Decomposition of periodical cicada carcasses influences nutrient uptake rates in experimental streams
Bibliographic record
Abstract
Abstract Every 17 years, Cicadidae Magicicada emerges in forests across the eastern USA, and their subsequent die-off provides a pulsed subsidy to streams, with unknown impacts on stream ecosystem function. To explore the impact of cicada subsidies on stream ecosystem function, and compare it with leaf litter inputs, we sequentially added dead cicadas and senescent leaf litter to replicated experimental streams. We added dead cicadas and leaves as high (47 gDM/m 2 ) and low (23 gDM/m 2 ) inputs to compare with a control stream. We monitored mass loss, water column nutrients, reach-scale metabolism, and periodically measured nutrient uptake rates. Cicada decomposition rates were similar to the labile leaf species (cicada k = 0.02 d −1 ; leaf k = 0.01 d −1 ). Water column nutrients changed after cicada addition; by Day three, background ammonium-N (NH 4 + -N) increased from below detection to 9 µgN/L, while soluble reactive phosphorus was only slightly elevated. Leaf litter addition had no effect on water column nutrients. On Day three, both levels of cicadas increased areal NH 4 + -N uptake rates, and the high treatment increased reach-scale heterotrophic respiration, whereas leaves had no effect. Temporal dynamics during the decomposition of cicadas and the comparison with leaf litter expands our understanding of how resource subsidies influence stream ecosystem function.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".