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Record W3122448941 · doi:10.1111/csp2.354

Scheduling incremental actions to build a comprehensive national protected area network for Papua New Guinea

2021· article· en· W3122448941 on OpenAlexaff
Vanessa M. Adams, Nadya Dimitrova, Hugh P. Possingham, James R. Allan, Caitlin D. Kuempel, Nate Peterson, Alu Kaiye, Malcolm Keako, Vivitskaia Tulloch

Bibliographic record

VenueConservation Science and Practice · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of British Columbia
FundersUnited Nations Development ProgrammeUnited Nations
KeywordsComputer scienceScheduling (production processes)Environmental resource managementAction planDeclarationEnvironmental planningOperations researchBusinessGeographyOperations managementEcologyEngineeringEconomics

Abstract

fetched live from OpenAlex

Abstract Systematic conservation planning identifies priority areas to cost‐effectively meet conservation targets. Yet, these tools rarely guide wholesale declaration of reserve systems in a single time step due to financial and implementation constraints. Rather, incremental scheduling of actions to progressively build reserve networks is required. To ensure this incremental action is guided by the original plan, and thus builds a reserve network that meets all conservation targets, strategic scheduling, and iterative planning is needed. We explore the issue of scheduling conservation actions using the national scale conservation plan for Papua New Guinea (PNG), commissioned by the PNG Conservation and Environment Protection Authority that identifies a comprehensive set of priority areas that meet conservation targets in both the land and sea. As part of the planning process a subset of areas were identified in collaboration as priorities for immediate action—termed areas of interest (AOIs). However, the extent to which targets are met if action stopped after implementing the AOIs is unknown. We test three possible implementation scenarios based on these priority areas to measure target achievement and shortfalls. We then consider how iterative planning would interact with scheduling actions to identify new long‐term priorities that will meet missing targets. Our results show that while a large number of conservation targets are met within the AOIs there are shortfalls for protecting threatened and range restricted endemic species. Meeting targets for these would require an updated set of national priorities and an additional 13% of land area compared with if all areas identified in the original assessment were protected in a single time step. This provides important insights into the benefits of strategic scheduling of implementation, as well as the need for capacity to monitor action and update priorities as implementation proceeds.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.117
GPT teacher head0.342
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2021
Admission routes1
Has abstractyes

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