The "Coffee-flux collaborative observatory": measuring and modeling carbon, nutrients, water and sediment Ecosystem services in a coffee agroforestry watershed (Costa Rica)
Bibliographic record
Abstract
Coffee-flux is a platform where collaborative research on coffee agroforestry is promoted: data are being shared between collaborators and positive interactions are enhanced. The philosophy is to concentrate several investigations on one specific site and for several years, to share a useful common experimental database, to develop modelling and to publish results in highly-ranked scientific journals. Applied research is also highly encouraged (e.g. C-Neutral certification, NAMA, Agronomy, etc.). Coffee-Flux benefits from infrastructure, easy access from CATIE and very good security, ready to welcome complementary scientific investigations and collaborations. The project is wide open to complementary projects, scientists and of course to students. The core data base is for sharing. The aim of Coffee-Flux is to assess carbon, nutrients, water and sediment Ecosystem Services (ES) at the scale of a coffee agroforestry watershed. Observation, experimentation, modelling and remote-sensing are combined, collecting data and calibrating models locally, then upscaling to larger regions. The project has been running continuously since 2009, in order to encompass seasonal and inter-annual fluctuations of coffee productivity and ecosystem services.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".