The response of small boreal catchments to extreme weather event: Hurricane Larry
Bibliographic record
Abstract
The boreal environment is high in dissolved organic carbon (DOC) and iron concentrations. This DOC is enriched in functional groups allowing it to bind strongly with iron and act as a significant source of iron to the coastal and marine environment. As climate change intensifies more extreme weather events will affect the northern hemisphere and boreal environment. These weather events can lead to massive fluxes of material but the impact it will have is currently unknown. Hurricane Larry made landfall on Newfoundland (NL) in 2021 providing an opportunity to investigate how the boreal environment will react to extreme weather events. We sampled three rivers before and after the hurricane to see how DOC and iron concentrations, and colour (a350) were affected by the hurricane. Temperature and pH were found to decrease in a statistically significant manner following the storm with iron and a350 having a significant increase, DOC despite increasing following the storm failed to meet the statistical significance threshold. The rivers chosen had a high abundance of natural landcover which buffered the catchment's response from the hurricane. Pearson's correlations were calculated between the landcover and the ∆ of the variables to access the resilience of the catchments to extreme weather events. Due to the small sample size Pearson's corelations could not achieve statistical significance but the results still provide a novel look into the resilience of the northern boreal regions to extreme weather events. These preliminary results show that a high percentage of forest and peatlands buffered against increases in DOC and colour, with wetlands buffering an increase in iron concentrations. This study represents one of the first to observe boreal catchment responses to extreme weather events such as hurricanes and can serve as basis for more robust studies based in the boreal region.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".