Rapid decomposition of maize detritus in agricultural headwater streams
Bibliographic record
Abstract
Headwater streams draining agricultural landscapes receive maize leaves (Zea mays L.) via wind and surface runoff, yet the contribution of maize detritus to organic-matter processing in agricultural streams is largely unknown. We quantified decomposition and microbial respiration rates on conventional (non-Bt) and genetically engineered (Bt) maize in three low-order agricultural streams in northwestern Indiana, USA. We also examined how substrate quality and in-stream nutrient concentrations influenced microbial respiration on maize by comparing respiration on maize and red maple leaves (Acer rubrum) in three nutrient-rich agricultural streams and three low-nutrient forested streams. We found significantly higher rates of microbial respiration on maize vs. red maple leaves and higher rates in agricultural vs. forested streams. Thus both the elevated nutrient status of agricultural streams and the lability of maize detritus (e.g., low carbon-to-nitrogen ratio and low lignin content) result in a rapid incorporation of maize leaves into the aquatic microbial food web. We found that Bt maize had a faster decomposition rate than non-Bt maize, while microbial respiration rates did not differ between Bt and non-Bt maize. Decomposition rates were not negatively affected by genetic engineering, perhaps because the Bt toxin does not adversely affect the aquatic microbial assemblage involved in maize decomposition. Additionally, shredding caddisflies, which are known to have suppressed growth rates when fed Bt maize, were depauperate in these agricultural streams, and likely did not play a major role in maize decomposition. Overall, the conversion of native vegetation to row-crop agriculture appears to have altered the quantity, quality, and predictability of allochthonous carbon inputs to headwater streams, with unexplored effects on stream ecosystem structure and function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".