El Grado en Informática y Servicios. Una respuesta a la nueva demanda del contexto social
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
Biological ion exchange (BIEX) has proved to remove natural organic matter (NOM) better than biological activated carbon (BAC). This raises the question if BIEX can be integrated into a full-scale drinking water treatment plant to remove NOM and ammonia. In this study, a pilot plant consisting of one BIEX filter, three GAC filters and one BAC filter was set up as second-stage filtration at the Sainte-Rose drinking water treatment plant (Laval, Canada). The pilot plant was operated for a period of nine months without regeneration of the ion exchange resins. The influent water showed low DOC (2.5 mg/L) and high sulfate concentrations (28.2 mg/L). Except of a short peak of DOC released at about 1 000 BV, the BIEX filter achieved a nearly constant removal of 29-36% over the whole study period. The DOC removals of GAC were similar to BIEX at < 8000 BV but then stabilized at 13-24% after 8 000 BV. Most DOC removal in the BIEX filter was achieved at the top 30 cm layer (81%) compared to 62-66% removal in the GAC/BAC filters in the same layer. After the rapid exhaustion of the primary ion exchange capacity (<1 000 BV), sulfate displaced the fraction of NOM with lower affinity than sulfate, corresponding to the initial DOC release in the BIEX filter. The fraction of NOM with higher affinity than sulfate can still replace sulfate, which explains the good long-term performance of the BIEX filter. BIEX released ammonia with an average of 15% in warm water condition, probably related to the small diameter of the column which limited backwash effectiveness.
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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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