Bibliographic record
Abstract
An experimental restoration in Wisconsin planted in 1986 tested the hypothesis that growing-season fire maintained richness of native herbaceous dicots (forbs). Replicated plantings were burned in May or July, or left unburned, every third year from 1989 to 2004 and monitored for differences in cover and richness through 2006. Native forb richness was higher in burned than unburned plots, with greatest richness following July burns. Two seasons after the 2004 fires, counts averaged 2 more native forb species in replicates burned in July than those burned in May and 4 more species in replicates burned in July than those left unburned. The strongest statistical response to fire season was higher richness of early-flowering species in replicates burned in July, largely attributable to early-flowering forbs planted in 1986 that persisted better after July burns than in other treatments. Spring fire increased cover of late-flowering C4 grasses. As of 2006, C4 grasses accounted for 76% cover after May fires, 52% after July fires, and 39% in unburned plots. Replicates burned in July held more alien species for the first 12 y, after which alien richness declined and differences among treatments disappeared. Summer fire best maintained richness of native, especially early-flowering, species.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".