Surface Soil Organic Matter Qualities of Three Distinct Canadian Arctic Sites
Bibliographic record
Abstract
Cryosolic soils store large amounts of carbon (C) because soil organic matter (SOM) decomposition is slower than plant growth. The response of arctic SOM to climate change is likely to depend not only on temperature, but also upon complex interactions between soil properties and SOM chemistry. We hypothesized that organic surface soils (>17% carbon) have more labile SOM than mineral surface soils (<17% carbon). Furthermore, we hypothesized that high arctic soils have more labile SOM than soils from the Low Arctic and subarctic. This study was conducted in 3 arctic ecosystems: subarctic (Churchill, Manitoba; n = 138), Low Arctic (Daring Lake, Northwest Territories;n = 60), and High Arctic (Truelove Lowlands, Nunavut; n = 54). The 0–10 cm depth of several different Cryosolic soils was sampled. The results from density fractionation and solid-state 13C cross polarization and magic angle spinning (CPMAS) nuclear magnetic resonance (NMR) spectroscopy showed that organic surface soils contained relatively more labile C than mineral surface soils. Organic soils contained about 13% more O-Alkyl-C and 30% less Aromatic-C than mineral soils. Furthermore, for Churchill, Daring Lake, and Truelove organic soils, 53, 73, and 20% of the C was included in the light fraction of SOM [LF (LF < 1.55 g mL-1)], whereas 24, 19, and 14% of the C was included in the LF of mineral soils, respectively. Organic surface soils of subarctic and low arctic sites contained relatively more labile C than the high arctic site. Results showed that the subarctic and low arctic sites store about 15% more O-alkyl-C and 35% less Aromatic-C than high arctic organic soils (P < 0.001).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".