{"id":"W4309310085","doi":"10.3390/rs14225774","title":"Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mitacs; Sapienza Università di Roma; Innovation Saskatchewan","keywords":"Support vector machine; Hyperspectral imaging; Quantum machine learning; Computer science; Decision tree; Artificial intelligence; Random forest; Machine learning; Quantum; Boosting (machine learning); Classifier (UML); Quantum computer; Pattern recognition (psychology); Algorithm; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001027246,0.0005479909,0.0008218372,0.0009565436,0.0007888719,0.0009500545,0.001485041,0.001034027,0.003511447],"category_scores_gemma":[0.002909148,0.0003550539,0.000887895,0.001032672,0.0006974193,0.001278001,0.0009886849,0.001330947,0.001901659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008210048,"about_ca_system_score_gemma":0.001255339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002419385,"about_ca_topic_score_gemma":0.003840965,"domain_scores_codex":[0.9991239,0.0002600814,0.00004681191,0.0001903268,0.0003068058,0.00007214462],"domain_scores_gemma":[0.9989292,0.0003325682,0.0001226458,0.0002774273,0.0003019746,0.0000360898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003731786,0.000309411,0.002343311,0.0004466606,0.0001002141,0.0001160681,0.0002242541,0.1385036,0.03695656,0.03415132,0.01613903,0.7703364],"study_design_scores_gemma":[0.0000176223,0.00005482684,0.0006955765,0.00001626303,0.000009705035,0.00005315459,0.00002263912,0.9716489,0.009055034,0.01340513,0.005003626,0.0000175023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01166046,0.0001509829,0.983656,0.0001117022,0.00004935587,0.0001221748,0.0004369023,0.001940274,0.001872028],"genre_scores_gemma":[0.180274,0.0001650561,0.8116724,0.0001701186,0.00006132763,0.0005285757,0.002294987,0.0003466674,0.004486757],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003511447,"threshold_uncertainty_score":0.011747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01717182392277426,"score_gpt":0.2635989655010669,"score_spread":0.2464271415782927,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}