“Conservation value”: a review of the concept and its quantification
Bibliographic record
Abstract
Abstract We examine the concept of “conservation value” (CV), its use in conservation biology and environmental management, and its quantification in the literature (1976–2015). We find that the concept has been applied to several different entities (e.g., species, communities, natural habitats, or human‐made ecosystems, such as agroecosystems), has many different meanings, and is measured using a variety of metrics (e.g., species richness, abundance/density, habitat use, or rarity/uniqueness). In most cases, the meanings ofCVused must be inferred from a paper's context. Actual and inferred meanings ofCVare broadly grouped into eight categories, which are clearly possibly overlapping. Most papers (86%) provide a sufficient explanation of theirCVmetric(s), but only 25% of all papers actually provide the explicit definitions ofCV. We use multivariate analyses firstly to detect the associations between meanings and entities, and find some strong associations. For example, when considering theCVof communities/regions, the meaning used tends to be either (1) a tool to prioritize the conservation efforts or (2) an indicator of endangerment. When considering, however, other entities, such as agroecosystems, the associated meanings are (1) the provision of habitat and food supply to wildlife or (2) the capacity of these entities to complement other kinds of conservation. We use multivariate methods also to examine the associations between metrics and meanings. The well‐known metrics of species richness, diversity, and abundance are associated with (1) the provision of habitat and food supply to wildlife or (2) the capacity of agroecosystems to complement other kinds of conservation. In general,CVapplied to natural systems is a far more nebulous concept (both in meanings and in metrics) than when it is applied to anthropic or human‐made ecosystems. We conclude thatCVis an evolving concept adapting to new conservation and management scenarios. Given the diversity of meanings and metrics present, it would be useful to recognizeCVmore formally as an umbrella concept. UsingCVas an umbrella concept would also enable researchers to find and compareCVmethodologies more easily and thus facilitate the development of new ones.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.033 | 0.038 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".