{"id":"W4402852106","doi":"10.1002/adfm.202411975","title":"Autonomous Sensing Architected Materials","year":2024,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department of Science and Technology, Ministry of Science and Technology, India; European Commission; University of Sydney; University of Guelph","keywords":"Materials science; Nanotechnology; Systems engineering; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001996576,0.0003608819,0.0003983418,0.0001879729,0.0001151757,0.0003086223,0.0001008809,0.0001228757,0.00151257],"category_scores_gemma":[0.00007019085,0.0003488431,0.00005652511,0.0002143389,0.00006046115,0.0004041138,0.0000421385,0.0001004531,0.0006390784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001205261,"about_ca_system_score_gemma":0.00002847543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001264804,"about_ca_topic_score_gemma":0.000002260098,"domain_scores_codex":[0.9982957,0.00006221246,0.00055174,0.0004070312,0.0002040205,0.0004793542],"domain_scores_gemma":[0.9993873,0.0001500746,0.00003961529,0.000268471,0.00005048281,0.0001040416],"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":[0.00004711396,0.000005129244,3.057055e-7,0.0002097488,0.00005715216,0.00005151566,0.00003135586,0.05626973,0.9356347,0.00286866,0.0003680417,0.004456534],"study_design_scores_gemma":[0.0002081817,0.00002946543,0.0002388672,0.0001890611,0.0000261586,0.0001684817,0.00001332181,0.000418907,0.9799204,0.005392818,0.01297222,0.0004220772],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612879,0.0003504492,0.01869671,0.00005448796,0.01314053,0.0001957662,0.0003026437,0.003821826,0.002149695],"genre_scores_gemma":[0.9896746,0.0000771907,0.007659124,0.0000695518,0.001155784,0.00003565511,0.0003216478,0.0001597725,0.0008466571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05585082,"threshold_uncertainty_score":0.9998963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01106205986219405,"score_gpt":0.2106672129601797,"score_spread":0.1996051530979857,"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."}}