Spatiotemporal patterns of primary productivity derived from remote sensing and flux measurements
Bibliographic record
Abstract
Forest ecosystem plays an important role in regulating the global climate by serving as the primary carbon pool for atmospheric carbon dioxide. Deforestation and forest degradation could pose a serious threat on the global emission of greenhouse gases, and this has been declared as the critical issue for United Nation's REDD programme. Under the threat of global warming and climate change, it is critical to quantify the mechanism of carbon flux in order to locate the so-called missing carbon sink and to identify potential strategies for mitigation. With complex ecosystem functions and uncertainty of climate change, both the micro-and macroscopic behavior of carbon flow and its influencing factors have to be systematically studied. There are consequently significant national and international efforts to develop a carbon monitoring system, such as the National Forest Carbon Monitoring, Accounting and Reporting System developed by Canada government, and National Carbon Accounting System by Australian government. These systems aims to tracking and forecasting land based emissions and removals of greenhouse gases from land-use changes, livestock and crop production, and disturbance events such as deforestation, afforestation, and natural disturbances. Based on synergic analysis of data from detailed forest inventory, remote sensing, and ecosystem modeling, the accounting results are used to monitor forest and carbon cycles, and are reported internationally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".