Aplicabilidad del modelo de contabilidad de carbono CBM-CFS3 en bosques templados de los ejidos "La Mojonera" y "Atopixco", Zacualtipán de Angeles, Hidalgo, México.
Bibliographic record
Abstract
La ejecución de cualquier actividad mitigadora de cambio climático requiere de la implementación de un mecanismo de evaluación y monitoreo que permita conocer su eficacia en condiciones específicas. Por ello, actualmente se desarrollan en México y en el mundo diversos estudios enfocados a contabilizar la captura del CO2 en bosques y selvas. En esta investigación se evaluó la aplicabilidad del modelo de contabilidad de carbono CBM-CFS3 en bosques templados sujetos a un régimen de aprovechamiento; como primer parte de la investigación fue necesario identificar la disponibilidad de datos, parámetros e información para alimentarlo, posteriormente se identificaron los mecanismos adecuados para ingresar la información al modelo CBM-CFS3, finalmente se realizó un análisis exploratorio de la aplicación del CBM-CFS3 para estimar existencias y flujos de carbono. Se hizo una comparación de la información estimada de inventarios tradicionales comparada con la información estimada por el modelo con la finalidad de calibración del mismo, de igual forma se generaron escenarios con aclareos, sin aclareos y bosque sin cosecha, para conocer cuál de estos maximizaría la captura de carbono y tomar decisiones mejor informadas. _______________ APPLICABILITY OF THE CARBON BUDGET MODEL OF THE CANADIAN FOREST SECTOR CBM-CFS3 IN TEMPERATE FOREST OF THE EJIDOS THE MOJONERA AND ATOPIXCO ZACUALTIPAN DE ANGELES, HIDALGO, MEXICO. ABSTRACT: The performance of any mitigating activity for climate change requires the implementation of a monitoring and evaluation mechanism that reveals its effectiveness at specific conditions. Therefore, currently being developed in Mexico and the world several studies has been focused on capturing CO2 accounting in forests and jungles. In this study we evaluated the applicability of the carbon accounting model CBM-CFS3 in temperate forests under harvesting treatments. The methodology used is as follows: 1) identifying the availability of data and information to feed parameters, subsequently 2) selecting the mechanisms for entering information to the CBM-CFS3 model and 3) conducting an exploratory analysis of the implementation of the CBM-CFS3 to estimate stock and carbon fluxes. Further, 4) a comparison was done using the results obtained by the CBM-CFS3 model versus traditional forest inventory data for calibration purposes. In this part, modeling was done for scenarios: without/with thinning and with no forest harvesting to know which of these options would maximize carbon capture and make more informed decisions.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".