Interacting controls on productivity in a northern Great Plains grassland and implications for response to ENSO events
Bibliographic record
Abstract
In this study we investigated the causes of annual variability in peak aboveground biomass production, net ecosystem productivity (NEP) and gross ecosystem productivity (GEP) during an 8-year period (1999–2006) in a northern Great Plains grassland near Lethbridge, Alberta, Canada. In particular, we tested for a significant relationship between growing season precipitation and productivity and determined whether soil moisture carry-over from the previous fall–winter could alter this relationship. We also investigated the interaction between soil moisture availability and temperature in controlling grassland productivity. There was a very strong correlation between total precipitation input and average soil moisture content during the May–October growing season. However, the growing season average soil moisture contents in 2003 and 2006 were very similar to those recorded in 1999, despite lower than normal precipitation occurring in these 2 years. This resulted from a positive difference between precipitation and evapo-transpiration that allowed significant soil moisture to be carried-over from the previous fall–winter during both 2003 and 2006. Strong logistic relationships were observed between soil moisture and annual productivity based on data from all years except 2003 and 2006, years which had higher productivity than was predicted from the logistic regression. Interaction between temperature and soil moisture explained this difference. Productivity values in 2003 and 2006 were high compared with 1999, a year with approximately the same soil moisture content, and this resulted from the higher average growing season temperatures that were apparent in 2003 and 2006. Analysis of weather records indicated that precipitation in the month of June was significantly higher during El Niño years than during La Niña years in Lethbridge. During the study period, aboveground biomass, NEP and GEP were generally higher in El Niño years and lower in La Niña years because of associated variation in summer precipitation.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".