Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mixed-species stands are on the advance in Europe. They fulfil many functions better than monocultures. Recent papers show that mixed stands can have higher yields, but it remains open whether mixed stands simply grow faster along the same self-thinning lines as pure stands or have higher maximum stand densities. We analyzed the effect of species mixing on maximum density based on triplets of pure and mixed stands at approximately maximum density. Most considered mixtures include Norway spruce (Picea abies (L.) H. Karst.). We show that (i) in mixed stands, maximum density is, on average, 16.5% higher than in neighbouring pure stands, and (ii) species mixtures with Norway spruce exceed densities of pure stands by 8.8%, on average. For individual species mixtures, we find a significant density effect of +29.1% for Norway spruce mixed with European larch (Larix decidua Mill.) and +35.9% for Scots pine (Pinus sylvestris L.) in association with European beech (Fagus sylvatica L.). No significant links with stand variables such as age and mean tree size and site fertility were found. The results indicate that species mixing substantially increases stand density, indicating a higher carrying capacity caused by a higher supply and use efficiency of resources. The implications for inventory, silviculture, and forest modelling are discussed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 |
| 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 it