Microbiological and molecular evaluation of an alluvial water well field and fouling-related phenomena
Bibliographic record
Abstract
An important source of water for the city of North Battleford, Saskatchewan is groundwater extracted from wells installed adjacent to the North Saskatchewan River. Unfortunately, these wells undergo fairly rapid deterioration (3-4 years) leading to reduced well capacity and water quality. The reasons for this deterioration are poorly understood. The studies in this thesis have tried to quantify the prevalence, activity and diversity of microbial populations in the aquifer and to explain the possible outcomes of microbial interaction with the environment which might lead to biofouling of the wells. A panel of conventional cultural, microscopic, metabolic and molecular techniques were utilized to analyze water, sediment and biofilm samples collected from various locations in the aquifer. The studies indicated that the aquifer was anoxic and harboured abundant concentrations of iron and manganese very close to the well and also presence of diverse groups of organisms including Fe-, Mn-, S-oxidizing bacteria as well as Fe-, Mn-, nitrite- and sulphate-reducing bacteria. A two year spatio-temporal study indicated that the biofilm growth significantly increased within the 1-2 m zone from the well and were also associated with a rapid reduction in specific capacity of the well. PCR, qPCR, and DGGE analyses indicated that the microbial community composition and diversity varied with space and time with greatest changes detected within the zone proximal to the well. Sequence data indicated that the major bacterial species prevalent in the aquifer belonged to Sulfuricurvum spp., Rhodobacter spp., Methylobacter spp., Acidovorax spp., and Geobacter spp. The studies demonstrated that water extraction influenced microbial community diversity, activity and composition, the effect of which did not extend beyond 1-2 m well radius. The application of impressed current did not demonstrate any anti-fouling effect, but rather favoured the growth of biofilm around the well and the accumulation of insoluble precipitates leading to accelerated deterioration of the well. Overall, the microbial community diversity, activity and composition in the study aquifer changed with respect to time and space, and water extraction. These changes altered the biogeochemical processes in the aquifer, especially within the zone closest to the wells leading to clogging and well deterioration.
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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 | 0.001 |
| 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".