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Record W4389226166 · doi:10.1111/cobi.14219

Honoring the legacy of a conservation champion: Bob Pressey (1953–2023)

2023· editorial· en· W4389226166 on OpenAlexaff
Jorge G. Álvarez‐Romero, Vanessa M. Adams, Natalie C. Ban, Morena Mills, Piero Visconti

Bibliographic record

VenueConservation Biology · 2023
Typeeditorial
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsChampionEnvironmental ethicsGeographyArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

On 5 July 2023, we lost a champion for conservation—Professor Robert (Bob) Pressey. His passion was a simple one—make a difference for biodiversity conservation. He fought fiercely for evidence- and outcomes-based conservation. Bob discovered (some might say invented) conservation planning in the late 1980s and quickly became one of the premier scientists in this field (Pressey, 2002). In the 1990s and early 2000s, he defined, applied, and continuously improved the framework of systematic conservation planning, providing the foundations and scientific methods used today to identify conservation priorities (Margules & Pressey, 2000; Pressey, 1994a; Pressey & Bottrill, 2008; Pressey & Cowling, 2001; Pressey et al., 1996). Through this work, he made significant contributions to establishing representative protected area networks in Australia (Ferrier et al., 2000; Pressey, 1994a), South Africa (Cowling & Pressey, 2003; Pressey et al., 2003), South Pacific (Mills et al., 2011), and beyond. His significant contributions to conservation science and practice are shown through his staunch support of and generous donations of time to the Society for Conservation Biology and other nongovernmental organizations and societies. In 2001, he received the Society for Conservation Biology's Edward T. LaRoe III Award for translating principles of conservation biology into real-world conservation. Further recognition of his contributions to conservation includes the Eureka Prize for Biodiversity Research in 2002, the New South Wales Premier's Award in 2004, the Australian Ecology Research Award in 2008, service as a governor of the World Wildlife Fund (WWF) in 2006, and election to the Australian Academy of Science in 2010 and to the Royal Society in 2022. In his academic career, when he felt his work in designing and implementing protected areas had not achieved the level of protection of biodiversity he sought, he shifted focus. He demonstrated that reserves are biased toward sites less threatened by extractive and commercial activities, which limits their effectiveness. He referred to this bias as "residual reservation." In the last decade of his career, his attention was drawn to understanding the causes and consequences of residual reservation (Pressey et al., 2021). By understanding these and proposing solutions, he hoped conservation scientists and practitioners would be better equipped to argue for more effective protection in the future. In my youthful naivety and arrogance, I believed that scientific insights would quickly change the way conservation was done and make it more effective. I have since discovered that the path to effectiveness is longer, more winding, and bumpier than initially perceived, with many confusing detours. I get up most mornings willing to give it another go. La lutte continue. For those of us who knew and worked with Bob, he was a friend and mentor, a respected and inspiring colleague, a natural storyteller (Das, 2012; Pressey, 1994b), and the ultimate source for bad jokes and 1970s surf videos. Bob's never-ending love of the environment, dedication to his work, pursuit of irreproachable and robust science, and grit permeate his published works and continue to inspire us. Vale Bob, la lutte will continue.

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)0.003
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0080.005
Open science0.0010.006
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0560.033

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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2023
Admission routes1
Has abstractyes

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