Linear loss rates (% loss rates in parentheses) of DOM (<em>a</em><sub>320</sub> and four EEMs components) over the first four days of incubation as a function of time (bold means regression <em>p</em>-values
Bibliographic record
Abstract
Table 3. Linear loss rates (% loss rates in parentheses) of DOM (a320 and four EEMs components) over the first four days of incubation as a function of time (bold means regression p-values Abstract The abundant thaw lakes and ponds in the circumarctic receive a new pool of organic carbon as permafrost peat soils degrade, which can be exposed to significant irradiance that potentially increases as climate warms and ice cover shortens. Exposure to sunlight is known to accelerate the transformation of dissolved organic matter (DOM) into molecules that can be more readily used by microbes. We sampled the water from two common classes of ponds found in the ice-wedge system of continuous permafrost regions of Canada, polygonal and runnel ponds, and followed the transformation of DOM over 12 days by looking at dissolved organic carbon (DOC) concentration and DOM absorption and fluorescence properties. The results indicate a relatively fast decay of color (3.4 and 1.6% loss d−1 of absorption at 320 nm for the polygonal and runnel pond, respectively) and fluorescence (6.1 and 8.3% loss d−1 of total fluorescent components, respectively) at the pond surface, faster in the case of humic-like components, but insignificant losses of DOC over the observed period. This result indicates that direct DOM mineralization (photochemical production of CO2) is apparently minor in thaw ponds compared to the photochemical transformation of DOM into less chromophoric and likely more labile molecules with a greater potential for microbial mineralization. Therefore, DOM photolysis in arctic thaw ponds can be considered as a catalytic mechanism, accelerating the microbial turnover of mobilized organic matter from thawing permafrost and the production of greenhouse gases, especially in the most shallow ponds. Under a warming climate, this mechanism will intensify as summers lengthen.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".