Warming‐enhanced preferential microbial mineralization of humified boreal forest soil organic matter: Interpretation of soil profiles along a climate transect using laboratory incubations
Bibliographic record
Abstract
Humified soil organic matter storage in boreal forests is large, and its responses to warming over relatively long timescales is critical for predicting soil feedbacks to climate change. To derive information relevant across decades to centuries from manipulative short‐term experiments, we conducted incubations of soils from two forested sites along the Newfoundland‐Labrador Boreal Ecosystem Latitude Transect in eastern Canada and assessed linkages between incubation data and these sites' profile characteristics. The sites differ in mean annual temperature by 3.4°C, but vegetation and soil types are similar. Organic soils (Oe + Oa) were incubated for 120 days at 15°C and 20°C, with and without a replaced Oi subhorizon possessing a distinct δ13C signature. Laboratory warming induced significantly greater mineralization and leaching of humified SOM relative to replaced Oi, congruent with greater warming‐induced increases in phenol oxidase activity relative to enzymes associated with labile C acquisition (percent increases of 101% versus 50%, respectively). These data suggest that warming can influence microbial communities and their enzymatic dynamics such that relative losses of humified SOM are disproportionately enhanced. This is consistent with stable isotopic, C:N, and radiocarbon profile differences between the two sites, which suggest a greater degree of microbial processing and greater relative losses of older SOC over the preceding decades at the warmer site, given our knowledge of organic inputs in these soils. This study is a first step toward linking the divergent timescales represented by soil profiles and laboratory manipulations, an important goal for biogeochemists assessing climate change impacts on SOM dynamics.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".