MétaCan
Menu
Back to cohort
Record W4214859808 · doi:10.1080/10871209.2022.2043492

A comparison of canid depredation research published in journal and gray literature

2022· article· en· W4214859808 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHuman Dimensions of Wildlife · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Calgary
FundersRoyal Canadian Geographical SocietyUniversity of Calgary
KeywordsGray (unit)Grey literatureDescriptive statisticsStatistical analysisGeographyPsychologyBiologyMEDLINEStatisticsMedicine

Abstract

fetched live from OpenAlex

We evaluated whether coyote and wolf depredation management research in peer-reviewed journals differed from research in gray literature (e.g., conference proceedings, research reports). Regression analysis showed that journal published research was more likely to have used statistical analyses and have authors with academic affiliations. These results show that reliance on one literature type may lead to management and research decisions based on partial information. Focusing on journal literature may reduce the likelihood of encountering descriptive (i.e., non-statistical) analyses that could inform management and illuminate future avenues of research. For instance, half of the 76 descriptive experimental research findings we located, including 10 controlled experiments, were found only in gray literature documents. Our results highlight that canid depredation managers and researchers should utilize both journal and gray literature.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.339
Teacher spread0.296 · 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