Bibliographic record
Abstract
The Alberta Lake Management Society accepts requests from citizen scientists across Alberta to have their lake monitored as part of the LakeWatch program. Volunteers often contact the Alberta Lake Management Society due to concerns around eutrophication, harmful algal blooms, watershed developments, biodiversity monitoring, and for the early detection of aquatic invasive species. If accepted into the program, a lake will be monitored 4-5 times throughout the open water season: once in June, once in July, twice in August, and once in September. ALMS hires and trains field technicians in proper sampling techniques and it is these field technicians who arrange the sampling trips with the citizen scientists. At the lake, the citizen scientist’s role is to transport the technicians around the lake on a boat and to assist with sampling. The field technicians provide all necessary sampling equipment, support the volunteers in training, and oversee sample preservation, handling, and shipment. This program is free of charge for individuals hoping to collect water quality data from their lake. This program is made possible with the support of various funders, including the Government of Alberta, and would not be possible without hundreds of hours of volunteer time by lake stewards.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.030 |
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".