Removal of the lichen mat by reindeer enhances tree growth in a northern Scots pine forest
Bibliographic record
Abstract
Reindeer ( Rangifer tarandus L.) lichen grazing enhanced Scots pine ( Pinus sylvestris L.) growth in a northeastern Fennoscandian forest. Lichen mat removal by grazing in a previously ungrazed area increased soil versus air temperature coupling. This caused faster soil spring warming and higher soil temperatures during late spring and summer, which are related to an earlier start of and better growth conditions during the trees’ growing season. Tree growth was related to spring and summer climate during the study period, 1896–2001. Snowmelt date, and ultimately soil warming and start of the growing season, may have caused the relationships between tree growth and spring climate. A drop in July temperature and an increase in the spring signals were found and attributed to the “divergence problem,” a widespread weakening in the relationships between tree growth and summer temperature in northern latitudes observed in the late 20th century. Differences in the relationships between tree growth and climate were found between trees growing in grazed and ungrazed parts of the stand. Tree growth differences were detected ∼10 years after the removal of the lichen mat. The fertilizing effect of reindeers on tree growth was considered minimal in this study, but its influence cannot be completely ruled out. Grazing intensity may thus be an important component of boreal forest carbon uptake.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".