Bibliographic record
Abstract
LakeKeepers aims to address the gaps in lake water quality data that exist in many parts of Alberta. Current monitoring strategies restrict much of the Provincial data to lakes within easy driving distance from Edmonton or Calgary – however, many of Alberta’s invaluable water resources exist outside of this range and as a result have little water quality data. Current monitoring strategies for the ice-on period through the winter months are also limited. Citizen scientists collect information such as water clarity, chlorophyll-a, total Kjeldahl Nitrogen, total Phosphorus, microcystin concentrations, water temperature, dissolved oxygen, and some other water quality parameters. Information is also collected through the year, with monitoring occurring in both the summer and winter months. While the focus of the summer sampling is to gather baseline water quality information, the winter sampling is focused towards collecting parameters that best represent fish habitat quality, since ice anglers are the primary citizen scientists in the winter months.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.024 |
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".