Can recovery from disturbance explain observed declines in total phosphorus in Precambrian Shield catchments?
Bibliographic record
Abstract
The plausibility of land disturbance as a cause of declining phosphorus (P) concentrations in oligotrophic lakes within south-central Ontario, Canada, is evaluated using the process-based model INCA-P. The model was calibrated upon three catchments in the Muskoka–Haliburton region (MHR): Harp (HP), Dickie (DE), and Plastic (PC), which have varying degrees of declining P export and different forms of historic disturbances (timber harvesting, tree death, and soil acidification, respectively). Hindcasts (1978–2007) were run with and without simulated disturbances. Model performance of both DE and HP was greatly improved when effects of wetland tree deaths (DE) and harvesting (HP) were included. In PC, with no record of timber harvesting and relatively minor declines in P, initial hindcasts successfully accounted for the majority of interannual P fluxes, and performance was only marginally improved through the simulation of soil acidification. Vegetation decay, harvesting, and catchment acidification accounted for 63%, 24%, and 0.6%, respectively, of P export over the past 30 years. Of all disturbances, wetland vegetation death had the highest impact on areal P exports, indicating that riparian stability is particularly important.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".