{"id":"W4414463030","doi":"10.1109/ieeedata.2025.3612373","title":"Collection: Datasets From Real-Time In-Situ Soil Monitoring for Agriculture 2025","year":2025,"lang":"en","type":"article","venue":"IEEE data descriptions.","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan; University of Manitoba; Environment and Climate Change Canada; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Loam; Soil water; Water content; Soil quality; Hydrology (agriculture); Agriculture; Precision agriculture; Environmental monitoring; Soil map","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":[0.001222561,0.002129813,0.001223443,0.002163376,0.0006469656,0.001270294,0.002780162,0.001781837,0.01360759],"category_scores_gemma":[0.003520441,0.0005075776,0.001193496,0.005314779,0.0003899624,0.001449594,0.001236865,0.001224081,0.02542411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001625005,"about_ca_system_score_gemma":0.00185061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06092741,"about_ca_topic_score_gemma":0.08964975,"domain_scores_codex":[0.9988101,0.000147383,0.0001837138,0.0002985663,0.0004137041,0.0001465147],"domain_scores_gemma":[0.997631,0.000267463,0.0002305779,0.0004348049,0.001258637,0.0001775629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009861759,0.00009909896,0.004893702,0.0005603186,0.00008779339,0.00006221619,0.00003562925,0.002947193,0.000635999,0.0004613145,0.983081,0.007037001],"study_design_scores_gemma":[0.0006300979,0.00008984328,0.0506721,0.0003736879,0.00008970955,0.0001245607,0.000364849,0.009754322,0.00246138,0.001756021,0.9335456,0.0001378844],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008925619,0.00005619957,0.000243482,0.0001079375,0.00004061356,0.00002848514,0.9975042,0.0005554702,0.0005710298],"genre_scores_gemma":[0.001324526,0.00003593513,0.0007714382,0.00004316914,0.000008162941,0.000097832,0.9974266,0.00004148382,0.0002508085],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06092741,"threshold_uncertainty_score":0.1211455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04507725774729743,"score_gpt":0.2686564452830543,"score_spread":0.2235791875357568,"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."}}