Bibliographic record
Abstract
The presence of aluminium in natural waters is of major concern at present because of the potential threat for the health of a number of species, including humans. In natural water, aluminium exists in different forms depending on the concentrations of various other species, organic matter, the types of minerals, the pH, etc. The aluminium species in the natural water – gibbsite system is considered in this work. The main approaches for estimating of the individual concentrations of the aluminium species involve the use of reliable thermodynamic data, together with experimental measurements of free or total concentrations of major components. The new type of diagrams based on graphical and computerized methods, which quantitatively describe the distribution of soluble and insoluble, inorganic, and organic, and monomeric and polymeric aluminium species in heterogeneous aquatic systems is presented. This approach utilizes thermodynamic relationships coupled with original mass balance constraints, where the mineral phases are explicitly expressed. The factors influencing the distribution of soluble and insoluble aluminium species in aquatic systems were analyzed. The new type of developed diagrams may be used to interpret data obtained within the framework of water-quality monitoring programs.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".