Data for: "Additional data confirms the impact of the COVID19 lockdown on the behavior and fattening of migratory snow geese"
Bibliographic record
Abstract
Please find all metadata and dataset information in the README.md file. Article abstract: The COVID19 lockdown provided a unique opportunity to study the impact of human activities and conservation measures on wildlife. However, most lockdown studies were opportunistic and based on limited data, because this ‘natural experiment’ was unexpected and short-lasting. Replication of scientific results is the cornerstone of the scientific method and ensures that conclusions from such short-term studies are robust. Here, we test predictions arising from a previous study where we showed the impact of the lockdown-induced reduction in hunting disturbance on the body condition and behavior of greater snow geese (Anser caerulescens a.), a species whose management is crucial for the conservation of northern ecosystems. The analysis of two additional years of data confirmed our predictions. The return to a high hunting pressure in springs 2021‐–2022 (post-lockdown) reduced overall goose body condition compared to the lockdown year. Goose fattening in post-lockdown springs was very similar to pre-lockdown years, differing from 2020 when a high body condition was reached earlier in spring than in any other year. Radio-tracked birds spent more time in profitable but risky agricultural lands in 2021 compared to 2020, as was the case in the pre-lockdown year. Our study provides robust evidence confirming the impacts of spring hunting on greater snow goose physiology. It demonstrates the long-lasting efficiency of the spring conservation hunt established two decades ago to limit the size of the population with the aim of preserving Arctic ecosystems from overgrazing and associated negative impacts on other arctic-nesting birds.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.431 | 0.138 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".