{"id":"W4401687400","doi":"10.1109/tgrs.2024.3446042","title":"SSL-SoilNet: A Hybrid Transformer-Based Framework With Self-Supervised Learning for Large-Scale Soil Organic Carbon Prediction","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Deutsche Forschungsgemeinschaft","keywords":"Computer science; Soil carbon; Scale (ratio); Total organic carbon; Transformer; Remote sensing; Environmental science; Artificial intelligence; Machine learning; Soil science; Soil water; Geology; Environmental chemistry; Engineering; Electrical engineering; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0008912974,0.001266567,0.001081701,0.0009347229,0.0003207212,0.0007871088,0.002443839,0.001216106,0.002065571],"category_scores_gemma":[0.001562775,0.0005337297,0.00104582,0.0007756704,0.0004422943,0.001352123,0.001024972,0.001274765,0.0009345035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008395743,"about_ca_system_score_gemma":0.001319929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01060999,"about_ca_topic_score_gemma":0.01579255,"domain_scores_codex":[0.9996772,0.00007510492,0.00001950135,0.0001071797,0.00007587791,0.00004509087],"domain_scores_gemma":[0.999587,0.0001694037,0.00004427753,0.00004324615,0.0001224799,0.00003356057],"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":[0.0002215831,0.0003214718,0.002774319,0.0001368687,0.0001692323,0.0001380317,0.00005428522,0.7271028,0.003260005,0.003919458,0.006227518,0.2556745],"study_design_scores_gemma":[0.000004090322,0.000009875926,0.00005831892,0.00000216826,0.000003374511,0.000004247612,0.000001978136,0.9986776,0.0002568412,0.0007871449,0.0001924344,0.000002042981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0312608,0.0008777011,0.9563611,0.0003027841,0.0001151143,0.000110686,0.0006709964,0.008448452,0.001852412],"genre_scores_gemma":[0.665724,0.0006820211,0.3229299,0.000578754,0.0001893487,0.0004531647,0.003781802,0.0004999943,0.005161109],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01060999,"threshold_uncertainty_score":0.02109647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006663452912177755,"score_gpt":0.2103322946904334,"score_spread":0.2036688417782556,"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."}}