Community Monitoring of Environmental Change: College-Based Limnological Studies at Crazy Lake (Tasirluk), Nunavut
Bibliographic record
Abstract
In light of the difficult logistics and high cost of polar research into climate change, involvement of local people can contribute immensely to important data collection. One can use the knowledge and skills of human resources that are already present—teachers, students, and community members. An example is the long-term Arctic monitoring program established at Crazy Lake (63°51' N, 68°28' W) near Iqaluit, Nunavut, to monitor snow and ice thickness, biological components, and water chemistry. Nunavut Arctic College students collected basic limnological data at Crazy Lake during spring field camps held between 10 and 16 April in 2005 and 2006. Mean snow depth ± SD for Crazy Lake was 0.46 ± 0.13 m (n = 24). White ice averaged 0.13 ± 0.12 m and black ice 1.38 ± 0.28 m. Total ice thickness (white ice + black ice) ranged between 0.91 and 1.91 m (mean = 1.51 ± 0.22 m). The total lake cover (snow + ice) averaged 1.97 ± 0.20 m. Water depth ranged from 1.48 to 18.58 m (mean = 10.10 ± 4.99 m).
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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 | 0.000 | 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) | 0.002 | 0.000 |
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 teacher head, 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".