Bibliographic record
Abstract
Abstract The presence of certain trace gases (e.g., CO2and CH4) in the atmosphere is related to human activity (fossil fuel combustion, deforestation, and intensive agriculture), and to the biogeochemical processes that occur in natural environments (tropical and boreal forests and tundra). For many years now, scientists have been trying to determine the importance of natural environments as sinks for or sources of these trace gases (1, 2, 3). The terrestrial biosphere constitutes a major carbon sink (4); more than a third of the anthropic CO2emissions are fixed there (5). However, certain changes in land use (e.g., deforestation and the draining of marshes for agriculture) can lead to changes in the way greenhouse gases (GHGs) are produced or fixed by modifying the physicochemical characteristics of these soils. In the medium and long term, these changes are likely to invert the carbon sink capacity that has been attributed until now to certain natural environments (e.g., boreal regions). This article examines how one of these land‐use changes, the creation of hydroelectric reservoirs, significantly modifies the carbon cycle in natural environments.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".