Estimating Ecosystem Responses to Changes in Mercury Loading: Lessons From the METAALICUS Project
Bibliographic record
Abstract
The Mercury Experiment to Assess Atmospheric Loadings in Canada and the US (METAALICUS) project is a whole-ecosystem, mercury (Hg) loading experiment specifically designed to examine the relation between atmospheric mercury deposition and fish Hg concentrations. This project was prompted by the observation that we lacked clear evidence whether a changes atmospheric Hg deposition might lead to a corresponding change in fish Hg, and at what time scales. To address this information need, a multi-national team of scientists was formed to devise a whole-ecosystem, Hgdosing study, whereby mercury would be deliberately added to an entire watershed. The study is being conducted at the Experimental Lakes Area (ELA), which is located in northwestern Ontario, Canada. Whole-ecosystem manipulation studies have a distinct advantage over small-scale (lab scale) studies, in that natural processes and complexities that are present in watersheds are accounted for in the scientific results. Starting in the spring of 2001, the METAALICUS team been dosing the Lake 658 watershed with about 20 ug/m2/y (about 3 to 4 times the current atmospheric load). The applied Hg is in the form of enriched stable isotopes, which can be analytically distinguished from previously existing ambient Hg, or currently depositing Hg. For Lake 658, about 70% of the Hg load is from runoff. Thus, in order to predict ecosystem-level responses to changes in atmospheric loading, it is critical to understand the linkages between deposition and Hg in runoff. The use of isotopically enriched Hg as a tracer has provided insights into watershed cycling of Hg that were previously unattainable. Results from this study have allowed for the construction of simple numerical models that provide estimates of the response times for Lake 658. In addition, if this model is generally applicable to other watersheds, insights into ranges in response times, and their controlling factors, can be ascertained and will be presented.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".