@ 1981, by the American Society of Limnology and Oceanography, Inc. Chemistry of interstitial water and bottom sediments as indicators of seepage patterns in Perch Lake, Chalk River, Ontario’
Bibliographic record
Abstract
A coring and squeezing technique was used to study seepage processes in Perch Lake. This lake was selected because a portion of the shallow aquifc r in its basin contains tritium, which is an excellent tracer. Tritium analyses of interstitial water samples and well waters from piezometers adjacent to the lake indicate a pattern of grouildwater discharge, or seepage, consistent with the predictions of mathematical simulations. Shallow groundwaters in the aquifer discharge almost vertically into the lake near the shor:; water deeper in the aquifer enters the lake farther from shore. As a result of this groundwater discharge, metals such as iron and manganese arc being deposited in the sediments. Interstitial waters in Perch Lake, especially nearshore, are:goundwaters or groundwaters mixed with lake waters. The chemistry of interstitial waters in lake sediments may thus be determined by the quality of local groundwaters as well as by sediment-water reactions and vertical diffusional mixing. Data from nests of piezorneters nl:ar the lake and from cores or multilevel piczometers in the lake bottom, information abou: bottom sediments, and mea-surements of seepage flux will be needed to estimate the coni ributions to lakes of dissolved components from groundwater discharge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.226 | 0.177 |
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".