{"id":"W3025081465","doi":"10.1007/978-3-030-41384-2_5","title":"Physico-chemical Characterization and Development of Hemp Aggregates for Highly Insulating Construction Building Materials","year":2020,"lang":"en","type":"book-chapter","venue":"Sustainable agriculture reviews","topic":"Hygrothermal properties of building materials","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Materials science; Composite number; Thermal conductivity; Composite material; Porosity; Moisture; Microstructure; Green building; Characterization (materials science); Nanotechnology; Architectural engineering; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002435833,0.0004849431,0.001107245,0.00006337278,0.00009618413,0.0001024238,0.0001475325,0.0002985842,0.00003854423],"category_scores_gemma":[0.00007936054,0.0003662174,0.00009389559,0.00004711003,0.00004994615,0.0002209215,0.0001020533,0.0001314586,0.000009040233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001711602,"about_ca_system_score_gemma":0.00003423493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000213464,"about_ca_topic_score_gemma":1.35587e-7,"domain_scores_codex":[0.9982153,0.00002229729,0.0009892475,0.0003435187,0.0001385892,0.0002910898],"domain_scores_gemma":[0.9990323,0.00002590026,0.0005310987,0.0001517418,0.0001824253,0.00007658757],"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":[0.00001577591,0.000003617104,6.598491e-7,0.01215248,0.00008771168,0.000001911564,0.0002255883,0.00001806901,0.9561457,0.007028976,0.000201418,0.02411809],"study_design_scores_gemma":[0.0001567711,0.00001656812,0.00000425687,0.001459561,0.00007081535,0.000007294178,0.00003459434,0.00001007076,0.4842596,0.0002846025,0.5132999,0.0003958821],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6831177,0.1727668,0.05398676,0.0008972742,0.006005384,0.05024638,0.001532666,0.00409806,0.02734895],"genre_scores_gemma":[0.3796683,0.07981239,0.4350691,0.00058501,0.01558755,0.004742779,0.01175387,0.002664095,0.07011694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5130985,"threshold_uncertainty_score":0.999879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415714166564985,"score_gpt":0.2054245980666288,"score_spread":0.191267456400979,"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."}}