{"id":"W4415335453","doi":"10.1016/j.optlastec.2025.114137","title":"Unveiling the Real-Time plant growth dynamics using wearable fiber Bragg grating sensors with enhanced Resilience for Agricultural Intelligence","year":2025,"lang":"en","type":"article","venue":"Optics & Laser Technology","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China; Major Science and Technology Project of Hainan Province; Henan Agricultural University; McGill University","keywords":"Precision agriculture; Wearable computer; Fiber Bragg grating; Irrigation; Plant growth; Sensitivity (control systems); Resilience (materials science); Fiber optic sensor","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002172072,0.0002438158,0.0002963638,0.00005249194,0.0006435247,0.00005914636,0.0006308283,0.0003883099,0.00003626448],"category_scores_gemma":[0.000176202,0.00008737785,0.00007050826,0.001041354,0.0004019846,0.00009403881,0.0001525568,0.0003494736,0.0000219106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000707143,"about_ca_system_score_gemma":0.00002273423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008169409,"about_ca_topic_score_gemma":0.0007276184,"domain_scores_codex":[0.9985199,0.0000370606,0.0003105836,0.0004608172,0.0001173454,0.0005542942],"domain_scores_gemma":[0.9989879,0.0004368664,0.0001666468,0.0001718495,0.0002036442,0.00003305776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002600799,0.0001740167,0.002146255,0.00005508078,0.0001544749,0.00002088162,0.00008830983,0.003630545,0.8780032,0.08779139,0.0001344896,0.02754129],"study_design_scores_gemma":[0.0008223316,0.001461345,0.002947312,0.000707261,0.0003433841,0.0001442712,0.009494639,0.168485,0.7886084,0.0251134,0.000555779,0.00131684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929765,0.00006784675,0.0007647215,0.00384638,0.00005727574,0.0005439756,0.0000589989,0.0004598087,0.001224462],"genre_scores_gemma":[0.9953248,0.0001004821,0.003249689,0.0000829061,0.00002824515,0.00005916845,0.00002919182,0.000003044937,0.001122467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1648544,"threshold_uncertainty_score":0.4949537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007041592337102036,"score_gpt":0.213293849839038,"score_spread":0.2062522575019359,"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."}}