Organic carbon concentration, absorbance and fluorescent characteristics, and isotopic composition of river water sourced from four glacially-fed rivers in the Canadian Rocky Mountains (2019-2021)
Bibliographic record
Abstract
Climate change is accelerating the warming of mountain glacial systems, leading to increased water fluxes and the enhanced export of glacially-derived sediment and organic matter (OM). Glacial OM represents an aged yet potentially bioavailable carbon pool that differs in composition from OM found in non-glacially sourced waters. Despite its significance, the composition of riverine OM from glacial headwaters to downstream reaches remains poorly understood in the Canadian Rockies. This dataset presents dissolved OM composition data derived from UV-vis spectroscopy including five calculated spectroscopic parameters (a254, S275_295, BIX, HIX, FI) and model outputs from parallel factor analysis of excitation-emission spectra (C1, C2, C3, C4). This data is supplemented with dissolved and particulate organic carbon concentrations and isotopic characteristics, and water isotope data. Data were collected over three summers (2019-2021) before, during, and after glacial ice melt along stream transects spanning 0-100 km downstream of glacial termini on the eastern slopes of the Canadian Rocky Mountains. Samples were obtained from the Bow River, North Saskatchewan River, and Sunwapta-Athabasca River. Paired microbial samples are archived at the NCBI database, under accession number PRJNA995204.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".