Place prioritization for biodiversity reserve network design: a comparison of the SITES and ResNet software packages for coverage and efficiency
Bibliographic record
Abstract
Abstract. The place prioritization problem in conservation biology is that of establishing a sequentially prioritized list of places on the basis of biodiversity content. Such a list can then be used to select reserve networks that are designed to be fully representative of the biodiversity of an area as efficiently as possible (for instance, with minimum area or cost). The usual goal is the representation of all chosen biodiversity surrogates up to or beyond a required target, or to the greatest available extent. The purpose of this paper is to compare the respective performances of two place prioritization software packages, SITES and ResNet, on four datasets (distributions of termite genera in Namibia, breeding bird species in the Falkland Islands/Islas Malvinas, vertebrate species in Texas and flora and fauna species that are at risk in Québec), to determine their respective merits. The two software packages implement radically different algorithms: SITES is based on a simulated annealing procedure for finding (local) optima; ResNet uses an algorithm based on rarity and complementarity. This analysis indicates that the rarity‐complementarity based algorithm of ResNet surpasses the simulated annealing approach of SITES with respect to time and completeness. SITES, however, contains other features that are useful in conservation planning. Ways in which the two packages can be used together effectively are suggested.
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How this classification was reachedexpand
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 | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".