Drivers of the variability of soil phosphorus fractions in boreal forested watersheds
Bibliographic record
Abstract
In the boreal forest, phosphorus (P) is tightly cycled and can be heterogenous in its distribution across the landscape. Characterizing chemical, physical, and landscape-scale drivers of variability in both concentration and forms of soil P can help us understand how P dynamics will respond to environmental change; however, data on forms of soil P in boreal forests are sparse. The goal of this study was to assess the variability of soil P across a boreal forested watershed. Seventy-four surface soil samples from a boreal forested catchment were analyzed for a suite of chemical and physical characteristics, and were paired with geospatial data to develop predictive models of forms of soil P. Water-extractable P concentrations were low, while total P (65.7–2197 mg kg−1) and plant-available (Mehlich-3-extractable) P (0.63–193 mg kg−1) concentrations varied widely across the study area. Partial least squares regression results indicated that plant-available P was strongly related to soil Mn and Ca content, while total P was more strongly related to organic C and wetness index. These results suggest that soil P can vary widely, even in nutrient-poor boreal ecosystems, and site-specific characteristics may play an important role in predicting variability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".