Assessment Indexes and Recommended Maximum Permissive Concentrations of ToxicSubstances in Irrigation Water for Growing Vegetables in Greenhouse
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is unreasonable to assess the environmental quality of the greenhouse vegetable growing area based on the current standards. The key points of selecting assessment indexes and determining maximum permissive concentrations (MPC) of the toxic substances in irrigation water for greenhouse vegetable field were discussed in this paper. Considering the harm to vegetable growing, effects on food quality and safety, damage possibility on rural ecology and environment,several indicators including pH, COD, LAS, TDS, chloride, sulfide, mercury, cadmium, arsenic, chromium, lead, coliform, ascarid were selected as the basic assessment indexes; while copper, zinc, selenium, fluoride, cyanide, mineral oil, phenol, benzene, boron, aluminum, manganese, molybdenum were selected as theoptional evaluation indexes. Comparing with related criterions of FAO, America,Canada, Germany, Australian, Japan and other countries, taking account of the status quo in China, maximum permissive concentrations of each toxic substance were given out by the author as following: pH 6～8.5, COD: 40 or 150 mg·L-1; LAS: 5.0 mg·L-1; TDS: 1000 mg·L-1; chloride: 250 mg·L-1; sulfide: 1.0 mg·L-1; mercury: 0.001 mg·L-1; cadmium 0.01 mg·L-1; arsenic: 0.05 mg·L-1; chromium: 0.10 mg·L-1; lead: 0.10 mg·L-1; coliform: 4000MPN·100 mL-1; ascarid: 2 eggs·L-1; copper: 1.0 mg·L-1; zinc: 2.0 mg·L-1; selenium: 0.02 mg·L-1: fluoride: 2.0 mg·L-1; cyanide: 0.50 mg·L-1; mineral oil: 1.0 mg·L-1; phenol: 0.1 mg·L-1; benzene: 0.01mg·L-1; boron: 0.5mg·L-1; aluminum; 5.0 mg·L-1; Iron: 5.0 mg·L-1; manganese: 0.2 mg·L-1; molybdenum: 0.01 mg·L-1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it