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Record W3173138302 · doi:10.3897/zookeys.1044.68648

Memories of Terry Erwin

2021· article· en· W3173138302 on OpenAlexaffabout
John C. H. Spence, David Kavanaugh, David R. Maddison, Olivia Boyd, Pietro Brandmayr, B. H. Garner, Sarah Maveety, Diego Mosquera, Wendy Moore, Kathryn Riley, J. H. Shorthouse, Linda Sims, Warren Steiner, Kelly Swing, Hans Turin, Laura S. Zamorano, Lyubomir Penev

Bibliographic record

VenueZooKeys · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotany and Plant Ecology Studies
Canadian institutionsLaurentian University
Fundersnot available
KeywordsTimelineSadnessFeelingSociologyPsychologyPsychoanalysisSocial psychologyHistoryAnger

Abstract

fetched live from OpenAlex

We were fortunate to have known Terry not only as an excellent professional coleopterist and an enthusiastic colleague, but also as a good friend. Entomological meetings for us came with an evening supper or two with Terry and the kind of laid-back personal catch-up that happens only among friends with long-term interest in each other’s lives. Through our connections with the University of Alberta and George Ball we were also happy members of Terry’s basal academic family. While we will join the rest of a broader scientific community in missing his presence in development of ideas about beetles, biodiversity and evolution, the kinds of work that Terry promoted will continue. We will, of course, be interested in following how the understanding of carabids and nature develops further from Terry’s contributions. This will most certainly continue to grow, partly through the efforts of those that he has influenced. Every practicing research scientist has some role to play in the great chain of discovery, and much of this volume is meant to celebrate Terry’s contributions and showcase how they have influenced the work of others. Our own more enduring sense of loss will flow from the personal interactions with Terry that were generally part of our timelines. Despite the sadness associated with such loss, our memories of interactions with Terry underscore a sense of joy and gratefulness for having connected with him interpersonally in life. Given Terry’s affable and social nature, many others will have such memories. Thus, when Lyubomir Penev asked us to coordinate a selection of ‘memories’ for this memorial volume, we were happy to undertake the task and gather together a selection of memories of our friend, Terry Erwin. What follows is a series of recollections by people who knew and worked with him from a number of perspectives during a broad range of his academic career. We are most grateful to those who have been willing to share their reflections. These are presented here as a way of reaching beyond Terry’s considerable scientific influence to also preserve some sense of his influence on the lives of people, and the ways in which he encouraged and inspired them. We thank all the contributors for their efforts and Diane Hollingdale for work to bring the included photographs to the best possible publication standard. John R. Spence Edmonton, Alberta David H. Kavanaugh San Francisco, California David R. Maddison Corvallis, Oregon

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)0.001
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0030.010
Insufficient payload (model declined to judge)0.0150.006

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 head0.011
GPT teacher head0.172
Teacher spread0.161 · 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
GenreCommentary

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".

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Citations1
Published2021
Admission routes2
Has abstractyes

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