Composition and Bioreactivity of Dissolved Organic Matter Leachates From End Members in a Mountain to Prairie Transitional River Valley
Bibliographic record
Abstract
Abstract River organic matter transformations impact the cycling of energy, carbon, and nutrients. The delivery of distinct dissolved organic matter (DOM) sources can alter aquatic DOM cycling and associated biogeochemical processes. Yet DOM source and reactivity are not well‐defined for many river systems, including in western Canada. Here, we explore DOM cycling in the mainstem of the Oldman River (stream order 6–7), a heavily regulated river network in southern Alberta (Canada). We compared seasonal river DOM content, composition, and bioavailability with nine endmember leachates from the river valley using optical properties and incubations to estimate biodegradable dissolved organic carbon (BDOC). River DOM composition was most similar to terrestrial soil leachates, followed by autochthonous DOM leachates. River DOM bioavailability was low (BDOC = 0%–16.6%, mean of 7.1%). Endmember leachate bioavailability increased from soils (BDOC = 23.9%–53.7%), to autochthonous sources (fish excretion, macrophytes, biofilm; BDOC = 49.9%–80.0%), to terrestrial vegetation (leaves, shrubs, grass; BDOC > 80%), scaling positively with protein‐like DOM content and amount of leachable dissolved organic carbon (DOC), and negatively with aromaticity. Seasonally, DOC concentrations changed little despite >15‐fold increases in discharge during spring. River DOM composition shifted modestly toward soil‐like endmembers in spring and more bioavailable autochthonous end members in autumn and winter. Low DOM bioavailability in the river mainstem and low DOC yields shown in previous work point to limited internal processing of DOM and limited bioavailable DOM delivery to downstream habitats, possibly due to upstream flow regulation. Our observations provide important insights into the functioning of western Canadian aquatic networks.
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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 | 0.001 | 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) | 0.000 | 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".