“Conservation value”: a review of the concept and its quantification
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
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 of CV used must be inferred from a paper's context. Actual and inferred meanings of CV are broadly grouped into eight categories, which are clearly possibly overlapping. Most papers (86%) provide a sufficient explanation of their CV metric(s), but only 25% of all papers actually provide the explicit definitions of CV . We use multivariate analyses firstly to detect the associations between meanings and entities, and find some strong associations. For example, when considering the CV of 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, CV applied 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 that CV is an evolving concept adapting to new conservation and management scenarios. Given the diversity of meanings and metrics present, it would be useful to recognize CV more formally as an umbrella concept. Using CV as an umbrella concept would also enable researchers to find and compare CV methodologies more easily and thus facilitate the development of new ones.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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