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Record W4406586048 · doi:10.22621/cfn.v138i1.3473

Annual OFNC Committee Reports for 2023

2025· article· en· W4406586048 on OpenAlexvenueno aff
Eleanor Zurbrigg, Derek Dunnett, Owen Clarkin, Gordon Robertson, Jakob Mueller, Ken Young, Edward Farnworth, Robert Lee, Kerri Keith, Jeffery M. Saarela, Janette Niwa, Ann Haley MacKenzie

Bibliographic record

VenueThe Canadian Field-Naturalist · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEarthquake and Disaster Impact Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceEnvironmental science

Abstract

fetched live from OpenAlex

The Awards Committee manages the process to annually recognize and thank those Ottawa Field Naturalists' Club (OFNC) members and other qualified persons who, by virtue of their efforts and talents, are deserving of special recognition.In late 2022, nominations were received and evaluated (see awards criteria at https://ofnc.ca/about-ofnc/awards),and recommended to the Board of Directors for approval.The awards were announced in January 2023 on the website.Biographies were written for the award recipients for inclusion in the Club's publications and posting on the website.Certificates were presented to award recipients on 1 April 2023 at the inperson annual awards appreciation event at St. Basil's Church.The recipients' names, type of award, and short rationale for recognition follow below.• Chris Traynor-George McGee Service Award, in recognition of 25+ years of dedicated service with the Birds Committee and its subcommittees.• Sharon Boddy-Conservation Award for a Member, for community leadership of habitat restoration projects at Carlington Woods and Hampton Park.• John Sankey-Conservation Award for a Nonmember, for conservation efforts to improve local greenspaces, in particular the Hunt Club Creek.

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)0.008
metaresearch head score (Gemma)0.013
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: Other · Consensus signal: Other
Teacher disagreement score0.288
Threshold uncertainty score0.577

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0030.001
Scholarly communication0.0060.002
Open science0.0030.002
Research integrity0.0080.004
Insufficient payload (model declined to judge)0.1730.116

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 head0.023
GPT teacher head0.311
Teacher spread0.288 · 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
GenreOther

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
Published2025
Admission routes1
Has abstractno

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