Evaluating the representation of ecological features in a protected area network: A Gap Analysis study of the Yukon
Bibliographic record
Abstract
A gap analysis study was conducted to assess the effectiveness of the protected area network in the Yukon Territory, based on the degree of representation of key ecological features. Protected area (PA) networks that are representative of the ecology over a large region are considered desirable for conservation; however, representative PA networks are challenging to achieve because ecological gaps often exist in the PA network. Ecological gaps signify conservation shortfalls, which weaken the overall success of the PA network. To determine if gaps were present in the Yukon’s PA network, five geographic information systems datasets were obtained from a variety of sources, in order to be processed in the gap analysis study of the Yukon. A comparison index method was employed to evaluate the representation of ecological features from three of the datasets: ecoregions, landcover and wildlife key areas. In addition, two human disturbance datasets, roads and communities, were used to assess the human footprint across the landscape. The results illustrated that the human footprint in the Yukon was generally minor; however, the region surrounding the city of Whitehorse was found to have the highest level of human disturbance. Areas which were disturbed by humans were considered unfavourable for conservation purposes, while regions relatively devoid of human impacts were considerably more desirable. Regions which displayed low levels of representation of ecological features were also ecologically desirable because these areas represented gaps in the Yukon’s PA network. Of the 57 ecological features used in this gap analysis study, 51% were not well represented by the current PA network, which highlights that there were substantial ecological gaps. The most prominent gaps were located in the southeast, central-west and northeast portions of the Yukon. Less critical gaps were prevalent throughout central Yukon. The findings also identified which ecological features were inadequately represented.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".