{"id":"W3201468330","doi":"10.1016/j.dib.2021.107410","title":"Dataset on the small- and large deformation mechanical properties of emulsion-filled gelatin hydrogels as a model particle-filled composite food gel","year":2021,"lang":"en","type":"article","venue":"Data in Brief","topic":"Food composition and properties","field":"Nursing","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Gelatin; Self-healing hydrogels; Composite number; Emulsion; Composite material; Materials science; Particle (ecology); Deformation (meteorology); Photographic emulsion; Particle size; Chemical engineering; Polymer chemistry; Chemistry; Engineering; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005656963,0.0005309886,0.0004275785,0.002004419,0.0002900228,0.0003851709,0.0004885608,0.0007120465,0.006016043],"category_scores_gemma":[0.001089666,0.0001167803,0.0004925322,0.002509915,0.0001884636,0.0004523909,0.0003471761,0.0003924172,0.002381569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003202675,"about_ca_system_score_gemma":0.0002731018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001992303,"about_ca_topic_score_gemma":0.002865465,"domain_scores_codex":[0.9996622,0.00002243441,0.00003326017,0.00007994085,0.0001731523,0.00002904442],"domain_scores_gemma":[0.9986514,0.0005004834,0.0002364183,0.0002093268,0.0003339321,0.00006843224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001638859,0.0009601574,0.04035298,0.002789552,0.0002574817,0.000780983,0.0002159717,0.01188719,0.6355476,0.001867496,0.02822079,0.275481],"study_design_scores_gemma":[0.00004541794,0.001137446,0.3288458,0.0002295519,0.0002233629,0.001659119,0.0002134599,0.01713201,0.527067,0.001716701,0.121565,0.0001651133],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.7878442,0.01119628,0.02308184,0.0007466912,0.0001649211,0.0001540207,0.1369525,0.001460847,0.03839869],"genre_scores_gemma":[0.7405536,0.009138965,0.02602706,0.0004288269,0.0001611506,0.0002705904,0.2114429,0.0002762301,0.0117007],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.006016043,"threshold_uncertainty_score":0.02012569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07332179921686797,"score_gpt":0.2710117717237012,"score_spread":0.1976899725068332,"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."}}