Mexican Forest Inventory Expands Continental Carbon Monitoring
Bibliographic record
Abstract
The terrestrial ecosystems of the North American continent represent a large reservoir of carbon and a potential sink within the global carbon cycle. The recent State of the Carbon Cycle Report [U.S. Climate Change Science Program (CCSP), 2007] identified the critical role these systems may play in mitigating effects of greenhouse gases emitted from fossil fuel combustion. However, there are currently large uncertainties in continental carbon models, and the scientific community's understanding of relevant carbon sources and sinks has been much less complete in Mexico than in Canada and the United States [Birdsey et al., 2007]. One reason for this disparity has been a lack of systematic field data from throughout Mexico. Recently, though, new field and satellite inventory information has become available through two Mexican resource agencies, the Comisión Nacional Forestal (CONAFOR) and the Instituto Nacional de Estadstica y Geografa (INEGI). This information has the potential to support new types of analysis within Mexico and to significantly augment efforts to characterize carbon dynamics at the continental scale. Moreover, because many of the forest types and land use issues present in Mexico can be found throughout Latin America, Mexico's support of carbon monitoring may provide a relevant example as other counties within the region develop their own inventories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".