Investigating seasonal hydrology and its relationship with microbiological indicators in the Apex River watershed (Iqaluit, Nunavut)
Bibliographic record
Abstract
Climate change in permafrost regions is projected to alter water resource distribution and water quality. The aim of this study was to characterize seasonal hydrology and dissolved organic matter (DOM) abundance and composition in the Apex River watershed in order to (1) identify water sources and pathways and (2) explore possible relationships between seasonal hydrology, DOM, and standard microbiological indicators (total coliforms (TC) and Escherichia coli). Discharge was measured at four sites in the Apex River (AR, CF, ET, and WT) from June 10th – August 28th, 2015. Water samples were collected three times weekly from June 8th - August 28th at the four sites and analyzed for DOC concentrations and DOM composition. Fluorescence spectroscopy and parallel factor (PARAFAC) analysis revealed the presence of five fluorescent components: three humic and two protein-like. DOM exports from the smaller east tributary (ET) exhibit predominantly protein-like (autochthonous) while DOM from the larger west tributary (WT) demonstrates humic-like (allochthonous) and protein-like (autochthonous) fluorescence. Autochthonous DOM is derived from microbial activity within water bodies while allochthonous DOM is derived from terrestrial sources. The rapid response of discharge to inputs indicates that snowmelt and precipitation runoff primarily follows overland pathways. Evidence of different timing of labile DOC availability between the Apex River outflow (AR), compared to ET, implies that controls on autochthonous DOM inputs differ between the two sites. TC densities show a correlation with protein-like fluorescence and biological freshness index (BIX). Results contribute to background knowledge which policy-makers can use to establish policies that ensure the sustainability of Iqaluit’s water resources.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".