Abiotic controls on nitrogen fixation and respiration in selected woody debris from the Pacific Northwest, U.S.A.
Bibliographic record
Abstract
We estimated the effects of temperature, moisture, and oxygen concentration on nitrogen fixation and respiration in woody debris and used this information to model seasonal variation in these processes. We measured acetylene reduction and CO2 evolution of wood samples to determine the relative effect of these abiotic factors on nitrogen fixation and respiration. The interactions of these abiotic factors were examined in a model to test whether temperature alone can be used as a predictor of seasonal changes in nitrogen fixation and respiration rates in woody debris. Nitrogen fixation rates were optimum near 30ºC, whereas respiration rates were optimum over a broader range, from 30°C to 50°C. Nitrogen fixation and respiration rates were greatest above 175% and 100% wood moisture content, respectively, with little activity below 50%. Nitrogen fixation was optimum at 2% O2, with activity much reduced above and below this concentration. Respiration was optimal when O2 exceeded 1%. In our simulations, annual nitrogen fixation and respiration rates were 7.8 and 1.7 times greater, respectively, when only temperature limitation was included than when moisture and oxygen limitations were also included. Therefore, seasonal interactions of abiotic factors need to be considered when estimating annual nitrogen fixation and respiration rates.
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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 | 0.000 | 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".