{"id":"W4407909757","doi":"10.1016/j.jssc.2025.125257","title":"Thermodynamic behaviour of the <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si103.svg\" display=\"inline\" id=\"d1e1706\"> <mml:msub> <mml:mrow> <mml:mi>τ</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>11</mml:mn> </mml:mrow> </mml:msub> </mml:math> -Al <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si104.svg\" display=\"inline\" id=\"d1e1716\"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:math> Fe1.7Si solid solutions from 0 K to 1270 K","year":2025,"lang":"lv","type":"article","venue":"Journal of Solid State Chemistry","topic":"Intermetallics and Advanced Alloy Properties","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Scalable Vector Graphics; Chemistry; Computer science; World Wide Web","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":[0.000250041,0.0002181091,0.0002801357,0.0006109169,0.000682764,0.0006199222,0.0004498372,0.0002915907,0.006219571],"category_scores_gemma":[0.001030774,0.0002424919,0.000242979,0.0004470886,0.0007449439,0.0006141359,0.0002143998,0.0006279821,0.0006323945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008549898,"about_ca_system_score_gemma":0.0005641881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00540057,"about_ca_topic_score_gemma":0.009502816,"domain_scores_codex":[0.9997861,0.00002642538,0.00001066519,0.00006462488,0.00007382188,0.0000385497],"domain_scores_gemma":[0.9997128,0.0001215114,0.00003619914,0.00001846552,0.00009188614,0.00001907237],"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":[0.00133882,0.0002478264,0.0181896,0.0007648704,0.000222105,0.0006780804,0.001213052,0.03934418,0.863526,0.03161567,0.00761109,0.03524871],"study_design_scores_gemma":[0.00004119726,0.0004972023,0.04319789,0.00005103402,0.0000498025,0.0003311884,0.0005899887,0.07705627,0.8592112,0.004723681,0.01410685,0.000143707],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798229,0.001234023,0.003136562,0.0003338547,0.00005802601,0.00002250991,0.002232107,0.0001482464,0.01301173],"genre_scores_gemma":[0.9942999,0.0004168808,0.00107394,0.00002599593,0.00001175266,0.00003051968,0.001246465,0.00006950008,0.002825028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006219571,"threshold_uncertainty_score":0.02080649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01476622136399281,"score_gpt":0.2427190662466757,"score_spread":0.2279528448826829,"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."}}