Forest tree, woody debris, and soil inventory data from long-term research plots for LTREB at the University of Michigan Biological Station
Bibliographic record
Abstract
Disturbances to forests, such as logging or wildfires, typically lead to large losses of carbon and nutrients from both the plants and soils of the ecosystem. Virtually all forests are in some state of recovery from such disturbances, whether caused naturally or by humans. Knowledge of the time required for a forest to recover its original amounts of carbon and nutrients after a disturbance is not complete, nor is an understanding of how regrowing plants, recovering soils and the year to year variation in climate interact to control recovery as a forest ages. This project takes advantage of long existing research plots in forests at the University of Michigan Biological Station to figure out how changes in forest structure, carbon and nitrogen contents of the forests, and variations in climate act together through time to influence how fast trees grow, nitrogen is retained, and carbon is captured and stored in forests. Scientists and students will make regular measurements of the types of trees, their stem sizes and mass, their patterns of leaf arrangement, the amounts of carbon and nitrogen in soils, and other factors in five forest that were cut and burned in 1936, 1948, 1954, 1980, and 1998 and so today range from 20 years to 120 years old. Several nearby much older forests will also be sampled. This will let the project link disturbances, climate and ecology for forests that are broadly representative of those across the northern United States, Canada, 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.029 |
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".