{"id":"W4365149257","doi":"10.1515/iupac.94.0213","title":"Henry’S Law Constants","year":2023,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Process Optimization and Integration","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Henry's law; Thermodynamics; Reciprocal; Dilution; Law; Constant (computer programming); Proportionality (law); Volatility (finance); Limit (mathematics); Statistical physics; Mathematical economics; Solubility; Mathematics; Chemistry; Physics; Philosophy; Mathematical analysis; Econometrics; Physical chemistry; Computer science; Political science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003139124,0.003011182,0.001952075,0.006895951,0.00143449,0.006363911,0.004636597,0.00257279,0.03959214],"category_scores_gemma":[0.02259865,0.0009664154,0.002380181,0.01141723,0.000830625,0.006540213,0.002379701,0.003988592,0.107467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002509927,"about_ca_system_score_gemma":0.003881271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01904833,"about_ca_topic_score_gemma":0.02015942,"domain_scores_codex":[0.9947431,0.0007222716,0.0007412248,0.001545299,0.001821309,0.0004266904],"domain_scores_gemma":[0.9931544,0.002133636,0.0005479159,0.001920204,0.002015256,0.0002285561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009221832,0.00005086091,0.002541518,0.001336429,0.00006899039,0.00003773741,0.00004805028,0.00213351,0.000272681,0.005208734,0.9583376,0.02987154],"study_design_scores_gemma":[0.00006321051,0.00002059603,0.002882651,0.0003869217,0.00003252191,0.0001068633,0.00006537307,0.002055467,0.0006375685,0.009308549,0.984377,0.00006329604],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009272224,0.002298781,0.00435215,0.0005458861,0.0003208909,0.0001391841,0.9766397,0.003139681,0.01163634],"genre_scores_gemma":[0.002981632,0.002009913,0.005370215,0.000317382,0.00007831962,0.0003853823,0.9849111,0.0006139257,0.003332122],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03959214,"threshold_uncertainty_score":0.1324489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01360158663558632,"score_gpt":0.3642728700354204,"score_spread":0.3506712833998341,"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."}}