Explicit Representation of Grazing Activity in a Diagnostic Terrestrial Model: A Data‐Process Combined Scheme
Bibliographic record
Abstract
Abstract Grazing activity is a fundamental behavior in pasture ecosystems and, globally, is a major disturbance that leads to destruction of terrestrial biomass. However, its impact on ecosystem C sequestration at large scales is not well understood due to its obvious anthropogenic property. In this study, we proposed a Data‐Process combined Grazing Scheme (DPGS) to quantify the regional grazing impact on ecosystem C sequestration in the typical pasture ecosystem, Temperate Eurasian Steppe. First, a pixel‐based livestock distribution map was generated based on fine‐scale (province/prefecture) inventory data using a resource‐oriented livestock distribution approach. Then the C consumption due to grazing ( C loss,graze ) was simulated by combining a late version of a remote‐sensing‐based terrestrial model, the Boreal Ecosystem Productivity Simulator and the Shiyomi grazing model. The modeled regional livestock density was evaluated against the Gridded Livestock of the World data set. The DPGS was able to reproduce the spatial distribution of livestock. Because extralarge herbivores (camel and horse) were involved in the calculation, the DPGS predicts higher livestock density than the Gridded Livestock of the World data set over 70% of the region. The modeled C loss,graze and its seasonal variability were validated against multiple site‐based data sets. The results showed good agreements with the field observations of C loss,graze . With further tests and data incorporations, this scheme has the potential to produce high‐resolution data sets of livestock distribution and C loss,graze and become a useful diagnostic instrument for model evaluation, parameterization, and intercomparison.
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 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 | 0.001 | 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 | 0.001 |
| 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".