Reconstructing Organic Carbon Accumulation and
Bibliographic record
Abstract
Over the past century, the global climate system has experienced unprecedented warming due to the rapid increase of greenhouse gas concentrations. Thus, understanding the fate of CO2 once it is released is critical to evaluating the impacts of future climate change. Lake sediments have the potential to store large quantities of CO2 in the form of organic carbon, yet the factors which control organic carbon burial efficiency in lakes are not fully understood. Consequently, this project examines the sedimentary record from one lake spanning the boreal forest-tundra ecotone in the eastern Mealy Mountains of southeastern Labrador, Canada in order to identify lake and watershed controls on organic carbon accumulation and evaluate the sensitivity of organic matter accumulation to changing climatic conditions. The pronounced environmental gradient and significant post-glacial environmental changes in this region provide an ideal setting for testing controls on organic matter accumulation. This study presents results of a multi-proxy reconstruction of one sediment record recovered from a lake located in the Mealy Mountains (53°N, 58°W). Proxies used in this study included % organics (%LOI, %C, and %N), XRF elemental analysis, magnetic susceptibility, and fossil pigment
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".