,0.00034799526,0.00018150666,0.0019460653,0.043194745,0.0016976773],"genre_scores_gemma":[0.14457396,0.00030593673,0.8304229,0.00053020916,0.000116227064,0.00035781035,0.010573158,0.0024080477,0.010711782],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999241,0.00014253323,0.00003145054,0.0003608191,0.00014420279,0.000080031714],"domain_scores_gemma":[0.9987545,0.00034727223,0.000059150312,0.0004684477,0.00029301152,0.00007761738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008676399,0.001611237,0.0015003113,0.0013597906,0.00075298786,0.0008776472,0.0036220194,0.0018176612,0.0064524477],"category_scores_gemma":[0.002648199,0.0008327219,0.0010096364,0.0012763288,0.0005106182,0.0022006428,0.0018516629,0.002618874,0.0063904636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004982526,0.00043940998,0.0014203237,0.0002850822,0.0002607866,0.00019880717,0.00014741765,0.040361825,0.037837952,0.0063544014,0.061633274,0.8505624],"study_design_scores_gemma":[0.000052825162,0.00010590681,0.000563271,0.000024406467,0.000047405352,0.00013603282,0.000041745723,0.9618065,0.02016973,0.009433234,0.007586676,0.00003227521],"about_ca_topic_score_codex":0.008002502,"about_ca_topic_score_gemma":0.029768007,"teacher_disagreement_score":0.008002502,"about_ca_system_score_codex":0.0006561417,"about_ca_system_score_gemma":0.0016146178,"threshold_uncertainty_score":0.021585584},"labels":[],"label_agreement":null},{"id":"W4415524602","doi":"10.1109/mlsp62443.2025.11204312","title":"Tackling Distribution Shift in LLM via KILO: Knowledge-Instructed Learning for Continual Adaptation","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Adaptability; Adaptation (eye); Domain (mathematical analysis); Domain knowledge; Domain adaptation; Language model; Deep learning; Production (economics)","score_opus":0.01583959248627164,"score_gpt":0.28380989559075254,"score_spread":0.2679703031044809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415524602","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":[0.13166587,0.0016825475,0.84201396,0.0006831896,0.00027258726,0.00020600724,0.00041588102,0.019582052,0.0034780041],"genre_scores_gemma":[0.81480646,0.00041123785,0.17734641,0.0009116425,0.00009813389,0.00033274404,0.00118081,0.00065100356,0.0042615873],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994148,0.00015485614,0.000037022557,0.00026326094,0.000071569484,0.000058440175],"domain_scores_gemma":[0.9978381,0.0012933218,0.00009758977,0.00043376544,0.00021909986,0.00011819078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016100898,0.0013260721,0.00096084067,0.00066637964,0.00038382507,0.000983011,0.0026650578,0.0013960779,0.0026851515],"category_scores_gemma":[0.006206381,0.0005423072,0.00070085574,0.00053371215,0.00081847113,0.00282175,0.0028959697,0.0030552116,0.001372908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040407447,0.0006293027,0.0042861975,0.00029473117,0.00016983348,0.00024740087,0.00031178555,0.23864771,0.019173747,0.0025955185,0.006585709,0.72665405],"study_design_scores_gemma":[0.000035753477,0.00013545156,0.0004967608,0.000017769087,0.00002633891,0.00006596604,0.0000633358,0.985715,0.005219254,0.006697263,0.0015027224,0.00002438189],"about_ca_topic_score_codex":0.0033789978,"about_ca_topic_score_gemma":0.006213258,"teacher_disagreement_score":0.0033789978,"about_ca_system_score_codex":0.0006074388,"about_ca_system_score_gemma":0.0010166058,"threshold_uncertainty_score":0.008982778},"labels":[],"label_agreement":null},{"id":"W4415540034","doi":"10.1145/3746027.3754723","title":"Selective Shift: Towards Personalized Domain Adaptation in Multi-Agent Collaborative Perception","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Feature (linguistics); Perception; Domain (mathematical analysis); Representation (politics); Adaptation (eye); Domain adaptation; Focus (optics); Feature extraction","score_opus":0.03466713313071666,"score_gpt":0.31117221168944315,"score_spread":0.27650507855872647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415540034","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":[0.0152438935,0.00017115935,0.9828856,0.000082131926,0.0000409394,0.000028889915,0.0000234952,0.00060276594,0.0009210356],"genre_scores_gemma":[0.70004636,0.00022858127,0.29636005,0.0003578145,0.000078463985,0.000121328216,0.0001792961,0.00019759132,0.0024305587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991479,0.00021612836,0.000029614906,0.00029998212,0.0002154772,0.00009084936],"domain_scores_gemma":[0.9988908,0.00043141295,0.0001119555,0.000294788,0.00018637492,0.00008477232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012894137,0.0010173818,0.00096569065,0.000495416,0.00048414778,0.00091331045,0.0014847167,0.00097606296,0.0013027609],"category_scores_gemma":[0.003322971,0.00045680424,0.0007873185,0.00056097726,0.00093947875,0.002030501,0.002585439,0.0016282572,0.00057468785],"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":[0.00034961582,0.00027625108,0.0018798389,0.00013415083,0.00016099622,0.00021206697,0.0006152411,0.513165,0.045638807,0.011419532,0.0038752032,0.4222733],"study_design_scores_gemma":[0.000011210675,0.000063347,0.00030398293,0.0000046974474,0.000010647755,0.000047764544,0.000052727126,0.9878592,0.004325606,0.006242833,0.001064075,0.000013896511],"about_ca_topic_score_codex":0.0027565788,"about_ca_topic_score_gemma":0.0025774094,"teacher_disagreement_score":0.0027565788,"about_ca_system_score_codex":0.00045621826,"about_ca_system_score_gemma":0.0007478373,"threshold_uncertainty_score":0.006819129},"labels":[],"label_agreement":null},{"id":"W4415543900","doi":"10.1007/978-3-031-92870-3_18","title":"Development of a Transfer-Learning Core-Image Based Classification Approach Across Diverse Geological Settings","year":2025,"lang":"en","type":"book-chapter","venue":"Quantitative geology and geostatistics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 Alberta","funders":"","keywords":"Transfer of learning; Transferability; Intuition; Concatenation (mathematics); Knowledge base; Feature (linguistics); Benchmarking","score_opus":0.07022650790540554,"score_gpt":0.3132626863127834,"score_spread":0.2430361784073779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415543900","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":[0.0070406185,0.00013677435,0.9870816,0.00015152816,0.00004901585,0.0001286583,0.00022467913,0.0026787308,0.0025085765],"genre_scores_gemma":[0.10589868,0.00022383338,0.8829525,0.00035315802,0.000071542745,0.00026029066,0.0019699847,0.00036603567,0.00790394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991166,0.00015491243,0.000047265676,0.00034079113,0.0002474316,0.000092974195],"domain_scores_gemma":[0.9987117,0.00027149863,0.00004239588,0.0002792602,0.00061973487,0.000075439915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018831374,0.0009872909,0.0010901776,0.001500875,0.00076786877,0.0012765683,0.0033657728,0.0015883627,0.003619016],"category_scores_gemma":[0.0028313852,0.00046446646,0.0013560388,0.0018153872,0.0006476232,0.0021458247,0.003372305,0.0026434073,0.0031705918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006208361,0.00031071127,0.0013889502,0.000105036765,0.00010133252,0.00009874755,0.00018173369,0.056541536,0.015035509,0.007543253,0.012029881,0.90660125],"study_design_scores_gemma":[0.000008337714,0.000058550384,0.00080869254,0.000019098361,0.000027353331,0.00010805069,0.000110166875,0.97067237,0.008279993,0.0137047265,0.006188156,0.000014504238],"about_ca_topic_score_codex":0.010135563,"about_ca_topic_score_gemma":0.014729852,"teacher_disagreement_score":0.010135563,"about_ca_system_score_codex":0.0008405341,"about_ca_system_score_gemma":0.0014210518,"threshold_uncertainty_score":0.020153105},"labels":[],"label_agreement":null},{"id":"W4415706964","doi":"10.1109/tai.2025.3627517","title":"MaxDiv: Zero-Shot Machine Unlearning via Distributionally Divergent Erasing Samples","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Forgetting; Order (exchange); Training set; Machine translation; Empirical research","score_opus":0.07158567658853181,"score_gpt":0.31499383529742236,"score_spread":0.24340815870889054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415706964","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":[0.01808113,0.00038929403,0.9781296,0.00022766928,0.000053402116,0.000066097375,0.00011863686,0.0019041657,0.0010301169],"genre_scores_gemma":[0.515983,0.0003464844,0.47624487,0.0008373572,0.0001347479,0.00029489564,0.001163406,0.0005150508,0.0044802018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817884,0.0007536211,0.00009137571,0.0004886304,0.00034863685,0.00013884132],"domain_scores_gemma":[0.99555606,0.0025505123,0.00020974097,0.0012463862,0.0002850628,0.00015227421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033416091,0.0013282563,0.001662617,0.00096519117,0.00078419055,0.0013003758,0.0036814387,0.0017699323,0.0026233878],"category_scores_gemma":[0.01141658,0.0006087767,0.000855209,0.0008837857,0.0024208205,0.0047493116,0.005012431,0.0027935212,0.00082957617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083386543,0.0003118621,0.0017441228,0.00032228464,0.0001789574,0.0002740437,0.00036614,0.24145813,0.011890512,0.053046837,0.0071796305,0.68239355],"study_design_scores_gemma":[0.00003306957,0.0001414774,0.00014334344,0.000019447383,0.000014099835,0.00012337796,0.000033522847,0.94139636,0.008461242,0.048166547,0.0014471881,0.000020281544],"about_ca_topic_score_codex":0.0010389581,"about_ca_topic_score_gemma":0.0016554231,"teacher_disagreement_score":0.0036814387,"about_ca_system_score_codex":0.0007815302,"about_ca_system_score_gemma":0.0012921368,"threshold_uncertainty_score":0.01767236},"labels":[],"label_agreement":null},{"id":"W4415708125","doi":"10.1109/icme59968.2025.11209218","title":"Exploring Flexibility in Incremental Few-Shot Object Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Flexibility (engineering); Feature (linguistics); Object detection; Classifier (UML); Incremental learning; Object (grammar); Class (philosophy); Adaptation (eye)","score_opus":0.17186170075882612,"score_gpt":0.3208801751713076,"score_spread":0.1490184744124815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415708125","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":[0.12748747,0.0010528588,0.86735785,0.0002783964,0.000059925605,0.00008852681,0.00014797159,0.002311839,0.0012152065],"genre_scores_gemma":[0.84216577,0.00030134167,0.15474576,0.0003875602,0.000089997215,0.00009472805,0.00066667626,0.00019882388,0.0013494364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894506,0.00018477661,0.000044653174,0.00046334747,0.00023369117,0.00012850213],"domain_scores_gemma":[0.9960621,0.0024260138,0.00022290724,0.0006376532,0.0004427391,0.00020852475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021319177,0.0010573872,0.0016503478,0.0014202915,0.00058415614,0.0011434277,0.0025339734,0.0010984282,0.000816367],"category_scores_gemma":[0.008728283,0.00064513844,0.0006893763,0.0009204237,0.0010731464,0.0027567255,0.002376737,0.0016382111,0.00041105793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005372007,0.00044217208,0.007957648,0.0002579563,0.00018535454,0.00041748866,0.0005008034,0.23196243,0.04760047,0.005753874,0.004806677,0.6995779],"study_design_scores_gemma":[0.000017020457,0.00010524927,0.0015868699,0.000012721888,0.000024234034,0.00012779779,0.00005415883,0.9818559,0.005923062,0.009480285,0.0007917037,0.000021019945],"about_ca_topic_score_codex":0.00442141,"about_ca_topic_score_gemma":0.0063293,"teacher_disagreement_score":0.00442141,"about_ca_system_score_codex":0.00058590504,"about_ca_system_score_gemma":0.0007663957,"threshold_uncertainty_score":0.011274815},"labels":[],"label_agreement":null},{"id":"W4415821191","doi":"10.1109/access.2025.3628158","title":"Self-Supervised Learning Using Nonlinear Dependence","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonlinear system; Representation (politics); Kernel (algebra); Independence (probability theory); Feature learning; Variance (accounting); Feature (linguistics)","score_opus":0.038521589667102625,"score_gpt":0.33023584243174936,"score_spread":0.29171425276464674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415821191","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028054954,0.0003429392,0.9690534,0.00017973706,0.00003778695,0.0000631586,0.00009934945,0.0011577477,0.0010109227],"genre_scores_gemma":[0.7568534,0.00034133787,0.23688208,0.00046525899,0.00019082642,0.00026223724,0.0012640269,0.00033258952,0.0034081955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779475,0.000852245,0.00010997848,0.0006190802,0.0005062046,0.00011769963],"domain_scores_gemma":[0.9928042,0.0039679813,0.00063626317,0.0011815813,0.0011924009,0.00021750738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003257931,0.0010059021,0.001613797,0.0013601143,0.0005829942,0.0011057298,0.0020987692,0.0012480197,0.0012725736],"category_scores_gemma":[0.009356601,0.00044518174,0.0007766885,0.0011108292,0.0014315983,0.0023604129,0.0020410614,0.0017490841,0.0007090313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024152742,0.00041183195,0.0040045776,0.0003179078,0.00022219398,0.00014826076,0.00025732387,0.46744835,0.007746355,0.014534179,0.0088236425,0.49584386],"study_design_scores_gemma":[0.0000054285138,0.000030098574,0.00015396814,0.000005454544,0.0000051754796,0.000016285936,0.000008474696,0.9932319,0.0008573411,0.005394754,0.00028519038,0.000005951268],"about_ca_topic_score_codex":0.001432126,"about_ca_topic_score_gemma":0.0020923256,"teacher_disagreement_score":0.003257931,"about_ca_system_score_codex":0.00079315406,"about_ca_system_score_gemma":0.001326196,"threshold_uncertainty_score":0.017229795},"labels":[],"label_agreement":null},{"id":"W4415964190","doi":"10.48550/arxiv.2510.17394","title":"MILES: Modality-Informed Learning Rate Scheduler for Balancing Multimodal Learning","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Tamkeen; York University; New York University Abu Dhabi","keywords":"Multimodal learning; Modality (human–computer interaction); Multimodality; Multimodal therapy; Deep learning; Artificial neural network; Joint (building)","score_opus":0.053062393449155455,"score_gpt":0.3173625996345813,"score_spread":0.2643002061854258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415964190","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04742249,0.0016312284,0.9299842,0.00043758884,0.0002672232,0.00029016624,0.00042084194,0.016411398,0.00313483],"genre_scores_gemma":[0.6412845,0.00055460946,0.3486205,0.0007075578,0.00021779789,0.00086948957,0.0014140937,0.0014556755,0.004875723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988356,0.0003978682,0.00007319814,0.0003392075,0.00022156266,0.00013247748],"domain_scores_gemma":[0.99788135,0.000985734,0.0001444156,0.00033996475,0.0004898108,0.00015861448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034272957,0.002040177,0.0011471431,0.00084899244,0.00055340095,0.0010815478,0.0031555165,0.001893461,0.0040096305],"category_scores_gemma":[0.0136169735,0.0005512751,0.0007215944,0.0005764208,0.0007448647,0.0029874372,0.0027909956,0.0030205029,0.0021628034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012398111,0.0006341022,0.004003018,0.00031019552,0.00021563143,0.00020436662,0.0003567808,0.40582418,0.02492123,0.0075295903,0.017740084,0.5370211],"study_design_scores_gemma":[0.00007301639,0.00012589454,0.00033169048,0.000019296116,0.000022649792,0.00005091734,0.00003768773,0.98409134,0.008788299,0.004893821,0.0015346305,0.000030805597],"about_ca_topic_score_codex":0.0041829413,"about_ca_topic_score_gemma":0.0048509906,"teacher_disagreement_score":0.0041829413,"about_ca_system_score_codex":0.0011028354,"about_ca_system_score_gemma":0.0017505792,"threshold_uncertainty_score":0.018125474},"labels":[],"label_agreement":null},{"id":"W4415988375","doi":"10.1038/s41746-025-02035-w","title":"A generalizable 3D framework and model for self-supervised learning in medical imaging","year":2025,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; Vector Institute; Sunnybrook Health Science Centre; University of Toronto","funders":"Google Research; National Institute of Mental Health; National Institute on Aging; Faculty of Health Sciences, Queen's University; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, Los Angeles; Genentech; National Institutes of Health; H. Lundbeck A/S; Servier; Temerty Family Foundation; Eisai; McDonnell Center for Systems Neuroscience; Queen's University; University of Ottawa; Canada Research Chairs; Natural Sciences and Engineering Research Council of Canada; BioClinica; Biogen; Pfizer; Centre for Addiction and Mental Health Foundation; IXICO; Alliance de recherche numérique du Canada; Bristol-Myers Squibb; Government of Ontario; London Health Sciences Foundation; Northern California Institute for Research and Education; McMaster University; Novartis Pharmaceuticals Corporation; Eli Lilly and Company; National Center for Advancing Translational Sciences; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Generalizability theory; Medical imaging; Limiting; Pretext; Visualization; Medical diagnosis","score_opus":0.013154579935456661,"score_gpt":0.2818658981233288,"score_spread":0.26871131818787214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415988375","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":[0.00417771,0.00035756908,0.99164104,0.00028832824,0.000046646895,0.000069639296,0.00042508467,0.0024507383,0.0005431883],"genre_scores_gemma":[0.248155,0.0009648505,0.7337968,0.001523311,0.0002924382,0.0010167796,0.0057803397,0.0017014041,0.0067689763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911565,0.00027834397,0.000038273993,0.0003269762,0.00018030238,0.00006050621],"domain_scores_gemma":[0.99876595,0.00047382907,0.00010498446,0.00034664865,0.00022473322,0.00008374946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018801831,0.0011573562,0.0012425575,0.0011069197,0.0005014099,0.0012995648,0.004145889,0.002394184,0.0024739453],"category_scores_gemma":[0.0042835604,0.0009809714,0.0019240433,0.0011038145,0.0014346889,0.0015010587,0.0027275106,0.003349739,0.0019288971],"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":[0.00017246597,0.00015486665,0.0015640889,0.00025068346,0.00015130326,0.00013602128,0.00016558834,0.7597994,0.008375598,0.018776378,0.021196341,0.1892573],"study_design_scores_gemma":[0.0000074641193,0.000023675497,0.00012729406,0.000011129208,0.0000057693646,0.000042642805,0.000006482157,0.98842293,0.0009991875,0.00861793,0.0017264254,0.00000897235],"about_ca_topic_score_codex":0.0059084245,"about_ca_topic_score_gemma":0.008598796,"teacher_disagreement_score":0.0059084245,"about_ca_system_score_codex":0.0010860608,"about_ca_system_score_gemma":0.0013495102,"threshold_uncertainty_score":0.0117480755},"labels":[],"label_agreement":null},{"id":"W4416052529","doi":"10.1016/j.bspc.2025.109089","title":"A self-supervised framework for improved generalisability in ultrasound B-mode image segmentation","year":2025,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Providence Health Care","funders":"Engineering and Physical Sciences Research Council; UK Research and Innovation","keywords":"Segmentation; Metric (unit); Pattern recognition (psychology); Breast ultrasound; Similarity (geometry); Representation (politics); Feature learning; Supervised learning; Deep learning","score_opus":0.009560800702488017,"score_gpt":0.2845185971131794,"score_spread":0.2749577964106914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416052529","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":[0.04099278,0.00124567,0.9485453,0.0006363562,0.00011554548,0.0001545727,0.000481165,0.00589083,0.0019379195],"genre_scores_gemma":[0.5076563,0.00084859144,0.47533107,0.0016071011,0.00051265885,0.00045833204,0.0049765254,0.0016135565,0.006995974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972932,0.00085741986,0.00013405437,0.0010590656,0.00046675696,0.00018951918],"domain_scores_gemma":[0.9958106,0.0018597074,0.00042323334,0.00085613754,0.00088088855,0.00016942117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004394754,0.0016923074,0.0018193308,0.0018267131,0.00071298395,0.0014881699,0.0032581247,0.0026062129,0.001473153],"category_scores_gemma":[0.009742561,0.0006927995,0.0016830643,0.0013253526,0.0014049006,0.0018562892,0.0022270787,0.0028273142,0.001144057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062977546,0.00048721556,0.004593608,0.00029334152,0.0003163708,0.00028934865,0.00035296587,0.4227924,0.020914769,0.006916862,0.016476294,0.525937],"study_design_scores_gemma":[0.000018909013,0.000048337366,0.00036324575,0.0000126739715,0.000015449741,0.000052631793,0.000012729074,0.9928558,0.0019035822,0.0037990583,0.0009071199,0.000010541869],"about_ca_topic_score_codex":0.0069763213,"about_ca_topic_score_gemma":0.00975563,"teacher_disagreement_score":0.0069763213,"about_ca_system_score_codex":0.0011730661,"about_ca_system_score_gemma":0.0016349988,"threshold_uncertainty_score":0.023241937},"labels":[],"label_agreement":null},{"id":"W4416197322","doi":"10.1080/13658816.2025.2585320","title":"Urban region representation learning <i>via</i> dual spatial contrasts","year":2025,"lang":"en","type":"article","venue":"International Journal of Geographical Information Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Representation (politics); Dual (grammatical number); Feature (linguistics); Face (sociological concept); Feature learning; Field (mathematics)","score_opus":0.010458988736236784,"score_gpt":0.2607439047907282,"score_spread":0.2502849160544914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416197322","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":[0.045601323,0.00023547087,0.9505814,0.0003038537,0.00003206243,0.000050080977,0.00028938922,0.0012527159,0.001653709],"genre_scores_gemma":[0.7354264,0.00026103228,0.26000702,0.00033596912,0.00006137056,0.00010554386,0.0014346936,0.00020435717,0.0021634726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994313,0.00013368217,0.00001857127,0.00027867444,0.00008350544,0.00005414766],"domain_scores_gemma":[0.99892455,0.0005317135,0.00009888564,0.00023962081,0.00014162716,0.00006361794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076111726,0.0007262373,0.0008035799,0.0010274139,0.00032894535,0.0012287245,0.0020621282,0.000988562,0.0016472498],"category_scores_gemma":[0.003293006,0.00040867415,0.0011324395,0.0011988094,0.00092722947,0.003059253,0.002299633,0.0018233756,0.0005136619],"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":[0.00033392617,0.00019591361,0.0063342466,0.00029103566,0.00020116693,0.0002409835,0.0005234063,0.48148173,0.015145488,0.04067043,0.007391422,0.44719028],"study_design_scores_gemma":[0.000012229415,0.00005313082,0.00056517025,0.00001449524,0.000020658848,0.00006905255,0.000063075335,0.9746688,0.002966873,0.020095915,0.0014576273,0.000012973254],"about_ca_topic_score_codex":0.003542002,"about_ca_topic_score_gemma":0.0053021437,"teacher_disagreement_score":0.003542002,"about_ca_system_score_codex":0.00094239466,"about_ca_system_score_gemma":0.0006037189,"threshold_uncertainty_score":0.0070427656},"labels":[],"label_agreement":null},{"id":"W4416214872","doi":"10.1109/tmm.2025.3632665","title":"Rethinking the Influence of Distribution Adjustment in Incremental Semantic Segmentation","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Segmentation; Feature (linguistics); Subspace topology; Feature vector; Stability (learning theory); Incremental learning; Regularization (linguistics); Domain knowledge; Feature learning","score_opus":0.0182876295252716,"score_gpt":0.272980366762932,"score_spread":0.25469273723766045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416214872","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":[0.09355034,0.00094050745,0.8983287,0.0007563271,0.00011158916,0.00013606086,0.00021836598,0.0030353395,0.00292273],"genre_scores_gemma":[0.7501448,0.0007204023,0.24396183,0.0006609374,0.00014098812,0.00020811592,0.00095355645,0.0007384439,0.0024709136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838126,0.00036312902,0.00009018718,0.0005768702,0.0003967729,0.0001916896],"domain_scores_gemma":[0.99482995,0.0031471245,0.00027679474,0.00084197446,0.0006550266,0.0002491902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002635474,0.0012808456,0.0013725046,0.0014757196,0.00093023846,0.002348499,0.0032983285,0.001874247,0.001998125],"category_scores_gemma":[0.01544155,0.0009104416,0.001082145,0.0012965649,0.0024809085,0.0058373506,0.0038342753,0.0027716733,0.000799661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056063355,0.0003706717,0.0056443717,0.00038347565,0.00017801864,0.0003911916,0.001229646,0.41920957,0.030125527,0.02123362,0.0051260963,0.5155471],"study_design_scores_gemma":[0.000024557325,0.000058928614,0.00061893126,0.000023826802,0.000027586037,0.000084343825,0.00009358484,0.97816783,0.0060555334,0.0134437345,0.0013794638,0.000021637461],"about_ca_topic_score_codex":0.008656204,"about_ca_topic_score_gemma":0.0094435355,"teacher_disagreement_score":0.008656204,"about_ca_system_score_codex":0.0013038539,"about_ca_system_score_gemma":0.002090349,"threshold_uncertainty_score":0.017211616},"labels":[],"label_agreement":null},{"id":"W4416236655","doi":"10.1007/978-981-95-3349-7_15","title":"Continual Learning for Multilingual Neural Machine Translation via Meta-Contrastive Memory Replay","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Université de Montréal","funders":"","keywords":"Forgetting; Machine translation; Focus (optics); Adaptation (eye); Stability (learning theory); Translation (biology); State (computer science)","score_opus":0.029903435201550373,"score_gpt":0.2801117572392014,"score_spread":0.25020832203765103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416236655","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021143526,0.0012643414,0.96832556,0.00028809803,0.00025356177,0.000063201274,0.0002455443,0.0049615386,0.003454691],"genre_scores_gemma":[0.5075642,0.0007292412,0.47667634,0.0003186393,0.0003009408,0.0003254936,0.0016034519,0.001052651,0.011429078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992902,0.0002390816,0.000050904095,0.00024947527,0.000099414945,0.00007087109],"domain_scores_gemma":[0.9979773,0.0012308942,0.00008033522,0.00041691182,0.00023559539,0.000059069123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015907071,0.0011917776,0.0012402235,0.00083745294,0.0007373731,0.0013017023,0.0022345644,0.001716783,0.008844178],"category_scores_gemma":[0.004515032,0.0007248448,0.00092792674,0.0011991553,0.00094728597,0.003908462,0.0028074193,0.0025795053,0.003895924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007471696,0.00031588975,0.0003891076,0.00040748276,0.00020005052,0.00033852537,0.00030157994,0.13030544,0.02154533,0.0279106,0.011435446,0.8061034],"study_design_scores_gemma":[0.000026648308,0.00012161425,0.00010283597,0.000023962957,0.000028237715,0.00008737661,0.00005443653,0.95867854,0.00823395,0.03032216,0.0022969607,0.00002320141],"about_ca_topic_score_codex":0.0021818255,"about_ca_topic_score_gemma":0.0034366467,"teacher_disagreement_score":0.008844178,"about_ca_system_score_codex":0.0005415458,"about_ca_system_score_gemma":0.00072815,"threshold_uncertainty_score":0.029586673},"labels":[],"label_agreement":null},{"id":"W4416275249","doi":"10.1088/1742-5468/ae1214","title":"A theory of initialisation’s impact on specialisation <sup>*</sup>","year":2025,"lang":"","type":"article","venue":"Journal of Statistical Mechanics Theory and Experiment","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"National Research Foundation; Wellcome Trust; Canadian Institute for Advanced Research","keywords":"Forgetting; Premise; Artificial neural network; Entropy (arrow of time); Monotonic function; Consolidation (business); Context (archaeology)","score_opus":0.025963623199998943,"score_gpt":0.3230886621369694,"score_spread":0.29712503893697045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416275249","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":[0.282574,0.0005644186,0.68031824,0.0029225287,0.00017142452,0.00009761356,0.00018790994,0.0006747165,0.03248918],"genre_scores_gemma":[0.9723838,0.0002179641,0.022707444,0.00023269966,0.00009834546,0.00011779158,0.00009465198,0.00014228132,0.0040051197],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99932945,0.00021924076,0.00003704044,0.0001343783,0.00014930687,0.00013066841],"domain_scores_gemma":[0.9840279,0.010636164,0.0015259166,0.0018573138,0.0010282183,0.00092444685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038492135,0.0005253472,0.00072812464,0.0012867515,0.00069474167,0.0017856532,0.0018424813,0.0014938206,0.011218908],"category_scores_gemma":[0.024766855,0.00049895636,0.0008319415,0.0005846792,0.0037348345,0.004276204,0.0021890493,0.0021702151,0.0007677309],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001278398,0.000088733614,0.0019936003,0.0001573922,0.000036793674,0.00022883576,0.00039298288,0.1309627,0.0050508054,0.8395733,0.0018853708,0.019501664],"study_design_scores_gemma":[0.000012115571,0.000068240006,0.001246281,0.000029119512,0.0000146645825,0.00010135192,0.00006384394,0.5003624,0.0013531806,0.495926,0.0008021838,0.00002068872],"about_ca_topic_score_codex":0.0011367124,"about_ca_topic_score_gemma":0.0007115778,"teacher_disagreement_score":0.011218908,"about_ca_system_score_codex":0.001341655,"about_ca_system_score_gemma":0.00053775747,"threshold_uncertainty_score":0.03753096},"labels":[],"label_agreement":null},{"id":"W4416284722","doi":"10.1109/iccv51701.2025.00277","title":"Resolving Token-Space Gradient Conflicts: Token Space Manipulation for Transformer-Based Multi-Task Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Defense Acquisition Program Administration","keywords":"Overfitting; Security token; Scalability; Adaptability; Task (project management); Adaptation (eye); Limit (mathematics); Gradient descent; Artificial neural network","score_opus":0.03317788782132405,"score_gpt":0.28880050957022874,"score_spread":0.2556226217489047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416284722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015147199,0.00023033455,0.9818972,0.00017087327,0.0000530627,0.000067888424,0.00006656938,0.0013312447,0.0010355171],"genre_scores_gemma":[0.69693863,0.00022958311,0.29677853,0.0005310411,0.00007400527,0.00030168763,0.00039455498,0.0003822105,0.004369777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931777,0.00020655456,0.000044979755,0.000227048,0.00012937459,0.0000742731],"domain_scores_gemma":[0.99835324,0.00079084083,0.00014563318,0.00035991732,0.00022314508,0.00012717444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017454504,0.0010327346,0.0009969429,0.00055416184,0.00048886775,0.0009978679,0.0028184487,0.0013217095,0.0032846655],"category_scores_gemma":[0.007504236,0.0005215639,0.0006294387,0.0007148437,0.001220636,0.0038472232,0.0026800449,0.0023537423,0.0011301355],"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":[0.00053212896,0.00032603252,0.0019143948,0.00023630254,0.000101660764,0.00025633062,0.00030684142,0.4729767,0.02363971,0.027397098,0.0060993116,0.46621352],"study_design_scores_gemma":[0.000017041033,0.000049698217,0.000079279336,0.0000055952573,0.000009372081,0.000040144107,0.000014983199,0.98220694,0.0034621968,0.013322402,0.0007831033,0.000009244272],"about_ca_topic_score_codex":0.0024420968,"about_ca_topic_score_gemma":0.00400119,"teacher_disagreement_score":0.0032846655,"about_ca_system_score_codex":0.0010088144,"about_ca_system_score_gemma":0.0012744252,"threshold_uncertainty_score":0.010988295},"labels":[],"label_agreement":null},{"id":"W4416402721","doi":"10.1109/ismar-adjunct68609.2025.00177","title":"Sequential Context Engineering for Zero-Shot Recognition of Procedural Tasks in Egocentric AR","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Context (archaeology); Generalization; Encoder; Context model; Work (physics); Pattern recognition (psychology)","score_opus":0.040869292311759085,"score_gpt":0.276680115451718,"score_spread":0.2358108231399589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416402721","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":[0.023471443,0.0008034367,0.9677163,0.00011839181,0.00011056661,0.00011463727,0.00033355015,0.0052810423,0.0020507223],"genre_scores_gemma":[0.55580795,0.0007854508,0.4339673,0.00051727577,0.00014500505,0.00029109936,0.001776118,0.0005038937,0.006205966],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993672,0.00010190836,0.000024465657,0.0002669389,0.00013513403,0.00010443215],"domain_scores_gemma":[0.9994425,0.00019836139,0.000041522857,0.00016374797,0.000102559934,0.00005123385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005449075,0.0010712015,0.00085950113,0.00070654735,0.000367649,0.0009333414,0.0015237123,0.0007368238,0.003486279],"category_scores_gemma":[0.0022456078,0.0003759162,0.00080608245,0.0005273653,0.00047860463,0.0015367351,0.0017456814,0.0016538177,0.0013395796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003976539,0.0003131946,0.0016446465,0.00019912286,0.00008425236,0.00017042508,0.00030526728,0.03995508,0.056635637,0.0068926387,0.007947065,0.885455],"study_design_scores_gemma":[0.000042790714,0.00036570904,0.0026007078,0.000048596743,0.00005614128,0.0003794328,0.00019297711,0.929175,0.036095448,0.020571606,0.01041383,0.000057742327],"about_ca_topic_score_codex":0.0056962036,"about_ca_topic_score_gemma":0.013023537,"teacher_disagreement_score":0.0056962036,"about_ca_system_score_codex":0.00050091586,"about_ca_system_score_gemma":0.0009919022,"threshold_uncertainty_score":0.011662781},"labels":[],"label_agreement":null},{"id":"W4416549096","doi":"10.1016/j.knosys.2025.114942","title":"Context-aware contrastive learning via structural harmony preservation for generalized category discovery","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Graph; Feature learning; Feature (linguistics); Context (archaeology); Representation (politics)","score_opus":0.023279468905824233,"score_gpt":0.2818612461954179,"score_spread":0.2585817772895937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416549096","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040982462,0.00048295266,0.95616025,0.00013702632,0.000042483076,0.000046128534,0.00010894591,0.00084320456,0.0011964266],"genre_scores_gemma":[0.6691171,0.00029465332,0.32712317,0.00026110464,0.00008294593,0.00009364656,0.00057206384,0.00018993883,0.0022653567],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992311,0.00016037651,0.00004134954,0.00035310455,0.00014083796,0.000073307056],"domain_scores_gemma":[0.9985103,0.00073539594,0.000099196965,0.00037140676,0.00019264838,0.000091181595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009855316,0.0005965994,0.0014542727,0.0013537639,0.00074311346,0.0011685343,0.0026843972,0.0013078786,0.0021269266],"category_scores_gemma":[0.0038834878,0.00045975097,0.0008052303,0.0012764758,0.0011062435,0.0030006075,0.0035083864,0.0018042129,0.0005868565],"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":[0.0005812808,0.0004351002,0.0021773637,0.00028819166,0.00026817826,0.00021299609,0.00034837454,0.09336322,0.043127004,0.02578928,0.0039501768,0.8294588],"study_design_scores_gemma":[0.000020097614,0.000089085166,0.00065852905,0.000014028069,0.000049068552,0.000114651375,0.000060010185,0.9512897,0.008607994,0.03802752,0.0010462344,0.000023047742],"about_ca_topic_score_codex":0.0026966326,"about_ca_topic_score_gemma":0.004694142,"teacher_disagreement_score":0.0026966326,"about_ca_system_score_codex":0.00052699744,"about_ca_system_score_gemma":0.00076597626,"threshold_uncertainty_score":0.0071153045},"labels":[],"label_agreement":null},{"id":"W4416582718","doi":"10.1109/tcss.2025.3621148","title":"A Stitch in Time Saves Nine: Progressive Information Bottleneck for Incremental Multiview Clustering","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Henan Province; National Natural Science Foundation of China","keywords":"Bottleneck; Cluster analysis; Information bottleneck method; Benchmark (surveying); Consistency (knowledge bases); Discriminative model; Encoder; Knowledge acquisition; Knowledge base","score_opus":0.019131941090006366,"score_gpt":0.2913828968729657,"score_spread":0.2722509557829593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416582718","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":[0.019551288,0.00055928994,0.9781392,0.00017275382,0.00004110015,0.00003849747,0.00008067773,0.0005420992,0.0008750606],"genre_scores_gemma":[0.64848787,0.0007238461,0.34590724,0.00036031872,0.00015594081,0.00020638261,0.0007221138,0.00031693006,0.0031193427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991178,0.00015934868,0.00006125543,0.00025613903,0.00029033405,0.000115137096],"domain_scores_gemma":[0.9980211,0.0007532796,0.00019793748,0.0003741799,0.0005153607,0.00013807263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015208675,0.0010315827,0.00139086,0.0009906344,0.00064055866,0.0013171422,0.0027225956,0.0010935523,0.0016388061],"category_scores_gemma":[0.0069368184,0.0006017129,0.000658975,0.0012708724,0.0009909737,0.0031424894,0.0022172737,0.0016534282,0.0004963921],"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":[0.00049903826,0.00020830723,0.0022712194,0.00029062832,0.00014213625,0.00024077523,0.000514715,0.49700722,0.026764225,0.033739775,0.0067556626,0.43156636],"study_design_scores_gemma":[0.000011134787,0.000055904795,0.00024383377,0.000011597244,0.000015952586,0.000044123142,0.000024891897,0.98701894,0.0041948375,0.0072432784,0.0011180127,0.000017439887],"about_ca_topic_score_codex":0.0062401406,"about_ca_topic_score_gemma":0.0051087197,"teacher_disagreement_score":0.0062401406,"about_ca_system_score_codex":0.0010696775,"about_ca_system_score_gemma":0.0017549144,"threshold_uncertainty_score":0.012407601},"labels":[],"label_agreement":null},{"id":"W4416726377","doi":"10.1109/mwscas53549.2025.11244442","title":"A Novel Continual Learning Approach for Robust Medical Image Segmentation","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Segmentation; Hausdorff distance; Image segmentation; Pattern recognition (psychology); Domain (mathematical analysis); Generalization; Benchmark (surveying); Scheme (mathematics)","score_opus":0.0299322745623758,"score_gpt":0.29329243332216376,"score_spread":0.26336015875978797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416726377","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":[0.022891365,0.00035993394,0.97347057,0.00021565217,0.000040298903,0.000056587414,0.00009211272,0.001717455,0.0011560192],"genre_scores_gemma":[0.5080677,0.00039605572,0.48543388,0.00048345266,0.00012829085,0.00013200963,0.0006447859,0.0003746906,0.0043391585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993469,0.00010876947,0.000035406527,0.00023948311,0.0002067906,0.000062586565],"domain_scores_gemma":[0.99899596,0.00028519437,0.000116983494,0.00031628378,0.00021217042,0.00007342959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014594322,0.0006124148,0.0008434862,0.0011580389,0.0004870584,0.000936071,0.0020985354,0.0014655964,0.0016453344],"category_scores_gemma":[0.003152518,0.00046710155,0.00084169814,0.0008136756,0.0012782448,0.0016801658,0.0025178888,0.0015056782,0.0007578957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000463144,0.00021779501,0.0023397827,0.00019445812,0.00012274172,0.00034428656,0.00030306756,0.2695478,0.04837037,0.010045388,0.004078568,0.6639727],"study_design_scores_gemma":[0.000010510845,0.000081622544,0.0003787092,0.000012223518,0.000013052285,0.00023573417,0.00002104266,0.979481,0.01144253,0.006474606,0.0018288416,0.000020147025],"about_ca_topic_score_codex":0.0018905426,"about_ca_topic_score_gemma":0.0024442875,"teacher_disagreement_score":0.0020985354,"about_ca_system_score_codex":0.00075717026,"about_ca_system_score_gemma":0.0009704309,"threshold_uncertainty_score":0.0077183247},"labels":[],"label_agreement":null},{"id":"W4416960172","doi":"10.48550/arxiv.2512.01405","title":"Fantastic Features and Where to Find Them: A Probing Method to combine Features from Multiple Foundation Models","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Alliance de recherche numérique du Canada","keywords":"Scalability; Exploit; Hyperparameter; Focus (optics); Transformer; Feature (linguistics); Robustness (evolution); Artificial neural network; Joint (building)","score_opus":0.062390518847791475,"score_gpt":0.3089751941152041,"score_spread":0.2465846752674126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416960172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069562405,0.0007358562,0.9057141,0.00091272837,0.00018665373,0.00013283038,0.001091416,0.017430196,0.004233862],"genre_scores_gemma":[0.5651051,0.00035498303,0.42224327,0.0007289012,0.000105739244,0.00021534988,0.0033070438,0.0017660896,0.0061735595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993175,0.00013686289,0.00003111024,0.00029042285,0.000116313306,0.000107821594],"domain_scores_gemma":[0.9987037,0.00036702934,0.00011578249,0.00056381006,0.00013705196,0.00011260231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016101527,0.002333568,0.0010893745,0.0010011776,0.0005740238,0.001521247,0.0037918882,0.001887219,0.004132755],"category_scores_gemma":[0.004971943,0.0010080463,0.0016089787,0.0012638019,0.0010959802,0.005118756,0.0043019056,0.0032515314,0.0020035973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008405286,0.0003616144,0.00665248,0.00026897847,0.0003239596,0.00043082898,0.00040150766,0.1734616,0.028951604,0.015417684,0.026819265,0.74606997],"study_design_scores_gemma":[0.00004340845,0.00010600324,0.00048521897,0.000018676365,0.000055674383,0.00010021485,0.000044650336,0.9694742,0.0069139875,0.019426972,0.0033040622,0.00002700126],"about_ca_topic_score_codex":0.0036470005,"about_ca_topic_score_gemma":0.00561953,"teacher_disagreement_score":0.004132755,"about_ca_system_score_codex":0.0009377791,"about_ca_system_score_gemma":0.0015015675,"threshold_uncertainty_score":0.013825417},"labels":[],"label_agreement":null},{"id":"W4417054089","doi":"10.23919/eusipco63237.2025.11226338","title":"Analyzing the Impact of Low-Rank Adaptation for Cross-Domain Few-Shot Object Detection in Aerial Images","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Object detection; Adaptation (eye); Object (grammar); Code (set theory); Aerial image; Object-class detection; Aerial imagery","score_opus":0.021364523997819077,"score_gpt":0.3261576337994757,"score_spread":0.30479310980165664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417054089","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":[0.1883124,0.0039551216,0.7940844,0.00094508805,0.00043381815,0.00022185236,0.0007636827,0.007348357,0.003935233],"genre_scores_gemma":[0.7108649,0.0007527187,0.27723294,0.00095573923,0.00021452605,0.00016233386,0.003428599,0.0009023404,0.005485876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980603,0.0006025976,0.00009046833,0.00065171253,0.00037013463,0.00022486548],"domain_scores_gemma":[0.9944353,0.0034403373,0.00025803156,0.00096760545,0.0006326324,0.00026602516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004873484,0.0018626562,0.0013355318,0.0011392685,0.0007516419,0.0016868856,0.0016186368,0.0019076883,0.002288414],"category_scores_gemma":[0.021586461,0.0005149365,0.0010214278,0.0008608642,0.0012119556,0.0027873272,0.0018763954,0.0029243897,0.0014188545],"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":[0.00093647646,0.00057930563,0.009979022,0.000622917,0.0006047985,0.0003165119,0.00025013665,0.5264108,0.03691408,0.005157881,0.013780033,0.40444803],"study_design_scores_gemma":[0.000025321136,0.00012835782,0.0017756983,0.000024081912,0.00003073712,0.00014871106,0.000055086362,0.9867335,0.0075122323,0.0024575512,0.0010825319,0.000026257374],"about_ca_topic_score_codex":0.008834494,"about_ca_topic_score_gemma":0.014092557,"teacher_disagreement_score":0.008834494,"about_ca_system_score_codex":0.0008181409,"about_ca_system_score_gemma":0.0011231883,"threshold_uncertainty_score":0.025773764},"labels":[],"label_agreement":null},{"id":"W4417077291","doi":"10.48550/arxiv.2505.23027","title":"Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Robustness (evolution); Training set; Minification; Ensemble learning; Feature (linguistics); Pattern recognition (psychology); Extractor; Empirical risk minimization; Support vector machine","score_opus":0.05673509766038131,"score_gpt":0.30157178847062643,"score_spread":0.24483669081024512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417077291","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22708854,0.0011567341,0.76574117,0.00050992327,0.00014503756,0.00010485541,0.00017719758,0.0022732643,0.002803312],"genre_scores_gemma":[0.88331056,0.00023817967,0.11196715,0.0005524087,0.00013829922,0.00013188185,0.00082083675,0.0002954497,0.0025453256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815613,0.00058325706,0.000111343,0.00061662856,0.0003612815,0.00017130203],"domain_scores_gemma":[0.99431336,0.0023789904,0.0003484376,0.001818759,0.0008278499,0.00031259313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003957523,0.0012167623,0.0015997385,0.0010760941,0.0010110919,0.001238808,0.0018120739,0.0016009087,0.001029539],"category_scores_gemma":[0.015636014,0.0004906887,0.000933457,0.00063685,0.00091067055,0.0028937168,0.0034383752,0.0024065592,0.00066264876],"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":[0.00038127787,0.0002966577,0.017384902,0.00012826122,0.00038960035,0.0001984619,0.00059367256,0.57292163,0.0158322,0.00913626,0.009316167,0.37342092],"study_design_scores_gemma":[0.000013375931,0.000080292186,0.0011169435,0.000014930599,0.000027031774,0.00006781053,0.00006259104,0.98625934,0.003368692,0.0078210905,0.0011524381,0.00001542523],"about_ca_topic_score_codex":0.0017816264,"about_ca_topic_score_gemma":0.0031173548,"teacher_disagreement_score":0.003957523,"about_ca_system_score_codex":0.0005791782,"about_ca_system_score_gemma":0.0008257854,"threshold_uncertainty_score":0.020929635},"labels":[],"label_agreement":null},{"id":"W4417089364","doi":"10.1109/iccv51701.2025.00301","title":"Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Bell (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overfitting; Pruning; Hyperparameter; Key (lock); Selection (genetic algorithm); Adaptation (eye); Randomness; Generalization","score_opus":0.032038336753782495,"score_gpt":0.2761377671376563,"score_spread":0.24409943038387383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417089364","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":[0.049239688,0.000973919,0.9429268,0.00045914119,0.00013872342,0.00011251374,0.0001858654,0.003357898,0.0026053532],"genre_scores_gemma":[0.676624,0.0007459818,0.3137463,0.0008699962,0.00023247759,0.00021274324,0.0017102095,0.00062550476,0.0052327346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985012,0.0005577581,0.0000653837,0.0004740033,0.00027266628,0.00012897038],"domain_scores_gemma":[0.9969254,0.0019202131,0.00015251362,0.00057415554,0.00028032376,0.0001473755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021825493,0.0015032758,0.0016856879,0.0006055263,0.00060055184,0.0012280372,0.0017857975,0.0017758253,0.0022178276],"category_scores_gemma":[0.0114364335,0.0006398799,0.0009897933,0.0006799839,0.001224193,0.002764071,0.0020946097,0.0033493584,0.0013920068],"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":[0.0005332929,0.0005236351,0.0020681343,0.0003541091,0.00024572472,0.00021192715,0.00028878287,0.6152051,0.015199865,0.010887988,0.0097479,0.34473357],"study_design_scores_gemma":[0.00001606481,0.00005453897,0.00018355735,0.000008679383,0.0000106381485,0.000043576783,0.000023044127,0.99163437,0.002289088,0.005213852,0.00050908077,0.0000135539985],"about_ca_topic_score_codex":0.006545035,"about_ca_topic_score_gemma":0.008678996,"teacher_disagreement_score":0.006545035,"about_ca_system_score_codex":0.00064142863,"about_ca_system_score_gemma":0.0011654772,"threshold_uncertainty_score":0.013013899},"labels":[],"label_agreement":null},{"id":"W4417150083","doi":"10.1016/j.knosys.2025.115015","title":"Multi-level alignment network for unsupervised domain adaptive multi-modality object re-identification","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Lakeridge Health; Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Anhui Province; Anhui University; National Natural Science Foundation of China","keywords":"Domain (mathematical analysis); Object (grammar); Consistency (knowledge bases); Modality (human–computer interaction); Cluster analysis; Pattern recognition (psychology); Enhanced Data Rates for GSM Evolution","score_opus":0.0810843789179939,"score_gpt":0.3194205932357736,"score_spread":0.23833621431777968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417150083","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":[0.007483547,0.00044446354,0.98976713,0.00011295239,0.000045484725,0.00003674834,0.00010725023,0.0012780874,0.0007242771],"genre_scores_gemma":[0.4084855,0.00078633026,0.5785002,0.00057971675,0.000143975,0.00024854168,0.0014157596,0.00044006086,0.009399843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999328,0.00011989898,0.00003095865,0.00031045763,0.00012769243,0.00008291263],"domain_scores_gemma":[0.99928266,0.0002488653,0.00008232802,0.00016100699,0.00017923128,0.000045890185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010245182,0.0009607011,0.0013441279,0.0009869819,0.00059416244,0.0008223679,0.0022906382,0.0018494873,0.0027306837],"category_scores_gemma":[0.0023598503,0.00056532066,0.001033435,0.0013779307,0.000582692,0.0017494063,0.0020819323,0.001993587,0.0017154295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036998265,0.00030670592,0.0011537566,0.00017835348,0.00024190558,0.0002460722,0.00015127327,0.14957412,0.048737507,0.007066616,0.0065409723,0.78543276],"study_design_scores_gemma":[0.0000049627174,0.00003092767,0.00044865216,0.000008684411,0.000024401252,0.000058169666,0.000018245652,0.987324,0.006714074,0.0043053566,0.0010498702,0.000012628708],"about_ca_topic_score_codex":0.0063429866,"about_ca_topic_score_gemma":0.009279566,"teacher_disagreement_score":0.0063429866,"about_ca_system_score_codex":0.0006707017,"about_ca_system_score_gemma":0.00083856256,"threshold_uncertainty_score":0.012612104},"labels":[],"label_agreement":null},{"id":"W4417477756","doi":"10.1007/s44275-025-00035-2","title":"DA: towards distribution adaptive test-time adaptation in dynamic wild world","year":2025,"lang":"en","type":"article","venue":"Moore and More","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Tsinghua University; Shenzhen Technology University","keywords":"Normalization (sociology); Robustness (evolution); Test data; Batch processing; Adaptation (eye); Database normalization; Dynamic data","score_opus":0.01131066449168065,"score_gpt":0.25472052485508934,"score_spread":0.2434098603634087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417477756","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":[0.02083128,0.00014572458,0.97612894,0.0001337415,0.000055102646,0.00003320069,0.00007261559,0.0020595677,0.0005398718],"genre_scores_gemma":[0.6777113,0.00021666478,0.3168231,0.00044891456,0.00012813223,0.00015026849,0.0006478376,0.00062011264,0.0032536979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989706,0.0003043344,0.000052105508,0.0003699377,0.00019195721,0.00011100704],"domain_scores_gemma":[0.99746835,0.0011139512,0.00019176216,0.0005318711,0.00052194105,0.00017220869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020180736,0.0010182137,0.0011506901,0.0008855973,0.0004664551,0.0010138917,0.0027637056,0.001110424,0.0013682252],"category_scores_gemma":[0.007695079,0.0005484735,0.000732967,0.0008460233,0.0011069989,0.0025141095,0.0021222369,0.0025655278,0.00081540016],"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":[0.00033377687,0.00032004283,0.004577914,0.00009644044,0.00016022557,0.00021392759,0.00021116342,0.5407932,0.019090645,0.009190392,0.0060723494,0.41893992],"study_design_scores_gemma":[0.0000040550776,0.000012948361,0.00025125232,0.0000018312388,0.0000049033183,0.000020532141,0.000009493627,0.9945182,0.0019606077,0.00282488,0.00038449868,0.0000067227634],"about_ca_topic_score_codex":0.0069946153,"about_ca_topic_score_gemma":0.004961685,"teacher_disagreement_score":0.0069946153,"about_ca_system_score_codex":0.0008573152,"about_ca_system_score_gemma":0.0009768612,"threshold_uncertainty_score":0.01390779},"labels":[],"label_agreement":null},{"id":"W64027530","doi":"10.1007/978-3-642-36657-4_1","title":"Deep Learning of Representations","year":2013,"lang":"en","type":"book-chapter","venue":"Intelligent systems reference library","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Initialization; Deep learning; Machine learning; Hierarchy; Representation (politics); Unsupervised learning; Feature learning; Generative grammar; Generative model; Task (project management); Engineering","score_opus":0.05009056090314877,"score_gpt":0.255337457136969,"score_spread":0.2052468962338202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W64027530","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036200928,0.009163055,0.9594305,0.0012617711,0.00040827558,0.000029329922,0.00056199206,0.0016769848,0.023848033],"genre_scores_gemma":[0.21762784,0.021390805,0.58584857,0.0010295016,0.00075458636,0.00022340922,0.0066791787,0.0012369509,0.16520922],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997688,0.000040884024,0.000011389912,0.00008516819,0.00007391797,0.000019808684],"domain_scores_gemma":[0.9997274,0.00009440067,0.000013818692,0.000091817674,0.000057373378,0.000015164407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004253985,0.0008033859,0.00060967065,0.00061809423,0.00025074533,0.0013495989,0.0012741382,0.00091311213,0.009554281],"category_scores_gemma":[0.0014755384,0.00052317994,0.00060193206,0.0010232243,0.00064458774,0.0023283227,0.0013070599,0.0025151812,0.0047222357],"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":[0.000037577378,0.000049663486,0.0001879654,0.0002474548,0.000057845733,0.000027352911,0.000054095224,0.059165336,0.004082574,0.12912287,0.04718217,0.7597852],"study_design_scores_gemma":[0.000011126017,0.000044236764,0.0004419007,0.00017791393,0.000036873567,0.00010766118,0.00003426891,0.5520855,0.009272671,0.34353483,0.09421865,0.00003426832],"about_ca_topic_score_codex":0.0027051123,"about_ca_topic_score_gemma":0.0044203675,"teacher_disagreement_score":0.009554281,"about_ca_system_score_codex":0.00091012806,"about_ca_system_score_gemma":0.0006633954,"threshold_uncertainty_score":0.031962216},"labels":[],"label_agreement":null},{"id":"W6888580618","doi":"10.21227/jp37-y129","title":"HITL UAV DoS GPS Spoofing Attacks (MAVLink)","year":2020,"lang":"en","type":"dataset","venue":"IEEE DataPort","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Autopilot; Global Positioning System; Flight plan; Spoofing attack; Data collection; Plan (archaeology); Software","score_opus":0.042345391356899076,"score_gpt":0.2896495723318982,"score_spread":0.24730418097499912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888580618","genre_codex":"software","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21824288,0.0015460354,0.15039812,0.0015711805,0.0016998445,0.0012430695,0.162201,0.2739584,0.18913953],"genre_scores_gemma":[0.7142364,0.00068424013,0.07194249,0.00051802007,0.000070694456,0.0010989527,0.16742814,0.012309752,0.0317113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997334,0.000040691626,0.0000139599615,0.000048818125,0.000096662385,0.00006650994],"domain_scores_gemma":[0.99969816,0.0000735293,0.000024562283,0.00007058657,0.000095017414,0.00003818384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034594626,0.0011125639,0.00049551256,0.00057472975,0.00037670898,0.0007510218,0.0012460207,0.000628891,0.027815776],"category_scores_gemma":[0.0013104292,0.0002861619,0.00048097828,0.00043870253,0.00018559807,0.0009135411,0.0009059241,0.001147013,0.008006387],"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":[0.0009849741,0.00028207942,0.011397637,0.0012157798,0.00018341794,0.00076539244,0.00041546373,0.4811329,0.00989656,0.00880284,0.43514836,0.04977466],"study_design_scores_gemma":[0.00028967444,0.00032217448,0.004472425,0.00011002037,0.000052354982,0.00016156748,0.0001706247,0.8269354,0.012238713,0.0033851597,0.15180714,0.00005472155],"about_ca_topic_score_codex":0.008142628,"about_ca_topic_score_gemma":0.007890204,"teacher_disagreement_score":0.027815776,"about_ca_system_score_codex":0.00058791856,"about_ca_system_score_gemma":0.00051199016,"threshold_uncertainty_score":0.0930531},"labels":[],"label_agreement":null},{"id":"W6891723697","doi":"10.48448/rqwd-cy29","title":"Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation","year":2023,"lang":"en","type":"other","venue":"Open MIND","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Set (abstract data type); Identification (biology); Process (computing); Quality (philosophy); Term (time)","score_opus":0.12230406977872929,"score_gpt":0.37280912548784056,"score_spread":0.2505050557091113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6891723697","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49275893,0.0973678,0.28304762,0.003478925,0.005464272,0.0044818074,0.013275738,0.06494079,0.035184097],"genre_scores_gemma":[0.74680454,0.0063780993,0.20060754,0.002306236,0.0007678393,0.0015048309,0.027471906,0.0033751086,0.0107839415],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99107754,0.0033230162,0.0006138145,0.0025654535,0.0017294004,0.00069079135],"domain_scores_gemma":[0.9856867,0.0072162044,0.0005441705,0.003833243,0.0018130718,0.0009066249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013224097,0.0051103197,0.003609748,0.0027894091,0.0012991466,0.0031117704,0.0051887333,0.0053807744,0.005035411],"category_scores_gemma":[0.033988565,0.0009065256,0.0019798628,0.001560881,0.0019651272,0.0066442466,0.0038786593,0.004071628,0.004126709],"study_design_candidate":"bench_or_experimental","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":[0.008145208,0.0034153485,0.009672969,0.0048832134,0.002272206,0.00054158125,0.00043466777,0.26563445,0.01674694,0.0026979295,0.04938187,0.63617367],"study_design_scores_gemma":[0.0012662886,0.0057205055,0.010786219,0.00072525063,0.0007831568,0.0013880893,0.0006086869,0.9268914,0.023567827,0.008080246,0.019828243,0.00035422022],"about_ca_topic_score_codex":0.012701834,"about_ca_topic_score_gemma":0.014552289,"teacher_disagreement_score":0.013224097,"about_ca_system_score_codex":0.0022380946,"about_ca_system_score_gemma":0.0018099559,"threshold_uncertainty_score":0.069936514},"labels":[],"label_agreement":null},{"id":"W6901860161","doi":"10.60692/kervm-vp575","title":"Pre-trained Models for SMP Classification and Segmentation","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Random forest; Mixture model; Naive Bayes classifier; Python (programming language); Artificial neural network; Classifier (UML); Bayesian probability; Pattern recognition (psychology); Segmentation","score_opus":0.06226005648060976,"score_gpt":0.23799217663881267,"score_spread":0.17573212015820291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901860161","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13146476,0.0070949825,0.34454486,0.002536955,0.0040629837,0.0013147937,0.34156027,0.12639417,0.041026264],"genre_scores_gemma":[0.23400438,0.001483725,0.1861936,0.0010298645,0.00038462592,0.0014897431,0.5407163,0.0043229936,0.030374758],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990804,0.00012991858,0.000048533286,0.00040105963,0.0001704092,0.0001696002],"domain_scores_gemma":[0.9991617,0.0002558612,0.00003728598,0.00020461675,0.00029338495,0.00004713228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009096651,0.00335018,0.0010234651,0.0015427866,0.00069129776,0.0015237507,0.002516513,0.0021868162,0.017405357],"category_scores_gemma":[0.0037773133,0.0007376264,0.0027185215,0.0015789316,0.00053659594,0.001720814,0.0012378219,0.00392328,0.0267576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000617359,0.00050190935,0.005970632,0.0008257293,0.00033395365,0.00042575455,0.00020830064,0.20557736,0.008856366,0.002746399,0.38706237,0.38687387],"study_design_scores_gemma":[0.00008765931,0.00015189536,0.0051668934,0.00029069238,0.000104304905,0.0002136423,0.00022145417,0.89595646,0.013351957,0.005676197,0.07867861,0.0001003433],"about_ca_topic_score_codex":0.0341584,"about_ca_topic_score_gemma":0.05000118,"teacher_disagreement_score":0.0341584,"about_ca_system_score_codex":0.0018141132,"about_ca_system_score_gemma":0.002065474,"threshold_uncertainty_score":0.067919195},"labels":[],"label_agreement":null},{"id":"W6906181993","doi":"10.15468/dl.zen6xj","title":"Occurrence Download","year":2025,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"Matching (statistics); Range (aeronautics); Set (abstract data type); Identification (biology); Download","score_opus":0.014155935306650555,"score_gpt":0.22522081124054477,"score_spread":0.21106487593389422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6906181993","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015117526,0.00006728757,0.00011740555,0.000060954113,0.000024639294,0.000012206668,0.9970251,0.0013545825,0.0011865934],"genre_scores_gemma":[0.00023325745,0.000036072586,0.00034399374,0.00004461253,0.0000036264594,0.000041795003,0.9986557,0.00015351061,0.00048740767],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990802,0.00012083428,0.00010489418,0.00033222948,0.00021968078,0.00014213432],"domain_scores_gemma":[0.99828964,0.00042983438,0.00012868336,0.0005076874,0.00042913912,0.00021507086],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007987474,0.002579531,0.0013444538,0.0040576337,0.0010007075,0.0020469762,0.0030458057,0.0020017542,0.09527729],"category_scores_gemma":[0.0048792893,0.00077469787,0.0012962599,0.00720875,0.00046249598,0.00208863,0.0023222463,0.0019631344,0.16467415],"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":[0.000031621796,0.000018381244,0.00034250872,0.00038619974,0.000012848206,0.000017783748,0.00001935034,0.00017051921,0.00013102958,0.0003207417,0.99649847,0.0020505937],"study_design_scores_gemma":[0.000093534465,0.000016848066,0.0020724176,0.00015297759,0.000015600217,0.00007345953,0.00008463071,0.0005234638,0.00037417174,0.0010702002,0.99550134,0.000021303109],"about_ca_topic_score_codex":0.022462785,"about_ca_topic_score_gemma":0.04430378,"teacher_disagreement_score":0.9047227,"about_ca_system_score_codex":0.0015671493,"about_ca_system_score_gemma":0.0021582688,"threshold_uncertainty_score":0.31873423},"labels":[],"label_agreement":null},{"id":"W6920283708","doi":"10.60692/2gknt-gd342","title":"Reinforced Training Data Selection for Domain Adaptation","year":2019,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Selection (genetic algorithm); Domain (mathematical analysis); Generator (circuit theory); Set (abstract data type); Dependency (UML); Training set; Adaptation (eye); Domain adaptation; Reinforcement learning","score_opus":0.09651272768991555,"score_gpt":0.2432969266735265,"score_spread":0.14678419898361095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920283708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030775966,0.00023009507,0.9660293,0.00020313139,0.00004914716,0.00012737242,0.00009338596,0.0015950194,0.0008965424],"genre_scores_gemma":[0.7348948,0.00016946277,0.26093948,0.00038668414,0.000067182365,0.00044041147,0.0005847433,0.00023088184,0.0022863222],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882764,0.0005012699,0.000058874364,0.0003870384,0.000154961,0.00007036602],"domain_scores_gemma":[0.9968303,0.0017305493,0.00016487745,0.00074867316,0.00039919696,0.00012652508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024932069,0.0008430692,0.0010126927,0.0006805404,0.00040629043,0.0005678215,0.001997188,0.0009360157,0.0014467934],"category_scores_gemma":[0.008937778,0.00044588587,0.00058606337,0.00059408596,0.0009581393,0.0018275777,0.001895551,0.0020330728,0.00066842866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035897168,0.0005710741,0.005940505,0.00018371979,0.00017694574,0.00020405486,0.00034684394,0.419559,0.022016404,0.012464872,0.0061152335,0.53206235],"study_design_scores_gemma":[0.000022157781,0.000061404455,0.00042555924,0.000008262042,0.000012776368,0.00004181271,0.000022248725,0.98629564,0.004417381,0.0075816326,0.0010994461,0.000011715648],"about_ca_topic_score_codex":0.001839842,"about_ca_topic_score_gemma":0.0028042414,"teacher_disagreement_score":0.0024932069,"about_ca_system_score_codex":0.00070788315,"about_ca_system_score_gemma":0.0011260788,"threshold_uncertainty_score":0.013185501},"labels":[],"label_agreement":null},{"id":"W6929503800","doi":"10.48620/77323","title":"Prior knowledge-guided vision-transformer-based unsupervised domain adaptation for intubation prediction in lung disease at one week.","year":2024,"lang":"en","type":"article","venue":"Open Access CRIS of the University of Bern","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Prior probability; Generalization; Domain (mathematical analysis); Pattern recognition (psychology); Medical imaging; Domain adaptation; Adaptation (eye); Adaptability","score_opus":0.05854744035310278,"score_gpt":0.32704222660570514,"score_spread":0.26849478625260237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929503800","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":[0.08035826,0.0022160565,0.91088325,0.000505164,0.00013931759,0.00011834831,0.00048194214,0.0033434723,0.0019542116],"genre_scores_gemma":[0.83191615,0.00092365005,0.15984558,0.00046759253,0.00013596175,0.00013132936,0.0021381166,0.00023350268,0.0042081964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957985,0.000099760924,0.000019943187,0.00016929429,0.0000694848,0.00006173226],"domain_scores_gemma":[0.99911433,0.00041571836,0.00009129717,0.00012668347,0.00015518253,0.000096670075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009334792,0.0009170093,0.0008410577,0.0010182987,0.0003226539,0.0005582306,0.0014735269,0.0011032973,0.0011956436],"category_scores_gemma":[0.0024980418,0.00037547635,0.0012747652,0.00068758277,0.0005549962,0.0011586231,0.001303574,0.0017823966,0.0008149453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005350948,0.00045172308,0.008271101,0.00021270667,0.00020250645,0.00032995938,0.0002424479,0.3417053,0.029860657,0.0041610757,0.01205861,0.6019688],"study_design_scores_gemma":[0.000009350788,0.0000665726,0.0010881652,0.000009810472,0.000020688312,0.00008895502,0.000022310258,0.99128973,0.0036038626,0.0029730236,0.0008144038,0.000013154432],"about_ca_topic_score_codex":0.0064212177,"about_ca_topic_score_gemma":0.008031714,"teacher_disagreement_score":0.0064212177,"about_ca_system_score_codex":0.0006335108,"about_ca_system_score_gemma":0.00088760274,"threshold_uncertainty_score":0.012767673},"labels":[],"label_agreement":null},{"id":"W6939258748","doi":"10.60692/h3mxe-etx36","title":"Deep clustering with a Dynamic Autoencoder: From reconstruction towards centroids construction","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Autoencoder; Cluster analysis; Benchmark (surveying); Function (biology); Centroid; Unsupervised learning","score_opus":0.01951740468207032,"score_gpt":0.1875282519239101,"score_spread":0.1680108472418398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939258748","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":[0.008468312,0.00015013693,0.9900193,0.000073187155,0.000019319474,0.000019367197,0.000036357327,0.0006426172,0.0005714236],"genre_scores_gemma":[0.312641,0.00033404163,0.6806004,0.00020018974,0.000052951193,0.00009323121,0.0005639332,0.00037435375,0.005139901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994375,0.00011282193,0.000027304028,0.00022967983,0.00013161622,0.000060996223],"domain_scores_gemma":[0.9992575,0.00020116445,0.00008503132,0.00020177275,0.00019924676,0.000055177596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093391875,0.0009007234,0.0011306362,0.00095472805,0.0005834213,0.0010601701,0.0021110254,0.0014851119,0.0017287247],"category_scores_gemma":[0.0023302354,0.0007621101,0.0008976366,0.0010084987,0.00096422277,0.0021555359,0.0020433967,0.0019226274,0.00114941],"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":[0.00014044064,0.000101156846,0.0012172144,0.00010830037,0.00010074604,0.00006824722,0.00024277168,0.5880738,0.013995042,0.020867912,0.0035144882,0.37156984],"study_design_scores_gemma":[0.0000031928987,0.000014105934,0.00010908087,0.000006243683,0.0000050229814,0.000022440021,0.000014980902,0.9914175,0.0022155864,0.0054881223,0.00069609587,0.0000076181477],"about_ca_topic_score_codex":0.0072303843,"about_ca_topic_score_gemma":0.009107541,"teacher_disagreement_score":0.0072303843,"about_ca_system_score_codex":0.0011675457,"about_ca_system_score_gemma":0.0011953692,"threshold_uncertainty_score":0.014376581},"labels":[],"label_agreement":null},{"id":"W6942203249","doi":"10.14288/1.0308121","title":"Henderson’s British Columbia gazetteer and directory for 1901 : comprising complete alphabetical directories of the cities and a classified business directory. Vol. VIII","year":2015,"lang":"en","type":"article","venue":"Open Collections","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"Directory; Columbia university; Index (typography); Beijing","score_opus":0.06307982855524513,"score_gpt":0.2636345100191511,"score_spread":0.20055468146390598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6942203249","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035434735,0.02636224,0.0013021285,0.006471798,0.002830929,0.0001587549,0.1493619,0.0011914694,0.80877733],"genre_scores_gemma":[0.011980305,0.010024282,0.0013907114,0.00066833774,0.00021822177,0.00009022567,0.036582533,0.0006854679,0.93835986],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99904305,0.00003733898,0.00003689007,0.0001468114,0.00052710896,0.00020871662],"domain_scores_gemma":[0.99784064,0.00010618958,0.00009229228,0.00012016091,0.0015685423,0.0002721924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004827213,0.0010804047,0.0008805031,0.006586458,0.00568958,0.004789736,0.0012515032,0.00090457045,0.18115611],"category_scores_gemma":[0.0024924763,0.0006390382,0.00024372895,0.02175838,0.0014837211,0.0021488797,0.001438149,0.001609551,0.08008666],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000147225655,0.000004765457,0.000379903,0.000083618,0.0000016886482,0.000023032831,0.0001946247,0.000028825494,0.000091838694,0.0029220295,0.96346605,0.032788884],"study_design_scores_gemma":[0.000001782683,0.0000013320011,0.0024176734,0.000053579322,0.000001378949,0.000012803409,0.00018526718,0.000010181293,0.00005837752,0.00021731498,0.9970323,0.000007936674],"about_ca_topic_score_codex":0.93056065,"about_ca_topic_score_gemma":0.97253394,"teacher_disagreement_score":0.18115611,"about_ca_system_score_codex":0.022403268,"about_ca_system_score_gemma":0.039339095,"threshold_uncertainty_score":0.6060276},"labels":[],"label_agreement":null},{"id":"W6950434628","doi":"10.5281/zenodo.8337068","title":"Dynamically Instance-Guided Adaptation: A Backward-free Approach for Test-Time Domain Adaptive Semantic Segmentation","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Horizon 2020 Framework Programme","keywords":"Segmentation; Classifier (UML); Domain adaptation; Adaptation (eye); Parametric statistics; Semantics (computer science)","score_opus":0.05055192879992903,"score_gpt":0.2533233821330278,"score_spread":0.20277145333309876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6950434628","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017732747,0.0003155258,0.97397715,0.00013528793,0.00006838434,0.000080137484,0.00015141461,0.0058917603,0.0016476376],"genre_scores_gemma":[0.42381224,0.0002753242,0.568051,0.0005318374,0.000074163785,0.00020171232,0.0012309192,0.0015682766,0.0042545414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987116,0.00026102248,0.0000681136,0.00054089987,0.000294505,0.00012382974],"domain_scores_gemma":[0.9984457,0.00048783387,0.000107585234,0.0005032399,0.0003410303,0.00011466879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016121577,0.0017203105,0.0012384175,0.0012708675,0.00051885063,0.0016274358,0.0033541545,0.0015645674,0.0028869032],"category_scores_gemma":[0.0039386703,0.0006672891,0.0012515229,0.0012963599,0.001036809,0.0027713347,0.0023672667,0.0021955988,0.0017205594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000416301,0.00026463327,0.0043977047,0.00018611857,0.000219432,0.00029063283,0.00046178928,0.19614767,0.052775644,0.008542199,0.0062531326,0.73004466],"study_design_scores_gemma":[0.000014902489,0.00005884467,0.0008061232,0.0000102943395,0.000035837624,0.00013577887,0.00008400669,0.9754228,0.012950552,0.0063872943,0.004064042,0.000029583627],"about_ca_topic_score_codex":0.0058824457,"about_ca_topic_score_gemma":0.0070126224,"teacher_disagreement_score":0.0058824457,"about_ca_system_score_codex":0.0010207177,"about_ca_system_score_gemma":0.0013686814,"threshold_uncertainty_score":0.011696398},"labels":[],"label_agreement":null},{"id":"W6957815172","doi":"10.60692/sbns0-0cq35","title":"A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McMaster University; University of Calgary","funders":"","keywords":"Benchmark (surveying); Segmentation; Image segmentation; Pattern recognition (psychology); Domain (mathematical analysis); Data set; Magnetic resonance imaging; Scale-space segmentation","score_opus":0.021786012556334177,"score_gpt":0.2133858961197514,"score_spread":0.19159988356341723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6957815172","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45899588,0.013761013,0.4487711,0.0028516948,0.0012235716,0.0017062767,0.021192897,0.03341894,0.018078655],"genre_scores_gemma":[0.50835025,0.0023485818,0.42051375,0.00080595,0.0002333158,0.0010645706,0.05795139,0.0024897796,0.006242414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970968,0.0009722444,0.00030250163,0.0007328116,0.00069711433,0.00019859384],"domain_scores_gemma":[0.99196786,0.003472542,0.00045209914,0.0013667297,0.0024278148,0.00031291545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054135225,0.0018979297,0.00105275,0.0025410808,0.001110158,0.0018959985,0.0025988556,0.0023331738,0.002017168],"category_scores_gemma":[0.017176153,0.00044183672,0.0010465528,0.0030833315,0.00100218,0.0014587666,0.001770979,0.0017496754,0.0015040676],"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":[0.0018251913,0.001444611,0.009584935,0.0019513718,0.0006355873,0.0006401875,0.0005242498,0.48686585,0.019382225,0.005918842,0.07959659,0.39163032],"study_design_scores_gemma":[0.0002458406,0.00066007616,0.011610552,0.00019705492,0.00010797657,0.0006247384,0.00032798416,0.91496205,0.033853907,0.010876373,0.02642388,0.00010951704],"about_ca_topic_score_codex":0.014137186,"about_ca_topic_score_gemma":0.015170144,"teacher_disagreement_score":0.014137186,"about_ca_system_score_codex":0.0016229175,"about_ca_system_score_gemma":0.0021341941,"threshold_uncertainty_score":0.02862978},"labels":[],"label_agreement":null},{"id":"W6976496237","doi":"10.60692/qdyvc-wk536","title":"Pre-trained Models for SMP Classification and Segmentation","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Random forest; Mixture model; Naive Bayes classifier; Python (programming language); Artificial neural network; Classifier (UML); Bayesian probability; Pattern recognition (psychology); Segmentation","score_opus":0.06226005648060976,"score_gpt":0.23799217663881267,"score_spread":0.17573212015820291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976496237","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13146476,0.0070949825,0.34454486,0.002536955,0.0040629837,0.0013147937,0.34156027,0.12639417,0.041026264],"genre_scores_gemma":[0.23400438,0.001483725,0.1861936,0.0010298645,0.00038462592,0.0014897431,0.5407163,0.0043229936,0.030374758],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990804,0.00012991858,0.000048533286,0.00040105963,0.0001704092,0.0001696002],"domain_scores_gemma":[0.9991617,0.0002558612,0.00003728598,0.00020461675,0.00029338495,0.00004713228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009096651,0.00335018,0.0010234651,0.0015427866,0.00069129776,0.0015237507,0.002516513,0.0021868162,0.017405357],"category_scores_gemma":[0.0037773133,0.0007376264,0.0027185215,0.0015789316,0.00053659594,0.001720814,0.0012378219,0.00392328,0.0267576],"study_design_candidate":"bench_or_experimental","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":[0.000617359,0.00050190935,0.005970632,0.0008257293,0.00033395365,0.00042575455,0.00020830064,0.20557736,0.008856366,0.002746399,0.38706237,0.38687387],"study_design_scores_gemma":[0.00008765931,0.00015189536,0.0051668934,0.00029069238,0.000104304905,0.0002136423,0.00022145417,0.89595646,0.013351957,0.005676197,0.07867861,0.0001003433],"about_ca_topic_score_codex":0.0341584,"about_ca_topic_score_gemma":0.05000118,"teacher_disagreement_score":0.0341584,"about_ca_system_score_codex":0.0018141132,"about_ca_system_score_gemma":0.002065474,"threshold_uncertainty_score":0.067919195},"labels":[],"label_agreement":null},{"id":"W6979296349","doi":"","title":"Train-Attention: Meta-Learning Where to Focus in Continual Knowledge Learning","year":2024,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Ministry of Science and ICT, South Korea; Institute for Information and Communications Technology Promotion; Yonsei University","keywords":"Security token; Focus (optics); Training set; Knowledge base; Deep learning; Knowledge acquisition","score_opus":0.07872696167202559,"score_gpt":0.21197053880232436,"score_spread":0.13324357713029877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979296349","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":[0.12504709,0.004673404,0.8345065,0.0015637842,0.00037261384,0.00035269556,0.0013392752,0.023365438,0.008779178],"genre_scores_gemma":[0.6658312,0.00072074257,0.32473662,0.00075409305,0.00012788437,0.00035460127,0.0027317288,0.000654753,0.004088257],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836904,0.0005665635,0.00009685903,0.0006091803,0.00022886346,0.00012952619],"domain_scores_gemma":[0.9952454,0.0026879797,0.00022419835,0.0011477072,0.0004890031,0.00020566574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032808757,0.0019342814,0.0012715112,0.00122554,0.0007483328,0.0017141071,0.004562892,0.0021053506,0.0025492678],"category_scores_gemma":[0.011869423,0.0006044195,0.00078496925,0.001181955,0.0011776581,0.0053498545,0.0027204459,0.0033066217,0.0010506875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078017387,0.00076216686,0.004615158,0.00083835603,0.00024348346,0.0002205788,0.00036037376,0.3007536,0.00846599,0.009846074,0.014533625,0.6585805],"study_design_scores_gemma":[0.000054107015,0.00020563096,0.00042502515,0.000039239483,0.000043000276,0.00007220061,0.000059154147,0.9744421,0.006445704,0.014918755,0.003268367,0.000026674637],"about_ca_topic_score_codex":0.006874308,"about_ca_topic_score_gemma":0.012226945,"teacher_disagreement_score":0.006874308,"about_ca_system_score_codex":0.0015098383,"about_ca_system_score_gemma":0.0019349526,"threshold_uncertainty_score":0.01735115},"labels":[],"label_agreement":null},{"id":"W6979312277","doi":"","title":"GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Forgetting; Task (project management); Context (archaeology); Class (philosophy); Sample (material); Training set","score_opus":0.08602960237184426,"score_gpt":0.3163622559130306,"score_spread":0.23033265354118632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979312277","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029819006,0.00082297344,0.9576543,0.00024198827,0.00013943223,0.00021021136,0.0002660102,0.009729841,0.0011163171],"genre_scores_gemma":[0.4317281,0.00036984417,0.559987,0.00069334806,0.00016246946,0.00060099683,0.0016068286,0.0010183317,0.0038330455],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988814,0.00026152836,0.00006515602,0.00043214823,0.00025651787,0.000103270046],"domain_scores_gemma":[0.99765563,0.00086797297,0.00016601423,0.00082395755,0.00032630356,0.00016008213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023564782,0.0018041313,0.0018589683,0.0011480132,0.00065851,0.0012756396,0.00386329,0.0015380351,0.0027795513],"category_scores_gemma":[0.008241034,0.0008029416,0.001058392,0.0010176072,0.0013918929,0.0033580845,0.004024735,0.0032002113,0.0016131757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060635095,0.0006299078,0.0032628553,0.00033158847,0.00022308191,0.00018306014,0.0004291399,0.127221,0.02726972,0.006730072,0.011356717,0.8217566],"study_design_scores_gemma":[0.000045116907,0.0001809279,0.00048110564,0.000023174678,0.000029313593,0.000098327255,0.000045829143,0.97110295,0.014740293,0.010509928,0.002707199,0.000035914254],"about_ca_topic_score_codex":0.002401259,"about_ca_topic_score_gemma":0.004595811,"teacher_disagreement_score":0.00386329,"about_ca_system_score_codex":0.0007206313,"about_ca_system_score_gemma":0.0012974617,"threshold_uncertainty_score":0.012462437},"labels":[],"label_agreement":null},{"id":"W6981788333","doi":"","title":"'Falsis nominibus imperium': An Oceanic History of Indigenous Power, Virtue, and Territorial Possession in the Crisis of English Colonization, 1570 - 1630","year":2025,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"Indigenous; Possession (linguistics); Colonialism; Politics; Sovereignty; Power (physics)","score_opus":0.005156164145391903,"score_gpt":0.1937372869139054,"score_spread":0.1885811227685135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6981788333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90193915,0.0049639605,0.00028066564,0.0023709787,0.000102630096,0.000017484968,0.000028317047,0.000008794468,0.090287946],"genre_scores_gemma":[0.98996395,0.0017115915,0.00008353473,0.00016335322,0.00002740494,0.000008784416,0.000011614666,0.0000081555645,0.008021732],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995351,0.00018024482,0.000019696767,0.000058899546,0.000077688564,0.00012838884],"domain_scores_gemma":[0.9995888,0.00019592638,0.000080199796,0.000024135617,0.00006392299,0.000047125992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096726243,0.00017838336,0.00027711634,0.00076939375,0.0070778662,0.0028824883,0.000408391,0.0007026621,0.0018917342],"category_scores_gemma":[0.0014667964,0.00017973455,0.00008666709,0.0010179563,0.01297452,0.0023889735,0.0028247344,0.0019302261,0.00018688374],"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":[0.000020134416,0.000014038498,0.0038146607,0.000067448505,0.000002765982,0.0009124812,0.9525428,0.000020301857,0.00041389215,0.031962816,0.00055892253,0.009669775],"study_design_scores_gemma":[0.0000053408185,0.00007101972,0.032911345,0.00039828065,0.00001322212,0.00087941246,0.783862,0.000088850466,0.0006620079,0.0031047843,0.17797878,0.000024983487],"about_ca_topic_score_codex":0.060225096,"about_ca_topic_score_gemma":0.12615073,"teacher_disagreement_score":0.060225096,"about_ca_system_score_codex":0.004408485,"about_ca_system_score_gemma":0.002537746,"threshold_uncertainty_score":0.11974913},"labels":[],"label_agreement":null},{"id":"W6987823399","doi":"","title":"Unsupervised Domain Adaptation for Estimating Occupancy and Recognizing Activities in Smart Buildings","year":2023,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University","keywords":"Occupancy; Domain (mathematical analysis); Transfer of learning; Adaptation (eye); Building automation; Matching (statistics); Domain adaptation; Labeled data; Data modeling; Model building","score_opus":0.050645588657515804,"score_gpt":0.30019189194985746,"score_spread":0.24954630329234165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6987823399","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":[0.08089446,0.00049892796,0.9141302,0.0001216656,0.00011588209,0.000077515535,0.00039694575,0.0023439364,0.0014203792],"genre_scores_gemma":[0.6892801,0.00039322203,0.3040687,0.0001800171,0.00013330665,0.00017759776,0.002633852,0.0002047677,0.0029284689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940157,0.00018130954,0.000026518246,0.00023168889,0.00008542694,0.00007342844],"domain_scores_gemma":[0.9991333,0.00040776507,0.000070175345,0.00018612563,0.00015390776,0.000048750408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008159365,0.00070529565,0.00074063655,0.00089397276,0.00025982226,0.00050026266,0.0010257341,0.00061796524,0.0010049304],"category_scores_gemma":[0.0024853887,0.0003156451,0.0010976311,0.0009434134,0.0004847234,0.000983068,0.00088399166,0.0012226525,0.0008000581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028991187,0.0004017618,0.009491672,0.00016116214,0.00018998419,0.00019722719,0.00030819632,0.3797448,0.019674808,0.0032872974,0.005309139,0.58094406],"study_design_scores_gemma":[0.000007676149,0.000030018704,0.0027904804,0.0000073715837,0.000010891118,0.000051615225,0.00007391552,0.98952746,0.004029334,0.0020259153,0.0014289398,0.000016373706],"about_ca_topic_score_codex":0.004929483,"about_ca_topic_score_gemma":0.0058455835,"teacher_disagreement_score":0.004929483,"about_ca_system_score_codex":0.000368078,"about_ca_system_score_gemma":0.0005749152,"threshold_uncertainty_score":0.009801567},"labels":[],"label_agreement":null},{"id":"W7011573803","doi":"","title":"Meta-learning for Clinical and Imaging Data Fusion for Improved Deep Learning Inference","year":2023,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; International Progressive MS Alliance; F. Hoffmann-La Roche; Teva Pharmaceutical Industries; Western Canada Research Grid; Biogen","keywords":"Deep learning; Inference; Sensor fusion; Pattern recognition (psychology); Fusion; Artificial neural network","score_opus":0.12652407767602192,"score_gpt":0.36078965635016613,"score_spread":0.23426557867414421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7011573803","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016270578,0.0025345278,0.9759998,0.0011633392,0.00021520485,0.000069670095,0.0007933621,0.0021176683,0.0008358288],"genre_scores_gemma":[0.46372685,0.0013103888,0.5247355,0.00089161255,0.00047934634,0.00024779642,0.0030921386,0.0005825402,0.0049337763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912685,0.0002592934,0.000077598044,0.00027437398,0.00015626904,0.000105611165],"domain_scores_gemma":[0.9978574,0.0010847226,0.00015471336,0.0003671953,0.0004241441,0.0001117238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038454195,0.0011513946,0.0021081052,0.0015016197,0.00041217406,0.0014040854,0.0020999818,0.0022025148,0.003335965],"category_scores_gemma":[0.008557517,0.0009957378,0.0029616205,0.0012519546,0.0004823913,0.0019052603,0.0025864695,0.0031989126,0.0016929697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007097011,0.00040313965,0.0041304654,0.00027945582,0.0008740736,0.00017906813,0.00009937874,0.21410379,0.017538536,0.0075336634,0.017398408,0.73675036],"study_design_scores_gemma":[0.00002015488,0.00006516967,0.00094435154,0.0000429332,0.0001237503,0.000103250226,0.0000138766545,0.98063594,0.007068012,0.009445177,0.0015148285,0.00002254131],"about_ca_topic_score_codex":0.004011065,"about_ca_topic_score_gemma":0.0064615393,"teacher_disagreement_score":0.004011065,"about_ca_system_score_codex":0.0009237808,"about_ca_system_score_gemma":0.0015260405,"threshold_uncertainty_score":0.020336807},"labels":[],"label_agreement":null},{"id":"W7019044707","doi":"","title":"Federated Learning With Generalization To New Domains","year":2024,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Generalization; Overfitting; Domain (mathematical analysis); Federated learning; Unsupervised learning; Smoothing; Sensitivity (control systems)","score_opus":0.006902963206787354,"score_gpt":0.19670761946443982,"score_spread":0.18980465625765247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019044707","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05333024,0.00018806144,0.9420914,0.0003699066,0.000035363882,0.000093993825,0.00014900754,0.0027045605,0.0010375197],"genre_scores_gemma":[0.7643871,0.00012591509,0.23163022,0.00039900423,0.00006117516,0.00023738084,0.0006763022,0.00023791617,0.002244964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789387,0.0006706167,0.000114007926,0.0008721677,0.00028944763,0.00016000526],"domain_scores_gemma":[0.99417865,0.0016477512,0.00034103083,0.002978343,0.0006453046,0.00020884046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033775384,0.0012835731,0.001954036,0.0006568146,0.0009327746,0.00151661,0.002974041,0.0015342056,0.001381676],"category_scores_gemma":[0.00938651,0.0006844094,0.0012853158,0.0010101012,0.0016770628,0.0046801474,0.0037773708,0.0030741857,0.0006870103],"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":[0.00025604627,0.000298681,0.0034726036,0.00009328127,0.0001114891,0.00013334339,0.00023066407,0.845714,0.004406203,0.009515089,0.0028896513,0.13287903],"study_design_scores_gemma":[0.000012470313,0.00003002774,0.0001554593,0.000004509691,0.0000073589513,0.00002212336,0.000023018327,0.9857896,0.0011468192,0.012367046,0.00043501207,0.0000065780455],"about_ca_topic_score_codex":0.0041238274,"about_ca_topic_score_gemma":0.0062671877,"teacher_disagreement_score":0.0041238274,"about_ca_system_score_codex":0.0015868986,"about_ca_system_score_gemma":0.0018473115,"threshold_uncertainty_score":0.01786238},"labels":[],"label_agreement":null},{"id":"W7021693285","doi":"","title":"Radio","year":2006,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"Summit; Radio broadcasting; The Internet; Radio communications","score_opus":0.005754057989131194,"score_gpt":0.17840669964204156,"score_spread":0.17265264165291036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7021693285","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005777593,0.00026096273,0.00091442827,0.0009702564,0.0016626469,0.000081965176,0.011778575,0.0012022002,0.98255116],"genre_scores_gemma":[0.0035115352,0.00022025936,0.0005176942,0.00056185527,0.00064529915,0.000051558272,0.007267797,0.0004780666,0.9867459],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99972075,0.00002902048,0.000007934068,0.000053979347,0.00013248427,0.000055665492],"domain_scores_gemma":[0.99936956,0.00008679575,0.000024978204,0.000094611714,0.0002792212,0.00014479767],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003868795,0.00081757084,0.00041794498,0.0017868852,0.0017626641,0.0025869492,0.0008117399,0.0010907816,0.73843414],"category_scores_gemma":[0.0014543232,0.00023818329,0.0002875874,0.0012713185,0.00031470307,0.0013493802,0.0013790507,0.0014869933,0.51951456],"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":[0.00007177203,0.000017539967,0.000095114236,0.000050729283,0.0000023163168,0.00005330502,0.000073994765,0.00004343109,0.0004411745,0.0013943292,0.9559267,0.041829467],"study_design_scores_gemma":[0.000013899702,0.000013237423,0.0007483671,0.000023292232,0.000002156618,0.000049052487,0.00006857697,0.000041820273,0.00012046634,0.000232542,0.9986822,0.000004362217],"about_ca_topic_score_codex":0.006841781,"about_ca_topic_score_gemma":0.021956814,"teacher_disagreement_score":0.26156586,"about_ca_system_score_codex":0.0005611425,"about_ca_system_score_gemma":0.0005521057,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7023407044","doi":"","title":"OrtOn Antivirus Customer Support || 1-844 855 1955 || Number, Antivirus 365 Toll Free Number","year":2016,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"Phone; Payroll; Phone call; The Internet; Toll","score_opus":0.015675458174184014,"score_gpt":0.27509521068194154,"score_spread":0.2594197525077575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023407044","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064309494,0.00038138285,0.0008899712,0.0004937697,0.0005490106,0.00010058939,0.0026635495,0.0038358879,0.9904427],"genre_scores_gemma":[0.0013684601,0.00021704228,0.00025751416,0.00014801139,0.000057273275,0.000024375378,0.001204951,0.000568886,0.99615353],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99945766,0.00003624472,0.000019004128,0.000106409454,0.00031379203,0.000066847155],"domain_scores_gemma":[0.99851936,0.00016911839,0.00005697497,0.00015701453,0.00074080576,0.00035659247],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003868835,0.0009474267,0.0007211586,0.0013010801,0.0013496367,0.004503779,0.0010488748,0.0011579897,0.8849746],"category_scores_gemma":[0.0019548147,0.00061552436,0.00039134314,0.0010977304,0.0003156535,0.0025652107,0.0013616347,0.0012090799,0.84796023],"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":[0.000053263782,0.00003892141,0.00022456114,0.00007119024,0.0000021383944,0.00005337102,0.000040960607,0.00003675718,0.0009400178,0.0018482751,0.9542512,0.04243928],"study_design_scores_gemma":[0.000011698101,0.000028532215,0.0004421576,0.000052624717,0.0000043375044,0.000087124594,0.00006891741,0.0001216992,0.0005276697,0.00023506081,0.9984124,0.000007861205],"about_ca_topic_score_codex":0.0054421546,"about_ca_topic_score_gemma":0.009431357,"teacher_disagreement_score":0.1150254,"about_ca_system_score_codex":0.000967974,"about_ca_system_score_gemma":0.0010859646,"threshold_uncertainty_score":0.16406971},"labels":[],"label_agreement":null},{"id":"W7024111708","doi":"","title":"Prescott - 107900 (C)","year":2009,"lang":"en","type":"other","venue":"OhioLink ETD Center (Ohio Library and Information Network)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"DOCK; Service (business); Port (circuit theory); Work (physics)","score_opus":0.008305066348003567,"score_gpt":0.19834929252475053,"score_spread":0.19004422617674696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024111708","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012293961,0.00047162638,0.0013350658,0.00094333646,0.00041937057,0.00007430661,0.006618119,0.0014594186,0.9874493],"genre_scores_gemma":[0.0038156714,0.00012354023,0.0004524578,0.00015957606,0.000022007125,0.000012018175,0.001589904,0.0003155267,0.99350923],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970335,0.000011554583,0.000003910966,0.00008425115,0.00011709357,0.00007970632],"domain_scores_gemma":[0.9995628,0.000018590124,0.000010839263,0.000037356767,0.00023818859,0.00013224209],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002778823,0.0007007278,0.0004604119,0.00092788343,0.0030508807,0.002337242,0.00095274934,0.0009856434,0.7436617],"category_scores_gemma":[0.0008575252,0.0003214375,0.00026574108,0.0013763275,0.0007117474,0.001336961,0.0014749406,0.0010503008,0.42864454],"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":[0.00006202376,0.000013784498,0.0004027958,0.000032463173,0.0000023158348,0.00014127001,0.00009069407,0.00009490165,0.0006100618,0.0081966845,0.9126413,0.07771171],"study_design_scores_gemma":[0.0000038647563,0.0000066778016,0.00066203147,0.000013800761,7.393722e-7,0.00004070262,0.000036921767,0.000058199148,0.00020830274,0.0003070809,0.9986578,0.000003872637],"about_ca_topic_score_codex":0.39848223,"about_ca_topic_score_gemma":0.7650753,"teacher_disagreement_score":0.7436617,"about_ca_system_score_codex":0.00509258,"about_ca_system_score_gemma":0.0037917872,"threshold_uncertainty_score":0.7923256},"labels":[],"label_agreement":null},{"id":"W7024757595","doi":"","title":"Strengthening\\tEnvironmental\\tAssessment\\tof\\tCanadian\\tSupported\\tMining\\tVentures\\tin\\t Developing\\tCountries","year":2001,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"Work (physics); Government (linguistics); Developing country; Process (computing); Identification (biology)","score_opus":0.01750378948157969,"score_gpt":0.25083978527665385,"score_spread":0.23333599579507416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024757595","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75750166,0.00038490733,0.090467386,0.017610842,0.00007536149,0.00031696554,0.000521728,0.0010892882,0.13203177],"genre_scores_gemma":[0.9420747,0.00024841336,0.041685376,0.0004994184,0.000010253773,0.000065854634,0.0004545806,0.00006400393,0.014897429],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987748,0.000557305,0.00003694462,0.0001768719,0.0002474883,0.00020649549],"domain_scores_gemma":[0.99632615,0.0010030248,0.0002643378,0.0006642316,0.0010872826,0.0006549497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030922678,0.00024878618,0.00021649328,0.0006719025,0.0023488866,0.0033150841,0.0012941425,0.0010182797,0.0076494166],"category_scores_gemma":[0.008915157,0.00014481776,0.00025066812,0.0009572766,0.0014366057,0.0033446425,0.004141234,0.0013941162,0.0007107449],"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":[0.00026948255,0.0015481068,0.08378192,0.00021116859,0.000051734285,0.00019970935,0.010668584,0.022545513,0.009832266,0.07158734,0.021234773,0.7780694],"study_design_scores_gemma":[0.00010458243,0.0007452088,0.14463854,0.0005082731,0.000098784345,0.00035042176,0.07108059,0.12846677,0.03547814,0.27362785,0.34477276,0.00012807881],"about_ca_topic_score_codex":0.0815901,"about_ca_topic_score_gemma":0.21843572,"teacher_disagreement_score":0.9184099,"about_ca_system_score_codex":0.003662191,"about_ca_system_score_gemma":0.017034207,"threshold_uncertainty_score":0.16223043},"labels":[],"label_agreement":null},{"id":"W7024824588","doi":"","title":"Table XIII. Nova metamorfosi dell’infrascritti autori, opera del R. P. F. Geronimo Cavaglieri […] libro primo, Milano: Agostino Tradate, 1600. In Contrafacta. Modes of Music Re-textualization in the Late Sixteenth and Seventeenth Century","year":2020,"lang":"en","type":"other","venue":"Research Padua  Archive (University of Padua)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"Opera; Nova (rocket); Table (database); Nova scotia; Table of contents","score_opus":0.061552992283161266,"score_gpt":0.27668934426912556,"score_spread":0.21513635198596429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024824588","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064491336,0.015311967,0.009693847,0.002066422,0.0047385585,0.0007897139,0.2779508,0.0037401349,0.6792595],"genre_scores_gemma":[0.03245159,0.021019362,0.031233229,0.0010371876,0.002077543,0.0012559892,0.39786986,0.003833688,0.5092215],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987198,0.00014723539,0.00017548296,0.00022393279,0.0006341607,0.000099379424],"domain_scores_gemma":[0.99719244,0.0006681495,0.00016379353,0.0004967906,0.0011732323,0.00030566158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012294474,0.000901778,0.00081881444,0.012680289,0.001451158,0.0045813103,0.0008026058,0.00048202975,0.13819757],"category_scores_gemma":[0.005210623,0.0003979129,0.00049621967,0.016733505,0.00072428683,0.002970503,0.0019162082,0.0016599566,0.06578141],"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":[0.00014539428,0.000045797744,0.0013818002,0.0011988098,0.00001952366,0.00006568851,0.00048459097,0.00020515849,0.0013877258,0.013757623,0.8026542,0.17865363],"study_design_scores_gemma":[0.000004739876,0.000010509228,0.0024584332,0.00019012114,0.000004763976,0.00008708947,0.00016152738,0.00004042138,0.00037537477,0.0009084123,0.995751,0.000007524867],"about_ca_topic_score_codex":0.0109179085,"about_ca_topic_score_gemma":0.018436218,"teacher_disagreement_score":0.13819757,"about_ca_system_score_codex":0.0019224726,"about_ca_system_score_gemma":0.0022649567,"threshold_uncertainty_score":0.46231693},"labels":[],"label_agreement":null},{"id":"W7024974220","doi":"","title":"Systematic Review and Meta-Analysis of Preterm Birth and Later Systolic Blood Pressure","year":2012,"lang":"en","type":"article","venue":"Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer 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":"Pediatric Oncology Group","funders":"","keywords":"Blood pressure; Systole; Diastole; Pulse pressure; Pregnancy","score_opus":0.01833962757332142,"score_gpt":0.22020279084435598,"score_spread":0.20186316327103457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024974220","genre_codex":"review","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":[0.016740346,0.97730565,0.002982473,0.00040803457,0.00074666,0.00060972356,0.00079595327,0.00007885598,0.00033226024],"genre_scores_gemma":[0.63352317,0.34621325,0.012420985,0.0019839462,0.00082963426,0.0027686604,0.0013230345,0.00011626132,0.0008210752],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9640924,0.021924585,0.008586687,0.0026453538,0.0022684373,0.00048256267],"domain_scores_gemma":[0.9242192,0.061122697,0.0071493685,0.003962579,0.002917824,0.00062840746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032908197,0.0030021972,0.019347122,0.006133123,0.00094007276,0.0028602185,0.00268812,0.0031334353,0.003989549],"category_scores_gemma":[0.11461939,0.0026335707,0.041829042,0.004816998,0.0013447469,0.002106939,0.0026653735,0.0025330794,0.00032305345],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031685138,0.000023383851,0.0026317448,0.106153384,0.8799527,0.00008895274,0.000086646876,0.00028202395,0.00018765464,0.00009342646,0.0004041338,0.0069274814],"study_design_scores_gemma":[0.0014517513,0.00024628034,0.0020702712,0.005230254,0.9899232,0.00005611872,0.000031440686,0.00013312678,0.00006137914,0.00020139114,0.0005798463,0.000014990853],"about_ca_topic_score_codex":0.006415683,"about_ca_topic_score_gemma":0.0144297425,"teacher_disagreement_score":0.032908197,"about_ca_system_score_codex":0.002366258,"about_ca_system_score_gemma":0.003989191,"threshold_uncertainty_score":0.17403728},"labels":[],"label_agreement":null},{"id":"W7035730723","doi":"","title":"Alexander &amp;amp; Baldwin Reports Third Quarter 2012 Results - KHQ Right Now - News and Weather for Spokane and North Idaho |","year":2012,"lang":"en","type":"other","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Period (music)","score_opus":0.025363573165568476,"score_gpt":0.2556041133988785,"score_spread":0.23024054023331003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7035730723","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005537719,0.0009844603,0.0068966025,0.043927252,0.014713614,0.0004914977,0.06722651,0.025054164,0.8351682],"genre_scores_gemma":[0.010482201,0.00023778045,0.0028819484,0.0019224425,0.0007075165,0.0000706335,0.022639763,0.0027524303,0.9583053],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983011,0.00011291946,0.000036749858,0.00022127583,0.0010458098,0.0002821766],"domain_scores_gemma":[0.99442095,0.0006668071,0.000092590024,0.00078271347,0.002365437,0.0016715381],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0028085716,0.0010782229,0.0009074027,0.002072204,0.002628233,0.00662819,0.0010953424,0.002047816,0.3758698],"category_scores_gemma":[0.006286562,0.00043923076,0.00070408784,0.0014353079,0.0007246527,0.0021323632,0.002196628,0.0024284269,0.2365792],"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":[0.000059957314,0.00005174878,0.00013854702,0.000015129067,0.0000036753745,0.000014308455,0.000007960773,0.000062278064,0.00013901277,0.00053284643,0.98609525,0.012879165],"study_design_scores_gemma":[0.00006727323,0.000057473782,0.0028302972,0.000025232994,0.000013273112,0.000027720425,0.00009467406,0.0013298827,0.0019815466,0.0016473535,0.9919024,0.000022887778],"about_ca_topic_score_codex":0.045630425,"about_ca_topic_score_gemma":0.14797296,"teacher_disagreement_score":0.6241302,"about_ca_system_score_codex":0.0024359922,"about_ca_system_score_gemma":0.0027621172,"threshold_uncertainty_score":0.890246},"labels":[],"label_agreement":null},{"id":"W7036649776","doi":"","title":"Compensation for pain and suffering damages predicted for victims of sexual offenses:A statistical analysis","year":2024,"lang":"nl","type":"article","venue":"VU Research Portal","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Damages; Sexual abuse; Pain and suffering; Compensation (psychology); Poison control","score_opus":0.09529442436004218,"score_gpt":0.40142654923515236,"score_spread":0.3061321248751102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036649776","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914963,0.00057959434,0.0046744714,0.00044828383,0.0000949163,0.00013345842,0.0015944454,0.00009182634,0.00088673824],"genre_scores_gemma":[0.9955318,0.00010889384,0.0011111838,0.00007197552,0.00003966868,0.00014698571,0.0019399035,0.00004415429,0.0010053847],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98895663,0.006316065,0.00078408496,0.002130103,0.001096208,0.00071693864],"domain_scores_gemma":[0.92484164,0.06306041,0.003933,0.0045356774,0.0017044223,0.0019246917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015779383,0.0004590899,0.001133237,0.003472017,0.0009777686,0.0014511338,0.0020046,0.0013968226,0.009235215],"category_scores_gemma":[0.04340941,0.0003809364,0.0034921698,0.002778275,0.0016799618,0.0019721997,0.0015736454,0.0027762793,0.0011804692],"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":[0.003447043,0.00075486075,0.96435404,0.00015098094,0.0034452977,0.0003895478,0.000844878,0.002247764,0.00037941002,0.0006251758,0.0034406155,0.01992041],"study_design_scores_gemma":[0.00019232836,0.0018308354,0.9452242,0.000084002,0.0018539269,0.0005437962,0.0035118079,0.042213976,0.0005609362,0.0017575274,0.0021300474,0.000096543816],"about_ca_topic_score_codex":0.011814106,"about_ca_topic_score_gemma":0.005172948,"teacher_disagreement_score":0.015779383,"about_ca_system_score_codex":0.0008032163,"about_ca_system_score_gemma":0.0011680251,"threshold_uncertainty_score":0.08345038},"labels":[],"label_agreement":null},{"id":"W7039521847","doi":"","title":"MasTec Schedules First Quarter 2012 Earnings Release and Conference Call\\n| Reuters","year":2012,"lang":"en","type":"other","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Earnings; Quarter (Canadian coin); Payment; Duration (music)","score_opus":0.018493644071105365,"score_gpt":0.22743508661778888,"score_spread":0.2089414425466835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039521847","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023996965,0.000406183,0.008699646,0.0035011559,0.003354102,0.0005779205,0.03531146,0.05727785,0.888472],"genre_scores_gemma":[0.0021156883,0.00011698055,0.0014751963,0.0001817751,0.00023175275,0.000075369164,0.013493096,0.005519603,0.97679055],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999438,0.000060555405,0.000016311467,0.00009889816,0.000286218,0.000099995166],"domain_scores_gemma":[0.9966809,0.00032142518,0.00007785659,0.000899853,0.0010604943,0.0009594693],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014070369,0.0011364054,0.0013361635,0.0019182696,0.0015097398,0.004495658,0.0022921013,0.0014443824,0.8605158],"category_scores_gemma":[0.004612356,0.0008367482,0.0010054805,0.0018635233,0.00035576543,0.0022048191,0.0020701913,0.0017856428,0.8487202],"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":[0.000066691275,0.00003450589,0.000032412,0.00001911273,0.0000019097213,0.000009489255,0.0000042906813,0.00006543891,0.00017434888,0.00036558494,0.9779808,0.021245355],"study_design_scores_gemma":[0.000085291074,0.00006957518,0.0009034972,0.000025679801,0.000005899368,0.00003382868,0.000032044998,0.0010953847,0.000756111,0.0010436481,0.99593264,0.00001637234],"about_ca_topic_score_codex":0.0064816703,"about_ca_topic_score_gemma":0.01907597,"teacher_disagreement_score":0.8605158,"about_ca_system_score_codex":0.0009522253,"about_ca_system_score_gemma":0.0013303611,"threshold_uncertainty_score":0.19895732},"labels":[],"label_agreement":null},{"id":"W7042417563","doi":"","title":"Passive sampling to understand and predict sources of wastewater and agricultural contamination in rural watersheds","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de l’Environnement, de la Protection de la nature et des Parcs; University of Waterloo; Ministry of Environment","keywords":"Passive sampling; Sampling (signal processing); Sucralose; STREAMS; Range (aeronautics); Wastewater; Contamination; Surface water","score_opus":0.013734820546442754,"score_gpt":0.20699430535573413,"score_spread":0.19325948480929137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7042417563","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9773942,0.00026949242,0.019646393,0.000066181485,0.0000044165804,0.00012590867,0.0009704461,0.00012390972,0.0013989673],"genre_scores_gemma":[0.97477245,0.00047173694,0.02206174,0.000051840612,0.000007023656,0.0001068388,0.0011385281,0.000018685965,0.0013711809],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998079,0.000032898042,0.000013691077,0.0000650442,0.000057248293,0.000023287439],"domain_scores_gemma":[0.9997614,0.00007860293,0.00006464936,0.000009893918,0.00007135561,0.000014172085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033066364,0.00030584566,0.00032647996,0.0009187685,0.00021151778,0.0006701103,0.00033045598,0.00038748886,0.00054195215],"category_scores_gemma":[0.00054277934,0.00017369048,0.0002426827,0.0011226748,0.0001770339,0.000466295,0.000370097,0.0002287229,0.00019860642],"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":[0.00024607964,0.00041249313,0.75106776,0.00038967494,0.0000995113,0.00026739755,0.000565051,0.020233897,0.12085148,0.00060177303,0.00064066594,0.104624234],"study_design_scores_gemma":[0.000049386275,0.0007653106,0.7412346,0.0000704348,0.000111681664,0.00030663557,0.0022344908,0.20104739,0.04443747,0.0019634138,0.0077386643,0.000040513278],"about_ca_topic_score_codex":0.009776704,"about_ca_topic_score_gemma":0.015067037,"teacher_disagreement_score":0.009776704,"about_ca_system_score_codex":0.00046896804,"about_ca_system_score_gemma":0.00054473896,"threshold_uncertainty_score":0.019439638},"labels":[],"label_agreement":null},{"id":"W7065157298","doi":"","title":"Detail, Angel and Children from Right Lancet of The James and Mary Brydson Memorial Window","year":2022,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"Window (computing); Metis; Biography; Narrative","score_opus":0.04396619135551127,"score_gpt":0.2587902489390625,"score_spread":0.21482405758355122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7065157298","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010955885,0.007733194,0.0023413668,0.13231757,0.026548749,0.00026010486,0.004434741,0.0015023132,0.81390613],"genre_scores_gemma":[0.01342119,0.0019450362,0.0008717728,0.0037057837,0.00058606226,0.000052380674,0.00040080326,0.00023670681,0.97878015],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998122,0.000023170174,0.0000063494213,0.000045035325,0.000050700826,0.000062623105],"domain_scores_gemma":[0.9987864,0.000048298327,0.000025442378,0.00003307598,0.0002067444,0.000900106],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00048078893,0.00045660068,0.0002767863,0.0004007396,0.0028987194,0.0018654544,0.00039915714,0.0008922627,0.45719838],"category_scores_gemma":[0.0015586666,0.00038117272,0.00023402601,0.00025648432,0.0003791479,0.0014318301,0.0021822834,0.0019626813,0.13433217],"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":[0.000037286565,0.000014185895,0.00025053043,0.000018851706,8.6784104e-7,0.00013974204,0.00026564547,0.000007705738,0.00026857338,0.0009971524,0.9818609,0.016138557],"study_design_scores_gemma":[0.0000063264415,0.00002217646,0.0012945756,0.00006179946,0.0000025188087,0.00028423546,0.0013187218,0.000009651732,0.00014064586,0.00038369853,0.99647117,0.000004599228],"about_ca_topic_score_codex":0.009606911,"about_ca_topic_score_gemma":0.063869156,"teacher_disagreement_score":0.45719838,"about_ca_system_score_codex":0.00082776754,"about_ca_system_score_gemma":0.0022286493,"threshold_uncertainty_score":0.7742406},"labels":[],"label_agreement":null},{"id":"W7066145984","doi":"","title":"French solar power generation surges in first quarter after sunny spell","year":2019,"lang":"en","type":"other","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Electricity generation; Solar power; Spell; Power (physics)","score_opus":0.01136146120381005,"score_gpt":0.21356729141715367,"score_spread":0.20220583021334362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7066145984","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8671737,0.0042151446,0.007398653,0.008021965,0.0030350415,0.000038158963,0.019486064,0.0030807734,0.08755063],"genre_scores_gemma":[0.9699139,0.00041922898,0.001196619,0.00032435273,0.00029457043,0.000010291406,0.0077935657,0.00015093658,0.01989649],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99988115,0.000018479997,0.000005038591,0.000030025763,0.00003487753,0.000030428702],"domain_scores_gemma":[0.99964345,0.00009256128,0.000033841545,0.00003280348,0.00013834746,0.000058936505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026738312,0.00025961798,0.00022891468,0.0007804032,0.0006239447,0.0007447004,0.00016776852,0.0005260958,0.006760699],"category_scores_gemma":[0.001290638,0.000055077933,0.0001937356,0.00072267,0.00018659316,0.00033872342,0.0003198875,0.00047309065,0.0012403529],"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":[0.0015465895,0.00014956923,0.1189491,0.00034571165,0.00015609994,0.004340298,0.0020135618,0.019035546,0.007876955,0.008733098,0.50619936,0.33065414],"study_design_scores_gemma":[0.00006028986,0.0002181665,0.58481556,0.00023045899,0.000058886002,0.0008487926,0.004446365,0.038544193,0.0059793876,0.0057886494,0.35893258,0.00007655861],"about_ca_topic_score_codex":0.08544731,"about_ca_topic_score_gemma":0.1314465,"teacher_disagreement_score":0.08544731,"about_ca_system_score_codex":0.0010491278,"about_ca_system_score_gemma":0.00040697094,"threshold_uncertainty_score":0.16989988},"labels":[],"label_agreement":null},{"id":"W7071587911","doi":"","title":"Self-Supervised Learning for Semantic Segmentation of Images","year":2023,"lang":"en","type":"dissertation","venue":"University Library (University of Saskatchewan)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canada First Research Excellence Fund","keywords":"Exploit; Segmentation; Pascal (unit); Redundancy (engineering); Artificial neural network; Image segmentation; Deep learning; Task (project management); Multi-task learning","score_opus":0.009497180765511948,"score_gpt":0.1920448176814285,"score_spread":0.18254763691591655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071587911","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017905997,0.001229975,0.97145003,0.0004476889,0.00008682099,0.00012607586,0.00038890136,0.002788963,0.0055754744],"genre_scores_gemma":[0.21026291,0.0016341863,0.7688578,0.00031166823,0.00017309924,0.0003230922,0.005023146,0.0007402269,0.012673877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917185,0.00018860889,0.000033511624,0.0003397368,0.00021444523,0.00005201412],"domain_scores_gemma":[0.9991455,0.0002939125,0.000086535474,0.00023548144,0.0001980439,0.000040391224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010539879,0.00076550414,0.0007117864,0.0011498907,0.0004996658,0.0008319106,0.0012784561,0.0009919939,0.0027141515],"category_scores_gemma":[0.002602939,0.00046012798,0.00092324737,0.0012105947,0.00092440046,0.0015213817,0.00095476443,0.0017200918,0.0017518972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001315645,0.0002443838,0.0007567253,0.00039585287,0.00010994209,0.000074646305,0.00027778724,0.15665695,0.018552318,0.027450712,0.024785742,0.7705633],"study_design_scores_gemma":[0.000011692256,0.00004430406,0.0006459205,0.000029401728,0.000013776963,0.00004289856,0.000047473874,0.94525886,0.011223977,0.032704793,0.009962731,0.000014162466],"about_ca_topic_score_codex":0.003158007,"about_ca_topic_score_gemma":0.006878551,"teacher_disagreement_score":0.003158007,"about_ca_system_score_codex":0.0012154834,"about_ca_system_score_gemma":0.0012242164,"threshold_uncertainty_score":0.009079754},"labels":[],"label_agreement":null},{"id":"W7071987064","doi":"","title":"T2K experience with laser calibration","year":2012,"lang":"en","type":"other","venue":"International Linear Collider","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"TRIUMF","funders":"","keywords":"","score_opus":0.01808301917448702,"score_gpt":0.26668556850221525,"score_spread":0.24860254932772824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071987064","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064739,0.005191205,0.74697703,0.004532978,0.0006091701,0.00009151986,0.0006199883,0.0073801884,0.16985893],"genre_scores_gemma":[0.48422214,0.0049838326,0.3611748,0.0008207885,0.00032819097,0.00008940758,0.0018453693,0.002402236,0.14413324],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991209,0.00029005847,0.00002256212,0.00021435496,0.00027978412,0.0000723596],"domain_scores_gemma":[0.99795663,0.0007896492,0.0000467327,0.0005455528,0.00047065536,0.00019073971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028832147,0.0004980842,0.00045308526,0.00034216294,0.00080138183,0.0016095471,0.001100382,0.0008693745,0.014559717],"category_scores_gemma":[0.0043367078,0.00030927753,0.00025872694,0.0009649152,0.0007650535,0.0029421945,0.001612183,0.001979746,0.0045840163],"study_design_candidate":"bench_or_experimental","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":[0.00044368042,0.00033028252,0.0019946196,0.00017295824,0.000061343315,0.00012659568,0.0011579322,0.034323543,0.013332715,0.02530754,0.051150165,0.87159854],"study_design_scores_gemma":[0.00012125489,0.00061169127,0.0062404615,0.00014624554,0.000056806854,0.0013319994,0.000981038,0.28014418,0.062399395,0.08588293,0.5619202,0.00016376053],"about_ca_topic_score_codex":0.004294076,"about_ca_topic_score_gemma":0.007245498,"teacher_disagreement_score":0.014559717,"about_ca_system_score_codex":0.00076465774,"about_ca_system_score_gemma":0.0006671431,"threshold_uncertainty_score":0.048707128},"labels":[],"label_agreement":null},{"id":"W7091188132","doi":"","title":"Iterative Amortized Inference: Unifying In-Context Learning and Learned Optimizers","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Leverage (statistics); Inference; Scalability; Task (project management); Generalization; Context (archaeology); Iterative and incremental development; Key (lock); Range (aeronautics)","score_opus":0.040171249076752305,"score_gpt":0.3162799663917387,"score_spread":0.27610871731498643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7091188132","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004168375,0.00041427618,0.99371696,0.00033939103,0.00003421206,0.000039269344,0.000038210193,0.0005927739,0.0006563856],"genre_scores_gemma":[0.43877718,0.00093046663,0.5525545,0.001150587,0.0003847724,0.00062230404,0.0004422807,0.00090086315,0.0042370763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716336,0.0013084775,0.00014813007,0.0007221284,0.00044302767,0.00021488847],"domain_scores_gemma":[0.9937453,0.0039223605,0.00038334966,0.0012720878,0.00044305052,0.0002337379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053953347,0.0022025711,0.0029095584,0.0011920814,0.00070066337,0.002803676,0.005853589,0.0033829408,0.0027284822],"category_scores_gemma":[0.01857941,0.0016978015,0.00169716,0.0013074988,0.0028268518,0.007036007,0.0052348175,0.0052752797,0.00090595265],"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":[0.00017202852,0.00014145808,0.0010127798,0.00020033131,0.00018164063,0.000082542385,0.00024116636,0.8162138,0.0016888268,0.08903145,0.0034510584,0.08758294],"study_design_scores_gemma":[0.000007969575,0.000021025126,0.000033549197,0.000012003382,0.000010980731,0.000011960759,0.0000054844913,0.9651694,0.0003765462,0.03387727,0.0004659857,0.0000077328095],"about_ca_topic_score_codex":0.004569184,"about_ca_topic_score_gemma":0.005513312,"teacher_disagreement_score":0.005853589,"about_ca_system_score_codex":0.002256768,"about_ca_system_score_gemma":0.00244374,"threshold_uncertainty_score":0.028533638},"labels":[],"label_agreement":null},{"id":"W7104045598","doi":"10.1109/tii.2025.3624577","title":"Retrospective Prototype Network Based on Center Difference Measure for Cross-Machine Few-Shot Fault Diagnosis","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Covariance matrix; Generalization; Fault (geology); Measure (data warehouse); Adaptability; Similarity measure; Similarity (geometry); Covariance","score_opus":0.06593037772478041,"score_gpt":0.3084342763249937,"score_spread":0.2425038986002133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104045598","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":[0.028530825,0.00058623013,0.96913546,0.00014113441,0.000061701525,0.00005748358,0.0000649939,0.0006001225,0.0008219255],"genre_scores_gemma":[0.8153148,0.0003899995,0.18118778,0.00021060022,0.000097672935,0.00020827416,0.0004685315,0.00012949065,0.0019927358],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989316,0.00021356596,0.00007184693,0.00039383047,0.0002940105,0.00009520387],"domain_scores_gemma":[0.99794537,0.00076967216,0.00025413817,0.00022272744,0.00071232405,0.00009575989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016668529,0.0011881205,0.0016475517,0.001819867,0.00062036526,0.0011186746,0.0022821864,0.0014276278,0.0012613775],"category_scores_gemma":[0.0068593817,0.0004897586,0.000942677,0.0011927282,0.0006980271,0.0026054236,0.0014282152,0.0012541005,0.00032591398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036539615,0.00018862655,0.006645167,0.0001821359,0.00022507679,0.0002066127,0.00025192997,0.48487327,0.007586851,0.006613533,0.0030248829,0.4898365],"study_design_scores_gemma":[0.0000051208604,0.000053316642,0.00036947266,0.0000062836766,0.000016423144,0.00005428258,0.000015008762,0.9955449,0.0013477204,0.0022739358,0.0003032115,0.000010332562],"about_ca_topic_score_codex":0.002969039,"about_ca_topic_score_gemma":0.00253033,"teacher_disagreement_score":0.002969039,"about_ca_system_score_codex":0.0009018677,"about_ca_system_score_gemma":0.0008198266,"threshold_uncertainty_score":0.008815289},"labels":[],"label_agreement":null},{"id":"W7106207613","doi":"","title":"Diffusion As Self-Distillation: End-to-End Latent Diffusion In One Model","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Diffusion; Modular design; Unification; Key (lock); Modularity (biology); Stability (learning theory); Component (thermodynamics); Analogy","score_opus":0.042514068482562575,"score_gpt":0.27240779528010434,"score_spread":0.22989372679754178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106207613","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":[0.030818079,0.00017677437,0.96415085,0.000407352,0.000048900492,0.000045090295,0.00007545481,0.002594568,0.0016829917],"genre_scores_gemma":[0.7007183,0.0001887853,0.2904658,0.00042720116,0.00005155392,0.00016881662,0.00041331787,0.0006110181,0.006955225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995602,0.00012982947,0.000019222854,0.00015413632,0.000076793374,0.000059877675],"domain_scores_gemma":[0.9988262,0.000517158,0.00008383074,0.00032796062,0.00014640878,0.0000983956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013126327,0.0009466147,0.0008331053,0.0003363685,0.0004916771,0.00080091157,0.0019879653,0.0014128643,0.0022959427],"category_scores_gemma":[0.0037265401,0.0006559154,0.00062007265,0.0003614784,0.0013277745,0.0034315018,0.002515905,0.0035121108,0.0007814253],"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":[0.0004262003,0.00023653229,0.0014708441,0.00017667541,0.000099119534,0.00014691058,0.0003328185,0.6812823,0.028078908,0.06040784,0.005809958,0.22153196],"study_design_scores_gemma":[0.000010698612,0.000023783468,0.00004638648,0.0000036549284,0.0000045287943,0.000016657239,0.0000059574295,0.9869697,0.0038177632,0.008622831,0.00047085749,0.000007120643],"about_ca_topic_score_codex":0.0034916739,"about_ca_topic_score_gemma":0.0060118735,"teacher_disagreement_score":0.0034916739,"about_ca_system_score_codex":0.0009471718,"about_ca_system_score_gemma":0.0011493783,"threshold_uncertainty_score":0.007680714},"labels":[],"label_agreement":null},{"id":"W7106256026","doi":"10.48550/arxiv.2511.15633","title":"Hierarchical Semantic Tree Anchoring for CLIP-Based Class-Incremental Learning","year":2025,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"Forgetting; Tree (set theory); Class (philosophy); Tree structure; Semantics (computer science); Visualization; On the fly; Graph","score_opus":0.08203057251551536,"score_gpt":0.22224838192085383,"score_spread":0.14021780940533846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106256026","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03513269,0.0005361324,0.9559405,0.00030851303,0.00009573616,0.0001117876,0.00046193443,0.0058658016,0.0015468247],"genre_scores_gemma":[0.69860244,0.00045860725,0.29191554,0.00064393226,0.00017090957,0.0002532823,0.0031258836,0.0005202232,0.0043091513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994473,0.00008874901,0.000023795343,0.00023657667,0.00013238072,0.000071204726],"domain_scores_gemma":[0.9984522,0.00052498665,0.0001326969,0.0005063227,0.00025467714,0.0001290844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092003914,0.0011480766,0.001112716,0.0011698133,0.0006359605,0.0008741007,0.003301123,0.0013526807,0.0029053146],"category_scores_gemma":[0.00410919,0.00052211236,0.0010406352,0.0014045486,0.001144027,0.0032929042,0.0023299484,0.0028840906,0.0010415526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035340057,0.00036719817,0.0033189731,0.00024982088,0.00014160342,0.0002620648,0.00044494102,0.22644243,0.0145507185,0.017015476,0.021991242,0.7148621],"study_design_scores_gemma":[0.000017735,0.00005851454,0.00030403896,0.000010863191,0.000019701207,0.000048068297,0.00002915217,0.9757547,0.0044719675,0.017386803,0.0018848625,0.000013714195],"about_ca_topic_score_codex":0.0068297386,"about_ca_topic_score_gemma":0.009636495,"teacher_disagreement_score":0.0068297386,"about_ca_system_score_codex":0.0010639279,"about_ca_system_score_gemma":0.0011661402,"threshold_uncertainty_score":0.013579965},"labels":[],"label_agreement":null},{"id":"W7106476855","doi":"","title":"Hierarchical Semantic Tree Anchoring for CLIP-Based Class-Incremental Learning","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"Forgetting; Tree (set theory); Class (philosophy); Tree structure; Semantics (computer science); Visualization; On the fly; Graph","score_opus":0.04394156600395886,"score_gpt":0.297814731444721,"score_spread":0.2538731654407621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106476855","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":[0.039830036,0.0006565065,0.9500404,0.00034230636,0.00011274746,0.00012032493,0.0005240172,0.00660551,0.001768086],"genre_scores_gemma":[0.71682334,0.0005037811,0.27333462,0.00066997216,0.00017455698,0.00024667074,0.003340775,0.00052078255,0.0043855044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994578,0.000085639665,0.000023614804,0.00022915239,0.00013225617,0.00007154445],"domain_scores_gemma":[0.99852836,0.0005043899,0.00012588379,0.00047219812,0.00024858734,0.000120579265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008937935,0.0011772391,0.001131635,0.0011645633,0.00062807783,0.0008783152,0.0033208271,0.001349451,0.002893374],"category_scores_gemma":[0.0038852587,0.00052767707,0.0010491941,0.0013870965,0.001097252,0.0032636188,0.002214899,0.002885753,0.0010650022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034966218,0.00036373874,0.0032104144,0.00025194834,0.00014076324,0.00026337965,0.00041879507,0.22311036,0.013609837,0.015568003,0.022624265,0.72008884],"study_design_scores_gemma":[0.000018501272,0.0000625517,0.00030551548,0.000011483472,0.000021149033,0.00005157551,0.00002975531,0.9761381,0.004424624,0.016944997,0.001977546,0.000014175164],"about_ca_topic_score_codex":0.0071327724,"about_ca_topic_score_gemma":0.010057072,"teacher_disagreement_score":0.0071327724,"about_ca_system_score_codex":0.0010733769,"about_ca_system_score_gemma":0.0011627288,"threshold_uncertainty_score":0.014182508},"labels":[],"label_agreement":null},{"id":"W7115171614","doi":"10.23977/cpcs.2025.090112","title":"Simplified Research on Daily Item Image Classification Based on MobileNet","year":2025,"lang":"","type":"article","venue":"Computing Performance and Communication systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transfer of learning; Contextual image classification; Convolutional neural network; Image (mathematics); Mobile device; Deep learning; Artificial neural network","score_opus":0.11614622233409704,"score_gpt":0.38091365667920474,"score_spread":0.2647674343451077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115171614","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":[0.3353275,0.003303554,0.64629126,0.0012436109,0.00042648418,0.0002215187,0.000673323,0.0025152795,0.009997489],"genre_scores_gemma":[0.88069135,0.0012922152,0.10741442,0.0002700016,0.00015333461,0.00009794519,0.0010851023,0.0000729687,0.008922639],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999689,0.00005531103,0.0000129644195,0.00011875912,0.00007216787,0.000051743962],"domain_scores_gemma":[0.999587,0.0000982398,0.000034869598,0.00008699705,0.00015801881,0.0000348826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043502697,0.00062848954,0.0006689624,0.0009221327,0.00027483594,0.00069581775,0.0010266942,0.00070426805,0.0021348891],"category_scores_gemma":[0.0012843559,0.0002573598,0.00052001345,0.0010797164,0.00035359364,0.0023637838,0.000508127,0.00084828,0.0010699899],"study_design_candidate":"bench_or_experimental","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":[0.0005080227,0.0004289495,0.012765384,0.00025943838,0.0001535362,0.0004665241,0.00018628444,0.1275777,0.02668708,0.009568342,0.009057034,0.81234175],"study_design_scores_gemma":[0.0000064989963,0.00008934591,0.0033747717,0.0000139724925,0.00002157518,0.000104262675,0.000060235718,0.98542655,0.005926101,0.0027084446,0.0022559087,0.0000123728405],"about_ca_topic_score_codex":0.010334925,"about_ca_topic_score_gemma":0.008475801,"teacher_disagreement_score":0.010334925,"about_ca_system_score_codex":0.0007993783,"about_ca_system_score_gemma":0.0005199634,"threshold_uncertainty_score":0.020549536},"labels":[],"label_agreement":null},{"id":"W7117255321","doi":"10.1109/tim.2025.3645912","title":"Robust Weakly Supervised Bearing Fault Diagnosis via Dual Learning and Curriculum Strategies","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Science Research of Jiangsu Higher Education Institutions of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Generalization; Transfer of learning; Fault (geology); Noise (video); Domain adaptation; Domain (mathematical analysis); Dual (grammatical number); Adaptation (eye); Feature (linguistics)","score_opus":0.0411241056453807,"score_gpt":0.2631874506391979,"score_spread":0.2220633449938172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117255321","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":[0.03601159,0.00021648633,0.961536,0.00023720521,0.000031389463,0.00003682618,0.00004375745,0.0007232153,0.0011635321],"genre_scores_gemma":[0.881755,0.00012195296,0.11469663,0.00025556283,0.000047821228,0.00009937273,0.0002491074,0.00008690138,0.0026876414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949336,0.00015247516,0.000022378194,0.00016203953,0.00010690148,0.00006287807],"domain_scores_gemma":[0.9988865,0.0005088071,0.00015306011,0.00014018048,0.00022283608,0.00008866707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011874773,0.000965439,0.0009268492,0.0005479024,0.0003660479,0.00072135316,0.0014333642,0.0009862239,0.0010918987],"category_scores_gemma":[0.004382187,0.00034809334,0.0005207984,0.00035643793,0.0009475144,0.0014693688,0.002438676,0.0015090766,0.00038773904],"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":[0.00026671827,0.00018193775,0.002948488,0.00012583785,0.00006400609,0.00016129663,0.00017264152,0.70617604,0.013635309,0.009316128,0.0025560416,0.26439553],"study_design_scores_gemma":[0.0000046389537,0.000028499568,0.00011554903,0.000003432353,0.0000033367212,0.000017535434,0.000007106963,0.9950138,0.0016014185,0.0030099137,0.00019081436,0.0000039260995],"about_ca_topic_score_codex":0.0018957559,"about_ca_topic_score_gemma":0.0018257363,"teacher_disagreement_score":0.0018957559,"about_ca_system_score_codex":0.0006332784,"about_ca_system_score_gemma":0.0010268873,"threshold_uncertainty_score":0.0062800646},"labels":[],"label_agreement":null},{"id":"W7124838817","doi":"10.1109/iccca66364.2025.11325387","title":"Next-Gen Species Recognition: Attribute-Based Zero-Shot Learning Techniques and Trends","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Benchmarking; Wildlife; Scalability; Domain (mathematical analysis); Representation (politics); Mechanism (biology); Generative grammar; Generative model","score_opus":0.07323985414675495,"score_gpt":0.28822278880074853,"score_spread":0.2149829346539936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124838817","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061827388,0.011606303,0.9036108,0.0017746513,0.00056397036,0.00019534359,0.0014173324,0.01094442,0.00805976],"genre_scores_gemma":[0.42736152,0.004367155,0.5455224,0.0014132637,0.00028744224,0.00019940213,0.009779441,0.0007336906,0.01033569],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99873656,0.00022375892,0.00005324349,0.00049375516,0.00041081058,0.000081910985],"domain_scores_gemma":[0.9986156,0.00046243132,0.0000764378,0.00033771805,0.0003960078,0.0001116725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002081115,0.0007411244,0.00087188225,0.0020318697,0.0006466734,0.0018198757,0.0033689125,0.0011750523,0.002603802],"category_scores_gemma":[0.0038495811,0.00043886018,0.0008035677,0.0015034662,0.0009231626,0.003236189,0.0020608834,0.0021087725,0.0018567329],"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":[0.00017068202,0.00027735857,0.0053641223,0.00032796038,0.00010636343,0.00009338076,0.00023704399,0.024105303,0.011454806,0.0064272056,0.012854976,0.9385808],"study_design_scores_gemma":[0.000016144022,0.00016186456,0.003988372,0.00008273306,0.00004783311,0.00035275222,0.00023792867,0.92680687,0.016994216,0.02692719,0.024329385,0.00005468274],"about_ca_topic_score_codex":0.008607894,"about_ca_topic_score_gemma":0.011665769,"teacher_disagreement_score":0.008607894,"about_ca_system_score_codex":0.0010704512,"about_ca_system_score_gemma":0.00092123396,"threshold_uncertainty_score":0.017115593},"labels":[],"label_agreement":null},{"id":"W7125609352","doi":"10.1109/icsai68704.2025.11345826","title":"Evaluating Zero-Shot and Few-Shot Learning on the Turkish Massive Multitask Language Understanding Dataset","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 Calgary; Dalhousie University","funders":"","keywords":"Turkish; Task (project management); Multi-task learning; Language acquisition; Task analysis; Feature (linguistics)","score_opus":0.17105220268155483,"score_gpt":0.38541365190289567,"score_spread":0.21436144922134084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125609352","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8265859,0.017871909,0.101916775,0.0010206149,0.0014265636,0.00057748065,0.013461393,0.026028981,0.0111103915],"genre_scores_gemma":[0.82279855,0.0017899711,0.06995296,0.0008081228,0.00026110205,0.0003411543,0.091019675,0.0010240102,0.012004573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973846,0.0007927079,0.00015520872,0.0010523734,0.00032926572,0.00028596603],"domain_scores_gemma":[0.99632925,0.0020273258,0.00011415542,0.00063629984,0.0005695064,0.0003234843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034669933,0.0031396705,0.0021170217,0.0021909676,0.0010384138,0.0015361492,0.00234195,0.0030828277,0.0027505297],"category_scores_gemma":[0.008298196,0.00038497077,0.0016639513,0.0013826755,0.0007713109,0.00396946,0.002327086,0.0023682816,0.0024402705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003609204,0.003293685,0.011382058,0.0019256661,0.0016761532,0.0007050824,0.00064279867,0.10757318,0.024647215,0.0018390972,0.091948,0.75075793],"study_design_scores_gemma":[0.00038049676,0.0018163221,0.016354995,0.0001808933,0.0006748243,0.00076207676,0.0014161081,0.92911357,0.027064243,0.0050064726,0.017032443,0.00019756371],"about_ca_topic_score_codex":0.017323319,"about_ca_topic_score_gemma":0.026217932,"teacher_disagreement_score":0.017323319,"about_ca_system_score_codex":0.0012133614,"about_ca_system_score_gemma":0.0016237645,"threshold_uncertainty_score":0.034444988},"labels":[],"label_agreement":null},{"id":"W7125769103","doi":"10.21428/594757db.516b1ccb","title":"Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Forgetting; Robustness (evolution); Adaptability; Recall; Quality (philosophy); Selection (genetic algorithm); Incremental learning","score_opus":0.007261221894014275,"score_gpt":0.21879043933585907,"score_spread":0.2115292174418448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125769103","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11232112,0.00053428975,0.88164353,0.00015964438,0.000059099813,0.000073155585,0.00005304275,0.0038110502,0.0013450381],"genre_scores_gemma":[0.78979,0.0002999793,0.20644873,0.000221377,0.000032012227,0.000117617965,0.00015442554,0.00017682397,0.002759057],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996425,0.00009075826,0.000026788563,0.00011670685,0.00008786774,0.000035389985],"domain_scores_gemma":[0.99852484,0.00066242355,0.00014277475,0.00029489843,0.00027827066,0.0000968807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001300026,0.00090220466,0.0007883726,0.00055265694,0.00026458447,0.0007459863,0.002015044,0.000718912,0.001258272],"category_scores_gemma":[0.0040556975,0.0003339295,0.00054040086,0.00033844035,0.0005376637,0.0016548219,0.0015214368,0.0009598712,0.0005786708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007488336,0.0006412111,0.0042879367,0.00026421962,0.00018996489,0.00022245798,0.00051209365,0.33420238,0.037640408,0.0043818196,0.0017723226,0.6151364],"study_design_scores_gemma":[0.000019275762,0.00022403424,0.00036324037,0.000008884088,0.00003198136,0.00006844613,0.000031869742,0.9845338,0.012015467,0.0019790723,0.0007071407,0.000016711016],"about_ca_topic_score_codex":0.002923343,"about_ca_topic_score_gemma":0.003636009,"teacher_disagreement_score":0.002923343,"about_ca_system_score_codex":0.0004879813,"about_ca_system_score_gemma":0.0010375506,"threshold_uncertainty_score":0.0068752766},"labels":[],"label_agreement":null},{"id":"W7125775593","doi":"10.21428/594757db.57f452f3","title":"Meta-Unsupervised Representation Learning: A New Approach to Factorization and Interpretability","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University","funders":"","keywords":"Interpretability; Matrix decomposition; Robustness (evolution); Initialization; Non-negative matrix factorization; Feature learning; Matrix completion; Semi-supervised learning; Unsupervised learning; Exploit","score_opus":0.08424689758426006,"score_gpt":0.30723650185495693,"score_spread":0.22298960427069686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125775593","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":[0.0010857347,0.00018736313,0.99801683,0.00016355385,0.000016378915,0.00001964857,0.000039513885,0.00012227781,0.00034872082],"genre_scores_gemma":[0.22924273,0.0012089675,0.76464915,0.0005670282,0.0004713097,0.00054523395,0.00068084145,0.0004083882,0.0022264388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962197,0.001807767,0.00021861604,0.000896242,0.0006958853,0.00016181516],"domain_scores_gemma":[0.98859453,0.0067901793,0.001219202,0.0022082,0.00096508826,0.00022282652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056471084,0.002260956,0.0024550532,0.0025633897,0.00081254233,0.0029757286,0.0033151882,0.0022821873,0.0018149699],"category_scores_gemma":[0.020346653,0.0011977935,0.0035260846,0.0023583085,0.0034372364,0.0052152155,0.003916488,0.0053602625,0.00068803376],"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":[0.0001375761,0.00017477277,0.0024176557,0.0005319023,0.0006261653,0.00027815584,0.0006228233,0.54493195,0.0064216345,0.23304059,0.004387703,0.20642899],"study_design_scores_gemma":[0.000014167523,0.00005235736,0.00021530753,0.000048090333,0.00003422502,0.00006983482,0.000023357094,0.87126297,0.0009787667,0.12514518,0.002130488,0.000025222931],"about_ca_topic_score_codex":0.002202913,"about_ca_topic_score_gemma":0.002424274,"teacher_disagreement_score":0.0056471084,"about_ca_system_score_codex":0.0015121232,"about_ca_system_score_gemma":0.0019241999,"threshold_uncertainty_score":0.029865146},"labels":[],"label_agreement":null},{"id":"W7125974112","doi":"10.1109/smc58881.2025.11342706","title":"ETAGE: Enhanced Test Time Adaptation with Integrated Entropy and Gradient Norms for Robust Model Performance","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Overfitting; Entropy (arrow of time); Test data; Minification; Heuristics; Training set; Cross entropy; Logarithm","score_opus":0.015875769273719482,"score_gpt":0.21956105096054235,"score_spread":0.20368528168682287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125974112","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":[0.01590565,0.0005899718,0.97498715,0.00018713369,0.00016416566,0.000103818886,0.00025265684,0.006594566,0.0012148921],"genre_scores_gemma":[0.42671835,0.0004797041,0.55972016,0.00073414994,0.00025928032,0.000535351,0.0025363592,0.002320381,0.0066962927],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989312,0.00033004754,0.000071750765,0.0002861119,0.00029131316,0.00008953369],"domain_scores_gemma":[0.9975885,0.0010807501,0.00014686286,0.0005575952,0.000461463,0.00016481931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024659447,0.0015284482,0.001397205,0.0010540306,0.0005329969,0.0012736068,0.0028118226,0.0017335915,0.0037116741],"category_scores_gemma":[0.010382468,0.00055617077,0.0010182506,0.0008244884,0.0008753072,0.0028895317,0.0028913636,0.0035200098,0.001884711],"study_design_candidate":"bench_or_experimental","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":[0.00045700214,0.00033908425,0.0024908416,0.00018338737,0.00022337204,0.00021063718,0.00017521271,0.35316727,0.023709718,0.009331787,0.017334241,0.5923774],"study_design_scores_gemma":[0.000019193214,0.00004658441,0.00023443613,0.000009056251,0.0000115174935,0.000054156648,0.000009699034,0.9902022,0.0041644056,0.004181769,0.001049377,0.000017464306],"about_ca_topic_score_codex":0.003562546,"about_ca_topic_score_gemma":0.004631239,"teacher_disagreement_score":0.0037116741,"about_ca_system_score_codex":0.00084553304,"about_ca_system_score_gemma":0.0014048163,"threshold_uncertainty_score":0.013041317},"labels":[],"label_agreement":null},{"id":"W7126079907","doi":"10.1109/bibm66473.2025.11356288","title":"Multi-Scale Global-Instance Prompt Tuning for Continual Test-Time Adaptation in Medical Image Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Adaptation (eye); Segmentation; Software deployment; Selection (genetic algorithm); Scale (ratio); Image segmentation","score_opus":0.0138640169930904,"score_gpt":0.3010424564485251,"score_spread":0.2871784394554347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126079907","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":[0.08061419,0.0009607817,0.9083605,0.00034164105,0.00017195976,0.0001432151,0.00023058911,0.007342753,0.0018343942],"genre_scores_gemma":[0.8304808,0.00038337108,0.16520311,0.00050957105,0.000110471636,0.00017985952,0.0006535841,0.0005994517,0.0018797667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888605,0.0002289894,0.000068647765,0.0004571737,0.00025185017,0.000107222055],"domain_scores_gemma":[0.997841,0.00087135914,0.0001753645,0.00053483894,0.00040439272,0.0001729783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016453383,0.0010152168,0.001021293,0.0005377021,0.00037275068,0.0010972808,0.001448096,0.0010107962,0.0014234307],"category_scores_gemma":[0.009504993,0.00034788498,0.0005115877,0.0006690635,0.0006729269,0.0020888478,0.00174185,0.0016600618,0.0006825686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085653295,0.00032231834,0.008804281,0.00028897444,0.00012892543,0.00046932252,0.000500066,0.29355705,0.06690589,0.004547162,0.009189959,0.6144296],"study_design_scores_gemma":[0.00004346317,0.00014963387,0.0026729617,0.00001954077,0.00004242053,0.00026336077,0.00009548516,0.96818805,0.018250206,0.006585131,0.0036495028,0.00004029623],"about_ca_topic_score_codex":0.0021834944,"about_ca_topic_score_gemma":0.0027740295,"teacher_disagreement_score":0.0021834944,"about_ca_system_score_codex":0.00058960763,"about_ca_system_score_gemma":0.0011405678,"threshold_uncertainty_score":0.008701503},"labels":[],"label_agreement":null},{"id":"W7126428129","doi":"10.21428/594757db.178a524d","title":"Consolidation using Multiple Task Learning with Context Inputsand Replay of CVAE Generated Pseudo-Examples","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Forgetting; Task (project management); Multi-task learning; Artificial neural network; Consolidation (business); Task analysis; Context (archaeology)","score_opus":0.03361176912610911,"score_gpt":0.26067500353550577,"score_spread":0.22706323440939666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126428129","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":[0.15925334,0.00035432255,0.83496743,0.0003162019,0.00010839276,0.000162914,0.00011530649,0.0022708757,0.0024512482],"genre_scores_gemma":[0.8461338,0.0001235211,0.15043618,0.00012480037,0.000030387462,0.00016430659,0.00029714665,0.00012926557,0.0025606325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995022,0.00013733403,0.00003326157,0.00015407563,0.00012064576,0.00005246311],"domain_scores_gemma":[0.9980122,0.00079878507,0.00017125218,0.00053690374,0.0003832059,0.00009763286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001138372,0.00086763705,0.0005856588,0.00039002506,0.00029430853,0.0007070171,0.001848427,0.00093360106,0.0019449819],"category_scores_gemma":[0.006312677,0.0005601435,0.00056587835,0.00034974088,0.0006669556,0.0022250756,0.0018477655,0.0017442071,0.0003951248],"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":[0.00037445273,0.00033747157,0.003272342,0.00016680693,0.00013067648,0.00026338556,0.00040963263,0.48266414,0.04744041,0.010297545,0.0022143456,0.45242882],"study_design_scores_gemma":[0.0000109774355,0.00007421534,0.00044224324,0.0000079416795,0.000010375026,0.000039241248,0.000017929564,0.9837546,0.010679247,0.0043531028,0.0005973314,0.000012783911],"about_ca_topic_score_codex":0.0025205298,"about_ca_topic_score_gemma":0.0035953596,"teacher_disagreement_score":0.0025205298,"about_ca_system_score_codex":0.0005533158,"about_ca_system_score_gemma":0.000660214,"threshold_uncertainty_score":0.006506622},"labels":[],"label_agreement":null},{"id":"W7126448957","doi":"10.21428/594757db.d0ee747e","title":"Liquid Ensemble Selection for Continual Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Delegation; Forgetting; Ensemble learning; Disjoint sets; Context (archaeology); Voting; Selection (genetic algorithm); Training set","score_opus":0.017142402566853336,"score_gpt":0.27455448013088934,"score_spread":0.257412077564036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126448957","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":[0.020759774,0.0005580409,0.97387075,0.00043310656,0.00011130801,0.00007311929,0.00008136589,0.0015007326,0.0026118124],"genre_scores_gemma":[0.7120269,0.00037498868,0.27752128,0.0006639589,0.0003757967,0.0004392968,0.0008754976,0.00046865092,0.007253661],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977933,0.0008324412,0.00008901663,0.00051167427,0.0005789639,0.00019465915],"domain_scores_gemma":[0.9949079,0.002519999,0.0002474997,0.0012129858,0.0008169641,0.00029457596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00510888,0.0009710653,0.0021163237,0.0010425459,0.0012477159,0.0013424405,0.002606224,0.0016847557,0.0038923132],"category_scores_gemma":[0.013935954,0.0005646163,0.0009563367,0.00084254757,0.0013509633,0.0026985814,0.0032444287,0.0028715844,0.0013265451],"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":[0.00037984506,0.00027287635,0.0037378913,0.00012927955,0.0001915656,0.00021772516,0.00025040755,0.61395055,0.004533052,0.047515087,0.011609106,0.31721264],"study_design_scores_gemma":[0.000008582922,0.00003393381,0.00013056134,0.000008061893,0.0000075321973,0.000025084348,0.000009537612,0.9822411,0.0008463914,0.015632689,0.0010496393,0.0000069433213],"about_ca_topic_score_codex":0.001825524,"about_ca_topic_score_gemma":0.0027727399,"teacher_disagreement_score":0.00510888,"about_ca_system_score_codex":0.0009938002,"about_ca_system_score_gemma":0.0013016677,"threshold_uncertainty_score":0.027018666},"labels":[],"label_agreement":null},{"id":"W7127291776","doi":"10.1109/ccece64018.2025.11364468","title":"Personalizing Human-Robot Communication using Statistically-Informed Domain Adaptation","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of Fredericton; University of New Brunswick","funders":"","keywords":"Adaptation (eye); Domain adaptation; Domain (mathematical analysis); Deep learning; Gesture; Extension (predicate logic)","score_opus":0.08046417793839049,"score_gpt":0.36269712503974993,"score_spread":0.28223294710135943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127291776","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":[0.017693356,0.000207758,0.9786952,0.00014707932,0.000040326304,0.00004888379,0.000057360565,0.0016055059,0.001504458],"genre_scores_gemma":[0.74487996,0.00029798716,0.24974205,0.00033727908,0.000084090665,0.0001864893,0.00030486964,0.00026024322,0.00390701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862134,0.0006445717,0.000038745293,0.00038950465,0.00019925555,0.000106609885],"domain_scores_gemma":[0.99774617,0.0011680957,0.00017103224,0.00061114074,0.00019924578,0.00010430452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017825535,0.0010897181,0.0007158898,0.00046654933,0.00049033033,0.0009315924,0.0013711982,0.0013316954,0.00220071],"category_scores_gemma":[0.0059227226,0.0004340246,0.0006542079,0.00049876544,0.0013596313,0.0021032179,0.0029909387,0.0018202906,0.0012276979],"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":[0.00046729358,0.00050433277,0.0023949628,0.00021646584,0.00017820322,0.00031814366,0.00067414565,0.50354356,0.054751813,0.012215213,0.006263859,0.418472],"study_design_scores_gemma":[0.000015681095,0.00013319642,0.0007719643,0.000015950945,0.00001393667,0.000115690054,0.00009346583,0.9628335,0.013150284,0.020484595,0.002334158,0.00003758293],"about_ca_topic_score_codex":0.0018019514,"about_ca_topic_score_gemma":0.002493444,"teacher_disagreement_score":0.00220071,"about_ca_system_score_codex":0.00068802654,"about_ca_system_score_gemma":0.0009701285,"threshold_uncertainty_score":0.00942713},"labels":[],"label_agreement":null},{"id":"W7131113352","doi":"10.1109/iccvw69036.2025.00500","title":"Towards Human-Like Invariance: Self-Supervised Learning with Feature-Level Rotation Alignment","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Robustness (evolution); Mental rotation; Rotation (mathematics); Hyperparameter; Representation (politics); Pattern recognition (psychology); Feature learning","score_opus":0.0283009825472242,"score_gpt":0.2717596301149755,"score_spread":0.24345864756775132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131113352","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039746236,0.00034131997,0.95276695,0.00021470357,0.000083875384,0.00007949239,0.00015445931,0.0048233727,0.0017895284],"genre_scores_gemma":[0.70676196,0.00020259428,0.28504065,0.0006154403,0.00013259926,0.00017839947,0.0015370338,0.0007669168,0.004764434],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99844676,0.00051433133,0.000052336887,0.00062591914,0.00024106306,0.000119600336],"domain_scores_gemma":[0.99788374,0.0006429602,0.00024307206,0.0007576205,0.00035543158,0.000117136544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021870816,0.0013182228,0.001215256,0.00070138526,0.00041859434,0.0009579782,0.0020575623,0.0010664617,0.0018464511],"category_scores_gemma":[0.0061926553,0.00047898988,0.0009919556,0.00077010493,0.0013193783,0.0018332718,0.0018096615,0.0022510889,0.0015296311],"study_design_candidate":"bench_or_experimental","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":[0.0003414687,0.000404124,0.0032368838,0.00017824657,0.00021948048,0.000115489245,0.0002087444,0.32650733,0.019069828,0.006544403,0.013669495,0.6295045],"study_design_scores_gemma":[0.000018994502,0.00007452566,0.00032023372,0.00000824031,0.00000955653,0.00003927275,0.000017774608,0.9903951,0.0034402048,0.004848171,0.00081649434,0.000011308028],"about_ca_topic_score_codex":0.0024118572,"about_ca_topic_score_gemma":0.003500041,"teacher_disagreement_score":0.0024118572,"about_ca_system_score_codex":0.00066681224,"about_ca_system_score_gemma":0.00093198445,"threshold_uncertainty_score":0.011566579},"labels":[],"label_agreement":null},{"id":"W7131240494","doi":"10.1109/dsis67228.2025.11390620","title":"Conventional Zero-Shot Learning with Semantic Graph-Enriched Non-Adversarial Synthetic Features","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Windsor","funders":"","keywords":"Generative grammar; Adversarial system; Classifier (UML); Semantic feature; Feature (linguistics); Training set; Graph","score_opus":0.011623751572422066,"score_gpt":0.24908018720942157,"score_spread":0.2374564356369995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131240494","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":[0.032316823,0.00033117106,0.9604937,0.00024424892,0.00006415827,0.00011331483,0.00031224452,0.0035062733,0.0026180518],"genre_scores_gemma":[0.77921605,0.00023471293,0.20833169,0.00068544486,0.00007962655,0.00023426501,0.0027206996,0.00052232697,0.007975264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934214,0.00018115273,0.000022108945,0.00023963868,0.00013062247,0.00008436855],"domain_scores_gemma":[0.9986745,0.0007171472,0.00008281236,0.00031900665,0.00012780233,0.00007865823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011832578,0.001294397,0.0010976786,0.0006941004,0.00038285542,0.0008299897,0.0025246956,0.00121558,0.0029481596],"category_scores_gemma":[0.0035063315,0.00047181416,0.0010436614,0.0004905225,0.0013614409,0.0016879997,0.002021705,0.002251411,0.0010623751],"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":[0.00028494964,0.00016116883,0.001730564,0.00017565362,0.00010338816,0.00017467771,0.000123275,0.76099575,0.0089393845,0.018773455,0.0071114283,0.20142636],"study_design_scores_gemma":[0.000006150931,0.000028081107,0.00010459319,0.0000052409982,0.0000047865688,0.000029325229,0.0000069206453,0.99073154,0.0015319977,0.007070526,0.0004751485,0.0000056998074],"about_ca_topic_score_codex":0.0033880472,"about_ca_topic_score_gemma":0.005173674,"teacher_disagreement_score":0.0033880472,"about_ca_system_score_codex":0.0010225085,"about_ca_system_score_gemma":0.0007927925,"threshold_uncertainty_score":0.009862602},"labels":[],"label_agreement":null},{"id":"W7131422250","doi":"10.1109/icdm65498.2025.00102","title":"Retrieval-Augmented Feature Generation for Domain-Specific Classification","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"National Science Foundation","keywords":"Interpretability; Feature (linguistics); Domain (mathematical analysis); Feature vector; Domain knowledge; Feature model; Pattern recognition (psychology)","score_opus":0.05892550197452328,"score_gpt":0.3007225011972353,"score_spread":0.24179699922271203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131422250","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024354778,0.000734778,0.96602136,0.00021156199,0.000071740826,0.00018749306,0.00050549244,0.006858787,0.0010539906],"genre_scores_gemma":[0.4040209,0.00038755435,0.58744615,0.00031694656,0.000107771026,0.00044935354,0.0043102517,0.00043842595,0.0025227254],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913496,0.00024814767,0.000058544058,0.0002927284,0.00019101263,0.00007457079],"domain_scores_gemma":[0.99767953,0.0011641855,0.00015098495,0.0006376454,0.00031153776,0.000056209825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015230298,0.0011263769,0.0010169903,0.002013865,0.00042896305,0.0006604745,0.001804974,0.0010172452,0.0026886617],"category_scores_gemma":[0.005090476,0.00029888964,0.0013148417,0.0015619113,0.00060095236,0.0018512107,0.001238109,0.0014184255,0.0013698009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027271482,0.0003880912,0.0028674006,0.00028590055,0.00010364996,0.00028339878,0.00015832881,0.04684821,0.02155796,0.005189051,0.013634969,0.90841043],"study_design_scores_gemma":[0.00008754768,0.00023156882,0.0014097387,0.000034066616,0.00008333433,0.0003473923,0.00008951559,0.9408949,0.020959841,0.025916455,0.009895994,0.000049639955],"about_ca_topic_score_codex":0.001828305,"about_ca_topic_score_gemma":0.003142422,"teacher_disagreement_score":0.0026886617,"about_ca_system_score_codex":0.000642638,"about_ca_system_score_gemma":0.00076619425,"threshold_uncertainty_score":0.00899452},"labels":[],"label_agreement":null},{"id":"W7132890024","doi":"","title":"Beyond Neural Collapse: Geometric Configurations of Deep Networks Trained with Mixup","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial neural network; Simplex; Classifier (UML); Deep neural networks; Cluster analysis; Deep learning; Training set; Generalization","score_opus":0.021548498798144446,"score_gpt":0.2996111152885296,"score_spread":0.2780626164903851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132890024","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4625586,0.0006505387,0.5260405,0.00094882207,0.000116500654,0.00011297631,0.00037026234,0.002294126,0.0069077304],"genre_scores_gemma":[0.96233755,0.00010691408,0.03479469,0.00029220284,0.000021403892,0.000074867254,0.00057122053,0.00033274858,0.0014684147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986646,0.00035209756,0.000067195724,0.0003807558,0.0002549238,0.00028051916],"domain_scores_gemma":[0.9977053,0.0007117419,0.00023010494,0.00073368475,0.00036901838,0.00025012597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025658305,0.0009831088,0.0009712152,0.0010922462,0.00089628325,0.0019994967,0.0017182841,0.0014660794,0.0035412079],"category_scores_gemma":[0.013814099,0.00085699133,0.000743305,0.00070597354,0.0025311909,0.0050289105,0.0037481368,0.0029462609,0.00076029665],"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":[0.0011986857,0.00031332826,0.017686982,0.00022511021,0.0003150737,0.0011624245,0.00080005644,0.7073654,0.034780297,0.06789745,0.008579796,0.15967542],"study_design_scores_gemma":[0.000022588314,0.0001652843,0.0024919964,0.000042222222,0.000019426547,0.00025660914,0.00017223076,0.94517547,0.01253272,0.037836183,0.0012482152,0.000037021786],"about_ca_topic_score_codex":0.0022943113,"about_ca_topic_score_gemma":0.003271967,"teacher_disagreement_score":0.0035412079,"about_ca_system_score_codex":0.0014262057,"about_ca_system_score_gemma":0.00085389306,"threshold_uncertainty_score":0.013569593},"labels":[],"label_agreement":null},{"id":"W7134097774","doi":"","title":"Généralisation de domaine en vision par ordinateur : apport des modèles pré-entraînés à grande échelle","year":2025,"lang":"fr","type":"dissertation","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Exploit; Robustness (evolution); Domain adaptation; Training set; Focus (optics); Domain (mathematical analysis); Task (project management); Generalization","score_opus":0.016821821576645718,"score_gpt":0.2470826615756214,"score_spread":0.23026083999897567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134097774","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08757948,0.0013523345,0.9055034,0.00057875953,0.00013145801,0.00009544958,0.00031549216,0.0027799315,0.0016637474],"genre_scores_gemma":[0.7405881,0.0011314205,0.24892196,0.0004352968,0.0001538841,0.0001535938,0.0016380385,0.0004936855,0.006483872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.999321,0.00016291955,0.000024779642,0.0003017699,0.000109801236,0.0000796851],"domain_scores_gemma":[0.9987179,0.0007088811,0.000068730995,0.00024106,0.00020086572,0.0000626213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019609744,0.001362014,0.0010601626,0.00092418835,0.00033094213,0.0016458443,0.0014611155,0.0017615092,0.00153176],"category_scores_gemma":[0.0047831787,0.00058134063,0.0015298547,0.0008499066,0.0007744067,0.0023936385,0.0013485196,0.0031210214,0.0015204403],"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":[0.0003167639,0.00020571236,0.0037968247,0.00017189338,0.00016209713,0.00019703049,0.0002669323,0.593194,0.016462836,0.00527456,0.004247771,0.37570354],"study_design_scores_gemma":[0.000005747915,0.00002778181,0.00057014177,0.000011903745,0.000011099172,0.00004793048,0.000029096742,0.99252456,0.0027838943,0.003023954,0.00095399946,0.0000098237515],"about_ca_topic_score_codex":0.010395179,"about_ca_topic_score_gemma":0.008126058,"teacher_disagreement_score":0.010395179,"about_ca_system_score_codex":0.00093090837,"about_ca_system_score_gemma":0.0008628117,"threshold_uncertainty_score":0.020669341},"labels":[],"label_agreement":null},{"id":"W7144138757","doi":"10.59400/cai3914","title":"MDL-AE: Investigating the trade-off between compressive fidelity and discriminative utility in self-supervised learning","year":2025,"lang":"","type":"article","venue":"Computing and artificial intelligence.","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"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":"Discriminative model; Feature learning; Heuristics; Bridging (networking); Autoencoder; Fidelity; Generative grammar; Chunking (psychology); Classifier (UML); Encoder","score_opus":0.06809786225321061,"score_gpt":0.32880314126532306,"score_spread":0.26070527901211243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7144138757","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":[0.08574997,0.0009735861,0.9077847,0.0009703455,0.00005008025,0.00012480086,0.00009739273,0.00080721127,0.0034418982],"genre_scores_gemma":[0.8473903,0.00035731448,0.148362,0.0005823347,0.00010955998,0.0001992671,0.000254409,0.00021024814,0.0025345462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981019,0.0009090255,0.00008266792,0.0004190078,0.000365375,0.00012197678],"domain_scores_gemma":[0.9906911,0.0069622663,0.00047819203,0.0010041673,0.0005488541,0.00031543718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047077043,0.0011233006,0.0010752267,0.0006361706,0.00046368962,0.0012808895,0.0021374442,0.002035458,0.0013125539],"category_scores_gemma":[0.016377496,0.00050194975,0.00043593487,0.0005076148,0.002177129,0.0034479501,0.0029243396,0.0025953627,0.0003811967],"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":[0.0003085391,0.00029596928,0.0032642772,0.0002998497,0.00012528118,0.00009851079,0.00024019931,0.7879677,0.0068375412,0.0385249,0.002613791,0.15942343],"study_design_scores_gemma":[0.000015837973,0.00008679698,0.00012239731,0.000008635891,0.0000051684938,0.000020461313,0.000012115772,0.9862054,0.0014685667,0.011809558,0.0002386199,0.0000064617084],"about_ca_topic_score_codex":0.0017621901,"about_ca_topic_score_gemma":0.0021550662,"teacher_disagreement_score":0.0047077043,"about_ca_system_score_codex":0.0012187028,"about_ca_system_score_gemma":0.0010365311,"threshold_uncertainty_score":0.024897039},"labels":[],"label_agreement":null},{"id":"W7144356592","doi":"10.71465/ajainn16","title":"Enhancing Neural Network Efficiency with Transfer Learning","year":2020,"lang":"","type":"article","venue":"American Journal of Artificial Intelligence and Neural Networks","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Artificial neural network; Transfer of learning; Adaptation (eye); Inductive transfer; Transfer (computing); Deep learning; Types of artificial neural networks","score_opus":0.03132343410501621,"score_gpt":0.2588744280083811,"score_spread":0.22755099390336492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7144356592","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":[0.063694045,0.0008010919,0.92839503,0.0006492723,0.0000812003,0.00011012961,0.000045414174,0.0014086,0.004815193],"genre_scores_gemma":[0.84050316,0.000528653,0.15483709,0.00032215595,0.00008554178,0.00021724841,0.00017145213,0.00019423348,0.0031404276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985739,0.0005784195,0.000099926525,0.00025796116,0.000357618,0.00013227513],"domain_scores_gemma":[0.9936818,0.003994782,0.0002946493,0.0010844924,0.00082374347,0.000120564786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00453464,0.0010244723,0.0015364826,0.0010702723,0.00060314056,0.0014715756,0.002471123,0.0019057747,0.002098109],"category_scores_gemma":[0.02380425,0.00045243316,0.0005368256,0.0012087771,0.0011735203,0.005223202,0.002843621,0.002264027,0.00081891345],"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":[0.00020076385,0.0002873745,0.0016490062,0.0001373655,0.00012919145,0.00010551863,0.00012268049,0.7796412,0.005570704,0.018345373,0.0018897037,0.19192106],"study_design_scores_gemma":[0.0000098159235,0.000041472744,0.00014172123,0.000006064534,0.000008651732,0.000017060049,0.000012882636,0.98570377,0.0021611114,0.011624932,0.00026806444,0.000004484493],"about_ca_topic_score_codex":0.0025361883,"about_ca_topic_score_gemma":0.0022708285,"teacher_disagreement_score":0.00453464,"about_ca_system_score_codex":0.0014051548,"about_ca_system_score_gemma":0.0013601572,"threshold_uncertainty_score":0.02398181},"labels":[],"label_agreement":null},{"id":"W7147718262","doi":"10.1109/cw68232.2025.00017","title":"Medical Open Set Recognition via Intra-Class Clustering","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Overconfidence effect; Cluster analysis; Class (philosophy); Set (abstract data type); Sample (material); Image (mathematics); Open set; Code (set theory)","score_opus":0.04841545657220808,"score_gpt":0.32176637326908575,"score_spread":0.27335091669687767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7147718262","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":[0.0760588,0.0016287573,0.90687996,0.00078951183,0.00023283526,0.00032958426,0.0009866654,0.009914793,0.0031790272],"genre_scores_gemma":[0.5790618,0.00068472273,0.40707624,0.0009802679,0.00019314412,0.00029879477,0.006459155,0.0006745023,0.004571255],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977149,0.000442303,0.00015748614,0.00077539094,0.00065078033,0.0002591765],"domain_scores_gemma":[0.9968554,0.0010416758,0.00024084875,0.0009532123,0.00072192325,0.00018685688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024010467,0.0013261524,0.001820016,0.0021978521,0.0010105964,0.0017496187,0.0040388065,0.002558088,0.002064023],"category_scores_gemma":[0.0074598272,0.00048365036,0.0015904466,0.0017142663,0.0012449552,0.002814115,0.0032065546,0.0034004997,0.0020725462],"study_design_candidate":"bench_or_experimental","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":[0.0004892164,0.0004755295,0.0054262215,0.00019563618,0.00020014115,0.00022779283,0.00036507187,0.13250457,0.011890523,0.006953231,0.017944563,0.8233275],"study_design_scores_gemma":[0.000015885033,0.00007562284,0.00087692495,0.000023072043,0.000019929275,0.00019672322,0.000112837064,0.9721582,0.009345426,0.014958879,0.00218318,0.000033419652],"about_ca_topic_score_codex":0.007917292,"about_ca_topic_score_gemma":0.0086395005,"teacher_disagreement_score":0.007917292,"about_ca_system_score_codex":0.0014083225,"about_ca_system_score_gemma":0.0013781289,"threshold_uncertainty_score":0.015742421},"labels":[],"label_agreement":null},{"id":"W7151571164","doi":"10.1109/icosec67334.2025.11459725","title":"Towards Fine-Grained Biodiversity Monitoring: A Review on Attribute-based Zero-Shot Learning and Domain Adaptation for Species Recognition","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Biodiversity; Domain (mathematical analysis); Adaptation (eye); Domain adaptation; Key (lock)","score_opus":0.10204283207968726,"score_gpt":0.3055342787440043,"score_spread":0.20349144666431704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7151571164","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011269751,0.37933105,0.60188764,0.0012158353,0.0005566542,0.000118114,0.00029882684,0.0011041343,0.00421799],"genre_scores_gemma":[0.21425062,0.44148013,0.33313844,0.0018400914,0.0017730542,0.00044109917,0.0025251603,0.0003650682,0.004186438],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989748,0.00019703618,0.00010599301,0.00042088295,0.00025371922,0.00004758872],"domain_scores_gemma":[0.99808073,0.0012574188,0.00011406625,0.0001746778,0.00030780947,0.00006524257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019777995,0.0014145755,0.0017825731,0.0017803243,0.00031402873,0.0014825248,0.0021889159,0.0013507626,0.0012346302],"category_scores_gemma":[0.00465795,0.00044875513,0.001369853,0.0026132665,0.0010071257,0.0028020781,0.0014997006,0.0016447948,0.00090853253],"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":[0.000065747096,0.00015511048,0.0013088866,0.0036521745,0.00020825514,0.00006410101,0.0001370568,0.021399349,0.0035535817,0.007098666,0.005898289,0.95645887],"study_design_scores_gemma":[0.000058580383,0.001140693,0.010467662,0.0039021964,0.0010080375,0.0019372648,0.0007068337,0.6351702,0.022857148,0.11920303,0.20316081,0.00038756235],"about_ca_topic_score_codex":0.0021988584,"about_ca_topic_score_gemma":0.0012707224,"teacher_disagreement_score":0.0021988584,"about_ca_system_score_codex":0.00060431386,"about_ca_system_score_gemma":0.0010221491,"threshold_uncertainty_score":0.010459721},"labels":[],"label_agreement":null},{"id":"W7160037063","doi":"10.1109/iccv51701.2025.00027","title":"Bootstrapping Grounded Chain-of-Thought in Multimodal Llms for Data-Efficient Model Adaptation","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Bootstrapping (finance); Adaptation (eye); Process (computing); Key (lock)","score_opus":0.0995441739132138,"score_gpt":0.3358859772925355,"score_spread":0.2363418033793217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160037063","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008345623,0.00038744125,0.9893119,0.00018243297,0.00004748091,0.00005904008,0.000066603636,0.0011670516,0.0004324673],"genre_scores_gemma":[0.544462,0.00044872233,0.44781867,0.00080498995,0.00023061885,0.0005206807,0.001119704,0.0008900247,0.0037045907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981736,0.0009200113,0.00010653647,0.00043847164,0.00020823331,0.00015312823],"domain_scores_gemma":[0.9880249,0.009959202,0.00028719683,0.0009013152,0.0005154429,0.00031198296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00376699,0.0015019559,0.003108902,0.0013578719,0.0011114151,0.0017203989,0.0034448921,0.003844207,0.006311104],"category_scores_gemma":[0.021890897,0.001462961,0.0018783405,0.0012385366,0.0016931817,0.0032902714,0.0052673714,0.0046994714,0.0020135085],"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":[0.00096240436,0.0003251756,0.0012505698,0.00042213596,0.00033366834,0.00038224048,0.0007089982,0.60522455,0.0069440664,0.027576357,0.0056430046,0.35022688],"study_design_scores_gemma":[0.000011372649,0.000022169288,0.000047601276,0.000013821171,0.000010770962,0.000013092574,0.000016668333,0.9844712,0.00049771357,0.014677833,0.00020902001,0.000008845388],"about_ca_topic_score_codex":0.0054719676,"about_ca_topic_score_gemma":0.008121286,"teacher_disagreement_score":0.006311104,"about_ca_system_score_codex":0.0010928947,"about_ca_system_score_gemma":0.0014075884,"threshold_uncertainty_score":0.0211128},"labels":[],"label_agreement":null},{"id":"W7160105901","doi":"10.1109/iccv51701.2025.02418","title":"SL <sup>2</sup> A-INR: Single-Layer Learnable Activation for Implicit Neural Representation","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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 British Columbia","funders":"","keywords":"Representation (politics); Artificial neural network; Set (abstract data type); Feature (linguistics); Generalization","score_opus":0.056732732713212865,"score_gpt":0.3161163749667324,"score_spread":0.2593836422535195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160105901","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006520773,0.00079426734,0.9692663,0.0007557812,0.00094020634,0.00006187987,0.0019561292,0.010684184,0.009020404],"genre_scores_gemma":[0.26352617,0.0009862322,0.6658653,0.0011013228,0.00053551886,0.00020791378,0.008640125,0.0038755196,0.055262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997936,0.000043443688,0.00001442431,0.00005704572,0.00006430426,0.000027179001],"domain_scores_gemma":[0.99952817,0.00013677805,0.00002266221,0.00017320222,0.00009800644,0.000041127638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000546452,0.0006967537,0.000479342,0.00031803502,0.0002076838,0.00088817853,0.0015496358,0.0013251922,0.025084842],"category_scores_gemma":[0.002344864,0.00029421452,0.00035062456,0.0004946773,0.00046940992,0.0012903486,0.0010386889,0.0017702888,0.010685725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068188476,0.00013550976,0.0004340346,0.0004381569,0.0000730189,0.0002476582,0.00006629358,0.049349062,0.043675307,0.045674592,0.15500586,0.7042186],"study_design_scores_gemma":[0.000026774986,0.00007869411,0.0002665373,0.000039433886,0.000018173221,0.00015623306,0.000014670853,0.8944621,0.04025245,0.03600246,0.028658282,0.000024270546],"about_ca_topic_score_codex":0.0025547151,"about_ca_topic_score_gemma":0.0062622777,"teacher_disagreement_score":0.025084842,"about_ca_system_score_codex":0.00036707215,"about_ca_system_score_gemma":0.0005843658,"threshold_uncertainty_score":0.0839172},"labels":[],"label_agreement":null},{"id":"W7160126070","doi":"10.1109/iccv51701.2025.01907","title":"Zero-Shot Compositional Video Learning with Coding Rate Reduction","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"Ministry of Science and ICT, South Korea","keywords":"Coding (social sciences); Data compression; Pattern recognition (psychology); Encoding (memory); Feature (linguistics)","score_opus":0.02225010630115013,"score_gpt":0.2667565170985882,"score_spread":0.2445064107974381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160126070","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":[0.009492862,0.00029583037,0.98883003,0.000085698186,0.00005037643,0.000043761793,0.0000601591,0.0005262269,0.00061515876],"genre_scores_gemma":[0.35133564,0.00071219186,0.6397087,0.00033866375,0.00016682407,0.00022750106,0.0010140945,0.00033958937,0.0061567603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994722,0.000149524,0.000025747016,0.00015553435,0.0001374687,0.000059511094],"domain_scores_gemma":[0.9989188,0.00048201397,0.000046140896,0.00023650096,0.00024607597,0.00007060247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011692323,0.0008769646,0.0012355419,0.00073223154,0.00035427013,0.00069877395,0.0015370124,0.0013453823,0.0024867388],"category_scores_gemma":[0.0036932318,0.00041201204,0.0009210909,0.0007409923,0.0006711204,0.0016759193,0.0019820114,0.0020696209,0.0011290229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005378325,0.0003620512,0.0008259942,0.0002805316,0.00016071142,0.00012803261,0.00013720995,0.13565966,0.06125259,0.015719865,0.004577239,0.78035825],"study_design_scores_gemma":[0.000014735337,0.000071035705,0.00026308326,0.000011956196,0.00002588254,0.00007424667,0.000017827191,0.9799218,0.011634461,0.007038228,0.0009140209,0.0000127125795],"about_ca_topic_score_codex":0.0027951382,"about_ca_topic_score_gemma":0.003914755,"teacher_disagreement_score":0.0027951382,"about_ca_system_score_codex":0.00042352264,"about_ca_system_score_gemma":0.00088245625,"threshold_uncertainty_score":0.008318961},"labels":[],"label_agreement":null},{"id":"W7160168152","doi":"10.1109/iccv51701.2025.00253","title":"Task-Aware Prompt Gradient Projection for Parameter-Efficient Tuning Federated Class-Incremental Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office of International Science and Engineering; Natural Sciences and Engineering Research Council of Canada","keywords":"Projection (relational algebra); Key (lock); Feature (linguistics); Noise (video); Matching (statistics)","score_opus":0.02931315761812395,"score_gpt":0.2870820392168526,"score_spread":0.2577688815987286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160168152","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":[0.021851586,0.0008868691,0.96506596,0.00023388684,0.00018789805,0.00012388855,0.00028840653,0.009733327,0.0016282194],"genre_scores_gemma":[0.60902697,0.0003994191,0.38073897,0.00066030695,0.0001598167,0.00031957807,0.0017170988,0.0010470711,0.005930823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914,0.00016872943,0.000043254193,0.0003218012,0.00019995616,0.00012617417],"domain_scores_gemma":[0.99841785,0.0006401312,0.00006398983,0.00041760568,0.00033447953,0.00012587225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013410929,0.0016382586,0.0027217008,0.0009074602,0.00067849463,0.001393996,0.0040138927,0.002137071,0.005207295],"category_scores_gemma":[0.006068542,0.0007465648,0.0008983248,0.00097561534,0.00073923013,0.002553815,0.0031126018,0.0029467803,0.0022391598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066401006,0.00056858547,0.001263856,0.00024118192,0.00014199056,0.00017147728,0.00018099748,0.1514766,0.01896443,0.006245211,0.01904657,0.8010351],"study_design_scores_gemma":[0.000023488157,0.000037101287,0.00017901539,0.000008776331,0.000016477685,0.000039406605,0.000016498443,0.99047524,0.003131925,0.0052815867,0.00077792304,0.000012627905],"about_ca_topic_score_codex":0.0077228257,"about_ca_topic_score_gemma":0.012801752,"teacher_disagreement_score":0.0077228257,"about_ca_system_score_codex":0.0007256559,"about_ca_system_score_gemma":0.0019188784,"threshold_uncertainty_score":0.017420173},"labels":[],"label_agreement":null},{"id":"W7160172947","doi":"10.1109/iccv51701.2025.01208","title":"AdaDCP: Learning an Adapter with Discrete Cosine Prior for Clear-to-Adverse Domain Generalization","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Generalization; Domain (mathematical analysis); Discrete cosine transform; Pattern recognition (psychology); Adapter (computing)","score_opus":0.015032862095317161,"score_gpt":0.278737355333836,"score_spread":0.2637044932385188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160172947","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":[0.008968285,0.00044823124,0.980475,0.00017362244,0.00016748316,0.00012103715,0.00032470678,0.00817104,0.0011505587],"genre_scores_gemma":[0.22941986,0.0005476583,0.75234157,0.0008756872,0.00018851318,0.00040290176,0.0038781364,0.0014365892,0.010909038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986137,0.00032076234,0.00007197307,0.00047697694,0.00036944315,0.00014707772],"domain_scores_gemma":[0.9983063,0.00046048642,0.000060262628,0.0006506139,0.00036276723,0.0001596995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024008092,0.0015603758,0.0020093627,0.0011576981,0.000747899,0.0012143592,0.0039545693,0.0028458447,0.006823066],"category_scores_gemma":[0.0059546586,0.0007927767,0.0011903958,0.0011863003,0.001018749,0.0027838477,0.0048735347,0.0047797863,0.0045880238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007209195,0.0005085137,0.0011594753,0.0001945103,0.00014877853,0.00016539061,0.000113747854,0.06973195,0.01581335,0.011953814,0.028402498,0.8710871],"study_design_scores_gemma":[0.000046156445,0.0001148025,0.0002917867,0.000018957948,0.00002476736,0.000106241605,0.000034783974,0.97217697,0.010008927,0.012708625,0.0044457274,0.000022150074],"about_ca_topic_score_codex":0.007059773,"about_ca_topic_score_gemma":0.009154134,"teacher_disagreement_score":0.007059773,"about_ca_system_score_codex":0.0008486588,"about_ca_system_score_gemma":0.0017970445,"threshold_uncertainty_score":0.02282542},"labels":[],"label_agreement":null},{"id":"W771161470","doi":"10.48550/arxiv.1507.00066","title":"Fast Cross-Validation for Incremental Learning","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.12802089563459243,"score_gpt":0.2379294389769891,"score_spread":0.10990854334239666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W771161470","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041161445,0.00038416238,0.9923273,0.00008137151,0.00007632039,0.00008757381,0.00009469473,0.0023362695,0.0004962175],"genre_scores_gemma":[0.19427754,0.0003629491,0.79944974,0.00032334388,0.00018369507,0.0007764194,0.0016440499,0.00094069587,0.002041573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943305,0.002296849,0.00032968685,0.0013057413,0.0014518589,0.0002853346],"domain_scores_gemma":[0.9820067,0.010209042,0.00071186945,0.0034516084,0.003272773,0.0003480166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009140693,0.0018777935,0.0021417863,0.002694956,0.0010508442,0.0014655036,0.0046188254,0.00222693,0.0037407405],"category_scores_gemma":[0.041683502,0.00095446117,0.0014920564,0.0018365976,0.0013305645,0.0023660688,0.0028445714,0.0040763468,0.0024983718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003307654,0.00026357808,0.0029489533,0.00037410585,0.0003531543,0.00015649418,0.00014494894,0.4756036,0.006261585,0.024005461,0.0138374055,0.47571987],"study_design_scores_gemma":[0.00001671006,0.000036582038,0.0002889698,0.000015847978,0.00001530967,0.00003844639,0.000007476708,0.9852636,0.0018090807,0.011219455,0.0012765026,0.000012156838],"about_ca_topic_score_codex":0.004120259,"about_ca_topic_score_gemma":0.004452259,"teacher_disagreement_score":0.009140693,"about_ca_system_score_codex":0.0013409052,"about_ca_system_score_gemma":0.002547102,"threshold_uncertainty_score":0.048341155},"labels":[],"label_agreement":null},{"id":"W84174504","doi":"","title":"Use of off-line dynamic programming for efficient image interpretation","year":2003,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Operator (biology); Computer science; Interpretation (philosophy); Rank (graph theory); Sequence (biology); Dynamic programming; Artificial intelligence; Domain (mathematical analysis); Machine learning; Algorithm; Mathematics; Programming language","score_opus":0.025502404946396797,"score_gpt":0.28609138236719006,"score_spread":0.2605889774207933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W84174504","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021599589,0.00008094633,0.9753058,0.00014250474,0.000012015899,0.000058288388,0.0000190514,0.0011856288,0.0015961326],"genre_scores_gemma":[0.6602811,0.000075862896,0.3373932,0.00015987678,0.000021065202,0.00018323744,0.000081625854,0.00021820517,0.0015858097],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913114,0.0003884047,0.000035126483,0.00017403913,0.00017872911,0.00009249214],"domain_scores_gemma":[0.9976301,0.0016791417,0.00017109701,0.0002468842,0.00020105742,0.000071599046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016946137,0.0010334004,0.0011417631,0.0006390748,0.0005381328,0.0010974376,0.0015902309,0.0010081418,0.0022261094],"category_scores_gemma":[0.0053046066,0.00071519805,0.0006144655,0.00041701214,0.0013915157,0.0017840468,0.0012483941,0.0018563468,0.00036519868],"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":[0.00015823937,0.00012003116,0.00042168362,0.000042633194,0.000029085073,0.000080019316,0.00009551318,0.86662906,0.0037623716,0.00974089,0.00076372153,0.11815675],"study_design_scores_gemma":[0.000007391586,0.0000108786035,0.000029981467,0.0000019168247,0.0000019802474,0.000006042994,0.0000040479376,0.9944899,0.0006397614,0.004696517,0.00010907551,0.0000024735514],"about_ca_topic_score_codex":0.0043880157,"about_ca_topic_score_gemma":0.005514983,"teacher_disagreement_score":0.0043880157,"about_ca_system_score_codex":0.0014021845,"about_ca_system_score_gemma":0.001317513,"threshold_uncertainty_score":0.010173619},"labels":[],"label_agreement":null},{"id":"W870084106","doi":"10.48550/arxiv.1506.00511","title":"Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":136,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Samsung; Nvidia","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Embedding; Deep learning; Natural language processing; Space (punctuation); Zero (linguistics); Machine learning; Pattern recognition (psychology)","score_opus":0.1694283030760944,"score_gpt":0.21973510881613223,"score_spread":0.05030680574003782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W870084106","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78919154,0.0026689586,0.19601472,0.00085067505,0.00029998654,0.00013818192,0.0032603391,0.0039586104,0.0036169447],"genre_scores_gemma":[0.9692561,0.00028480898,0.022435369,0.00014255525,0.000055612036,0.000048324408,0.0047877566,0.000049111444,0.0029403944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957925,0.0000933536,0.000022349293,0.00016083974,0.00006581872,0.0000783723],"domain_scores_gemma":[0.9981805,0.0010049343,0.00017485945,0.0001588172,0.0003592779,0.00012156075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093088136,0.0013534783,0.00072118815,0.0014415083,0.00031139952,0.00078627747,0.0014137271,0.001398952,0.0010854164],"category_scores_gemma":[0.0040860996,0.0004088378,0.0006315253,0.00082256587,0.00056299346,0.002061257,0.00061277504,0.0012133747,0.0006133757],"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":[0.0007748621,0.00084706896,0.03089915,0.00039807498,0.00020014835,0.0004999404,0.00014865253,0.6255234,0.01037287,0.004334382,0.019795155,0.30620632],"study_design_scores_gemma":[0.000005850548,0.000024419958,0.0009066071,0.000009264256,0.0000071194404,0.000017118617,0.000012259656,0.9958752,0.0014369575,0.001542342,0.00015817984,0.00000466578],"about_ca_topic_score_codex":0.0133024845,"about_ca_topic_score_gemma":0.018473532,"teacher_disagreement_score":0.0133024845,"about_ca_system_score_codex":0.0015717976,"about_ca_system_score_gemma":0.0006152085,"threshold_uncertainty_score":0.026450098},"labels":[],"label_agreement":null}]}