Assessment of Stream Metabolism and Associated Environmental Drivers in the Greiner Lake Watershed, Nunavut, Canada
Bibliographic record
Abstract
Stream metabolism is an ecological process that can be monitored to assess carbon cycling and productivity within a stream ecosystem. GPP (gross primary productivity) is measured as oxygen produced by autotrophs and ER (ecosystem respiration), which is measured by oxygen depleted by all living organisms. Complications arise when estimating GPP and ER in the Arctic because most methods require a period of darkness when GPP ceases, however, summer regimes of photosynthetically active radiation (PAR) do not reach zero. Furthermore, natural diffusion of oxygen from the atmosphere (k) must be accounted for but this requires extensive field work, thus posing problems for remote locations. Few studies have assessed how stream metabolism is influenced by the surrounding environment, even though it is well established that stream metabolism in other biomes is affected by key environmental variables.\nThe thesis assesses methods that are appropriate for estimating stream metabolism in the Arctic and determines stream metabolism and associated environmental variables in the Greiner Lake Watershed, Nunavut. Stream metabolism was estimated using streamMetabolizer and empirical methods. These methods were compared based on values expected for low productivity streams, and model diagnostics (process and observation error) for Bayesian statistics. StreamMetabolizer produced biologically possible days with realistic average values and ranges of GPP and ER.\nEstimates of GPP and ER from streamMetabolizer were used in a partial least square regression analysis (PLSR) with environmental variables measured at each site (water chemistry, channel form, land cover type and surrounding waterbodies). I discovered that GPP was positively related to median substrate particle size (D50), and ER was positively related to the area of upstream lakes and stream width. D50 may have been providing ideal habitats for primary producers, and lakes may have been impacting downstream controls of ER. Overall, streamMetabolizer is a useful method for determining stream metabolism in Arctic environments that are remote and have limited periods of darkness in the summer. Moreover, this research contributes to a growing database of stream metabolism in the Arctic and indicates key environmental variables influencing stream metabolism in the Arctic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".