Bibliographic record
Abstract
Diverse perspectives in the theory and practice of restoration ecology create a productive space to continuously improve outcomes for people and biodiversity. The practical side of restoration ecology often focuses on the recovery of ecosystem structure—the habitat and organisms that create ecological communities. This structural approach has led to many successes, but falls short when it comes to accommodating both complex ecological interactions and a sustainable role for people as reciprocal agents in restoration practice. Process‐based restoration offers a complementary approach to this structural perspective. Just as structural restoration “balances a ledger” of facilitated and suppressed species based on their perceived value and role in meeting restoration goals, process‐based restoration focuses on suppressing weedy interactions and enhancing desired interactions. At least four features characterize process‐based restoration—including emphasis on the intrinsic and utilitarian values of: (1) the regenerating processes of natural disturbance; (2) functional, indirect, and trait‐mediated interactions; (3) selective connectivity to titrate the amount and types of ecological flows desired for recovery goals; and (4) an inclusive human connection with nature. These features work in concert, such as renewing the role of forest burning by Indigenous people to facilitate growth of forage plants that bolster populations of harvestable animals. With restoration becoming an increasingly vital and internationally recognized field in the coming century, a more effective and inclusive approach will be needed to conserve biodiversity and the cultures that depend on it.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".