Abundance of microbial genes associated with nitrogen cycling as indices of biogeochemical process rates across a vegetation gradient in Alaska
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nitrification and denitrification processes are crucial to plant nutrient availability, eutrophication and greenhouse gas production both locally and globally. Unravelling the major environmental predictors for nitrification and denitrification is thus pivotal in order to understand and model environmental nitrogen (N) cycling. Here, we sampled five plant community types characteristic of interior Alaska, including black spruce, bog birch, tussock grass and two fens. We assessed abundance of functional genes affiliated with nitrification (bacterial and archaeal amoA) and denitrification (nirK/S and nosZ) using qPCR, soil characteristics, potential nitrification and denitrification rates (PNR and PDR) and gross mineralization rates. The main chemical and biological predictors for PNR and PDR were assigned through path analysis. The potential N cycling rates varied dramatically between sites, from some of the highest (in fens) to some of the lowest (in black spruce) measured globally. Based on path analysis, functional gene abundances were the most important variables to predict potential rates. PNR was best explained by bacterial amoA gene abundance followed by ammonium content, whereas PDR was best explained directly by nosZ gene abundance and indirectly by nirK/S gene abundance and nitrate. Hence, functional gene abundance is a valuable index that integrates recent environmental history and recent process activity, and therefore is a good predictor of potential rates. The results of this study contribute to our understanding of the relative importance of different biological and chemical factors in driving the potential for nitrification and denitrification across terrestrial ecosystems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it