Current progress on the LBA data-model intercomparison project (LBA-DMIP)
Bibliographic record
Abstract
The main goal of the LBA-DMIP is to understand how the different land-surface models (LSM) simulate the biogeochemical processes in the Amazon. Moreover, LBA-MIP is designed to research the land-surface processes over tropical and temperate regions of South America. Data analysis of carbon, energy and water fluxes measured by flux towers and resulting from models produce a consistent analysis of land surface budget. We integrate such information to evaluate a suite of land surface models over the Amazon and study the effects of land cover conversion from forest to savanna in that region. To accomplish these goals, LBA-MIP requires a data management system that will enable researches to access, understand, use and analyze large amount of diverse variables at multiple temporal and spatial scales. Based on the LBA-MIP goals, we have made an effort to make available drivers and ancillary information at the best quality possible for the modeling groups. Some of the issues with the data includes time-shifting, unrealistic precipitation and downward radiation among other problems that are now resolved. The final LBA-DMIP goal is to compare the ecosystem models that simulate energy, water and carbon fluxes over the LBA area and understand the land-atmosphere interactions from diurnal to interannual timescales. It also presents the opportunity to improve the representation of the Amazon region dynamics within the global and regional climatological frameworks. This work presents the current progress on the LBA-DMIP activities that includes observational data preparation and analysis of resulting simulations from more than 30 different land surface models across 8 LBA sites. Participating modeling groups are representing various parts of the globe including Brazil, USA, Canada and countries in Europe and Asia.
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 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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".