Establishing baseline limnological conditions in Baker Lake, Nunavut
Bibliographic record
Abstract
The Baker Lake Cumulative Effects Monitoring Program — also known as “Inuu’tuti” — uses both western science and Inuit Qaujimajatuqangit. The program measures any changes in Baker Lake and the waters flowing into it. These changes can result from mining activities, the way the land is used, or the warming climate. Baker Lake is a typical large Arctic lake. It is cold, low in nutrients, has plenty of oxygen for fish, and metals are very low. This project measured baseline water quality in the lake during two open water surveys in August 2015 and 2017 and one under-ice survey in May 2016. Knowing the current conditions will help to understand changes in the future. Two items of concern for residents were measured: a “fishy” taste in the water; and a salty taste in the water. The fishy taste is likely caused by a type of golden algae. These microscopic plants release substances that create a “fishy” taste and odour in the water at certain times of the year. The salty taste is noticed when low lake levels and high tides or winds at Chesterfield Inlet allow ocean water to spill into Baker Lake. The ocean water mixes with surface waters, leading to a salty taste. This was also documented in a scientific study in 1965. The results of this project showed that there was always some ocean water at depth in the lake. However, the amount of ocean water and the depth it occurred at changed over the seasons and between years.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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; both teacher heads agree on what is shown here.
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".