{"id":"W4281751451","doi":"10.9734/ajeba/2022/v22i1730638","title":"Financial Distress and It’s Prediction: A Case Study of the Textile and Garment Industry","year":2022,"lang":"en","type":"article","venue":"Asian Journal of Economics Business and Accounting","topic":"Working Capital and Financial Performance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Financial distress; Quarter (Canadian coin); Market liquidity; Nonprobability sampling; Sample (material); Business; Financial ratio; Going concern; Accidental sampling; Actuarial science; Textile industry; Linear discriminant analysis; Debt; Accounting; Finance; Operations management; Audit; Engineering; Statistics; Auditor's report; Financial system; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003903746,0.0001163768,0.0002204105,0.0001149661,0.0005967486,0.0001987455,0.0001273455,0.00004175834,0.00002122217],"category_scores_gemma":[0.00003594859,0.000094034,0.00002949964,0.0002202107,0.00007105272,0.0007521478,0.0004196772,0.0003203657,1.405437e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001846425,"about_ca_system_score_gemma":0.00003349785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001639098,"about_ca_topic_score_gemma":0.0001167163,"domain_scores_codex":[0.9992235,0.000007428115,0.0004207879,0.0001389888,0.0000791909,0.0001300949],"domain_scores_gemma":[0.9991038,0.0000129559,0.0006826151,0.00008944036,0.0001007359,0.00001044583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001094685,0.0003348117,0.9395388,0.0002341228,0.00005092665,0.0002164461,0.001750282,0.0009915646,0.00001463367,0.001664702,0.0006789809,0.05441521],"study_design_scores_gemma":[0.002420815,0.0001195353,0.9401597,0.0001924978,0.0001757508,0.002585231,0.03463667,0.002652954,0.000002969905,0.001651893,0.01508657,0.0003154605],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997771,0.0001484109,0.000002867901,0.0009645821,0.0005954786,0.0001287725,0.000009042819,0.000003355805,0.0003764711],"genre_scores_gemma":[0.9989038,0.00002522303,0.000008727126,0.0002465922,0.0007795963,0.000005034919,8.628419e-7,0.0000104463,0.00001978152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05409975,"threshold_uncertainty_score":0.4589768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01123962423249794,"score_gpt":0.1816741109325138,"score_spread":0.1704344867000159,"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."}}