Restoration, reclamation, and rehabilitation: on the need for, and positing a definition of, ecological reclamation
Bibliographic record
Abstract
Within the burgeoning field of restoration ecology, defining the concept of reclamation relative to rehabilitation and ecological restoration is important to enhance comparability between studies, as well as to enable clear communication of project specific methods and goals. The Society for Ecological Restoration's international standards (SER Standards), second edition, defines reclamation as “the process of making severely degraded land fit for cultivation or a state suitable for some human use.” However, we posit that this definition, and its anthropogenic focus, does not well match how the term is often used by practitioners, and in some legal or agency documents. Further, the relationship between restoration, rehabilitation, and reclamation is unclear. We propose a more specific term and definition, ecological reclamation: “the process of assisting the recovery of severely degraded ecosystems to benefit native biota through the establishment of habitats, populations, communities, or ecosystems that are similar, but not necessarily identical to surrounding and naturally occurring ecosystems.” This definition emphasizes that the objective of a reclamation project may not be direct human use, and begins to better distinguish between ecological reclamation, rehabilitation, and ecological restoration; however, more work and discussion on these relationships is required. Distinguishing these terms will result in better comparisons between studies, improving current and future literature reviews. Further, this term will also enable practitioners to better define project goals, and enhance communication to stakeholders, practitioners, and researchers.
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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 | 0.069 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.012 | 0.128 |
| Scholarly communication | 0.026 | 0.055 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".