Long-Term Hydrological Reconstruction From a Beaver Meadow
Bibliographic record
Abstract
Beaver ponds contribute 0.8-1.0 million tons of atmospheric methane (CH4) emissions per year globally (Whitfield et al., 2014; Wik et al., 2016) and are found to be the largest CH4 emitters among all the wetland types in boreal environments (Roulet et al., 1992). However, the sources and underlying mechanisms of carbon emission in beaver ponds require further elucidation. I have reconstructed the historical development of a beaver meadow located in the Sibbald Research Wetland in the Rocky Mountains of Kananaskis Provincial (Ollennu) Park, Alberta, Canada. I used a combination of testate amoebae, plant macrofossils, and other geochemical proxies to provide high-resolution reconstructions along three peat cores extracted in hydrologically distinct portions of the meadow. To my knowledge, this is the first achievement at reconstructing long term hydrological conditions in these systems. In addition to providing a unique ecological reconstruction for this site, my project outlines any hydrological changes in the meadow as well as changes in nutrient input, which I tentatively used as proxies for past beaver activity.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".