Abundance and patterns of transparent exopolymer particles (TEP) in Arctic floodplain lakes of the Mackenzie River Delta
Bibliographic record
Abstract
The Mackenzie River Delta is a lake‐rich arctic floodplain that receives high inputs of dissolved organic matter (DOM) and suspended particulates from allochthonous and autochthonous sources, and may transfer carbon from dissolved to particulate phase via in situ formation of transparent exopolymer particles (TEP). TEP provides food for grazers, surfaces for bacteria, and increased potential for aggregation and sedimentation of organic matter. During open water 2006, we tracked TEP abundances in three Delta lakes representing gradients that include declining river‐to‐lake connection times, increasing levels of dissolved organic carbon (DOC), and declining chromophoric‐DOM (CDOM). Unexpectedly, TEP abundances were highest immediately after the flood, when autochthonous autotrophic production was at a seasonal low and CDOM a seasonal high. Moreover, the lake with the strongest riverine influence and lowest levels of autochthonous autotrophic production had the highest mean TEP‐carbon (TEP‐C) concentrations among the lakes. The mean proportion of particulate organic carbon (POC) represented by TEP‐C increased with increasing river connection time, and appears to represent a substantial proportion of POC in Mackenzie Delta Lakes. Unexpectedly, the TEP gradient was most strongly related to CDOM (river water source) rather than overall DOC. Variations in CDOM accounted for 53% of TEP‐C variation among the lakes, indicating allochthonous matter was the most important source of TEP. DOC release from in situ macrophytes during periods of high photosynthesis may contribute to TEP formation in the lake with lowest riverine influence, but pH levels >9.5 driven by the high photosynthetic rates complicate the interpretation of results from this lake.
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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 | 0.001 |
| 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".