Artificial light as a disturbance to light‐naïve streams
Bibliographic record
Abstract
Summary Artificial light at night is prevalent in human‐dominated landscapes, and streams in these landscapes can be expected to be affected by artificial lights. We hypothesised that artificial light at night would reduce the activity of aquatic insects, resulting in reduced drift rates, lower fish growth rates and lower leaf litter decomposition rates. We tested these hypotheses by installing street lights to reaches in four forested, natural streams of coastal British Columbia each paired with a control reach. Cutthroat trout ( Oncorhynchus clarkii ) are the top predators in these streams and feed mostly on terrestrial and drifting aquatic invertebrates. We found that the night‐time drift of aquatic invertebrates in lit reaches was ˜50% of the drift in dark reaches. However, the density of emerging aquatic insects, the density of insects falling into reaches, leaf litter decomposition rate and the number and growth rate of trout caught were not significantly different between the dark and experimentally lit reaches. We conclude that, while short‐term exposure to artificial light during the summer changes invertebrate behaviour, it does not significantly alter other trophic levels in forested headwater streams. Our results suggest that low levels of artificial light do not strongly influence stream ecosystems, but future research should determine whether this is true for all seasons and longer‐term exposure to light.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".