Limnological data from 49 lakes in Tursujuq National Park, Northern Quebec (Nunavik)
Bibliographic record
Abstract
The chemical characteristics of lakes change according to local conditions in their watershed: soil type, vegetation type, proximity to the coast, etc. We studied 49 lakes distributed along a 300 km longitudinal transect through Tursujuq National Park, Nunavik, in August 2015 and 2016. We present the surface limnological data on pH, alkalinity, nutrients (nitrogen and phosphorus), carbon, chlorophyll a, ions and metals (Calcium, Magnesium, Sodium, Potassium, Silica, Silver, Aluminum, Antimony, Arsenic, Barium, Beryllium, Bismuth, Boron, Cadmium, Calcium, Cerium, Cesium, Chromium, Cobalt, Copper, Gallium, Germanium, Indium, Iron, Lanthanum, Lead, Lithium, Magnesium, Manganese, Molybdenum, Nickel, Niobium, Palladium, Platinum, Potassium, Rubidium, Scandium, Selenium, Sodium, Strontium, Tellurium, Thallium, Tin, Titanium, Tungsten, Uranium, Vanadium, Yttrium, Zinc, Zirconium). These data, sampled several metres from the shoreline, help us understand the current state of the lakes and their water chemistry in this protected study area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.202 |
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".