Recent Warming, Rather than Industrial Emissions of Bioavailable Nutrients, Is the Dominant Driver of Lake Primary Production Shifts across the Athabasca Oil Sands Region
Bibliographic record
Abstract
Freshwaters in the Athabasca Oil Sands Region (AOSR) are vulnerable to the atmospheric emissions and land disturbances caused by the local oil sands industry; however, they are also affected by climate change. Recent observations of increases in aquatic primary production near the main development area have prompted questions about the principal drivers of these limnological changes. Is the enhanced primary production due to deposition of nutrients (nitrogen and phosphorus) from local industry or from recent climatic changes? Here, we use downcore, spectrally-inferred chlorophyll-a (VRS-chla) profiles (including diagenetic products) from 23 limnologically-diverse lakes with undisturbed catchments to characterize the pattern of primary production increases in the AOSR. Our aim is to better understand the relative roles of the local oil sands industry versus climate change in driving aquatic primary production trends. Nutrient deposition maps, generated using geostatistical interpolations of spring-time snowpack measurements from a grid pattern across the AOSR, demonstrate patterns of elevated total phosphorus, total nitrogen, and bioavailable nitrogen deposition around the main area of industrial activity. However, this pattern is not observed for bioavailable phosphorus. Our paleolimnological findings demonstrate consistently greater VRS-chla concentrations compared to pre-oil sands development levels, regardless of morphological and limnological characteristics, landscape position, bioavailable nutrient deposition, and dibenzothiophene (DBT)-inferred industrial impacts. Furthermore, breakpoint analyses on VRS-chla concentrations across a gradient of DBT-inferred industrial impact show limited evidence of a contemporaneous change among lakes. Despite the contribution of bioavailable nitrogen to the landscape from industrial activities, we find no consistency in the spatial pattern and timing of VRS-chla shifts with an industrial fertilizing signal. Instead, significant positive correlations were observed between VRS-chla and annual and seasonal temperatures. Our findings suggest warmer air temperatures and likely decreased ice covers are important drivers of enhanced aquatic primary production across the AOSR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".