Variability of sedimentary phosphorus composition across Canadian lakes
Bibliographic record
Abstract
Phosphorus (P) in lake sediments is stored within diverse forms, often associated with metals, minerals, and organic matter. Sediment P can be remobilized to the water column, but the environmental conditions influencing the P retention-release balance depend upon the sediment chemistry and forms of P present. Sequential fractionation approaches can be used to help understand forms of P present in the sediments, and their vulnerability to release. We assessed P composition in surficial sediments (as an assemblage of six P-fractions) and its relationship with watershed, and lake-specific explanatory variables from 236 lakes across Canada. Sediment P composition varied widely across the 12 sampled Canadian ecozones. The dominant P-fractions were the residual-P and the labile organic P, while the loosely bound P corresponded to the smallest proportion of sediment TP. Notable contrasts in sediment P composition were apparent across select regions – with the most significant differences between sediment P in lakes from the mid-West Canada region (Prairies and Boreal Plains ecozones) and both Eastern coastal (Atlantic Maritime and Atlantic Highlands) and Western coastal (Pacific Maritime) ecozones. The ecozone attributes most critical to sediment P speciation across Canadian lakes were related to soil types in the watershed (e.g., podzols, chernozems, and Luvisols) and the chemical composition of lake water and sediments, such as dissolved Ca in lake water, bulk sedimentary Ca, Al, and Fe, dissolved SO4 in lake water, lake pH, and salinity. Understanding predictors of the forms of P stored in surficial sediments helps advance our knowledge of in-lake P retention and remobilization processes across the millions of unstudied lakes and can help our understanding of controls on internal P loading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".