Monitoring and modelling total phosphorus contributions to a freshwater lake with cage-aquaculture
Bibliographic record
Abstract
A mass-balance modelling approach combined with a sensitivity analysis was utilized to gain an improved understanding of the relative contributions of phosphorus (P) loading from various anthropogenic and non-anthropogenic sources into Lake Wolsey (Manitoulin Island, Ontario, Canada), a Type 2 freshwater lake with a cage-aquaculture facility. Total P loadings were estimated from eight sources (inlet exchange, non-point sources, cage-aquaculture facility, internal loading, groundwater seepage, atmospheric deposition, leaf litter and dwellings) and three sinks (outlet exchange, sedimentation and sportfishing). Results indicated that over the study period (May–November 2007) the non-point sources were the leading contributor of total P to Lake Wolsey (1120 kg P) followed by the cage-aquaculture facility loading (915 kg P), inlet exchange (539 kg P), groundwater inputs (305 kg P), dwellings (219 kg P), internal P recycling loads from the hypoxic hypolimnion (186 kg P), atmospheric deposition (79 kg P) and decomposing leaf litter (8.1 kg P). When comparing the loadings in this study, the sensitivity analysis showed that non-point sources were the only significant input parameter of total P loading to the in-lake concentrations of P in Lake Wolsey(P = 0.05). Information from this project will provide water quality managers with sound scientific information to make defencible decisions pertaining to policy and regulatory approaches for water quality risk assessment and management of cage-aquaculture in Type 2 sites.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".