{"id":"W4399293775","doi":"10.3390/rs16112007","title":"Evaluation of Data Sufficiency for Interannual Knowledge Transfer of Crop Type Classification Models","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Adaptability; Relevance (law); Transferability; Transfer of learning; Machine learning; Agriculture; Data-driven; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01102893,0.001281971,0.0008398604,0.001218889,0.0007375442,0.001343207,0.001614742,0.00207348,0.001132309],"category_scores_gemma":[0.05932702,0.0004768183,0.000771884,0.001173502,0.001059372,0.004081724,0.001812776,0.001657308,0.0003263433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001471202,"about_ca_system_score_gemma":0.001382491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009541573,"about_ca_topic_score_gemma":0.006472989,"domain_scores_codex":[0.9966853,0.001472909,0.0004917028,0.0007117362,0.0004388684,0.0001995315],"domain_scores_gemma":[0.9499744,0.03848761,0.001600922,0.005594328,0.003787164,0.0005555652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003004057,0.001834458,0.0502774,0.0005050208,0.0005899541,0.0003565001,0.0005471412,0.6051134,0.01324803,0.001302371,0.00230609,0.3209155],"study_design_scores_gemma":[0.000143542,0.0008887834,0.01353864,0.00007040615,0.00014654,0.0001253758,0.0003495163,0.9671648,0.01458066,0.001791089,0.00116147,0.00003927747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9507552,0.001104951,0.04195483,0.0007563821,0.000137274,0.0002844888,0.0007860586,0.001259573,0.002961284],"genre_scores_gemma":[0.9764706,0.0001539873,0.02158942,0.0001129816,0.00002413053,0.0001367672,0.001045276,0.00006678206,0.0004000736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01102893,"threshold_uncertainty_score":0.05832726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.242550183174677,"score_gpt":0.3552233231498637,"score_spread":0.1126731399751867,"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."}}