Analysis of spatio-temporal features of a carbon source/sink and its relationship to climatic factors in the Inner Mongolia grassland ecosystem
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
Global climate change has become a major concern worldwide. The spatio-temporal characteristics of net ecosystem productivity (NEP), which represents carbon sequestration capacity and directly describes the qualitative and quantitative characteristics of carbon sources/sinks (C sources/sinks), are crucial for increasing C sinks and reducing C sources. In this study, field sampling data, remote sensing data, and ground meteorological observation data were used to estimate the net primary productivity (NPP) in the Inner Mongolia grassland ecosystem (IMGE) from 2001 to 2012 using a light use efficiency model. The spatio-temporal distribution of the NEP in the IMGE was then determined by estimating the NPP and soil respiration from 2001 to 2012. This research also investigated the response of the NPP and NEP to the main climatic variables at the spatial and temporal scales from 2001 to 2012. The results showed that most of the grassland area in Inner Mongolia has functioned as a C sink since 2001 and that the annual carbon sequestration rate amounts to 0.046 Pg C/a. The total net C sink of the IMGE over the 12-year research period reached 0.557 Pg C. The carbon sink area accounted for 60.28% of the total grassland area and the sequestered 0.692 Pg C, whereas the C source area accounted for 39.72% of the total grassland area and released 0.135 Pg C. The NPP and NEP of the IMGE were more significantly correlated with precipitation than with temperature, showing great potential for C sequestration.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it