Tree Swallow Nest Box Productivity Dataset from Long-Point, Ontario, Canada (1977-2014)
Bibliographic record
Abstract
The Tree Swallow (Tachycineta bicolor) is one of the most common birds in eastern North America that normally nests in tree cavities excavated by other species like woodpeckers, but also readily accepts human made nest’ boxes. Based on this quality and their abundance, Birds Canada has monitored nest boxes of tree swallows around the Long Point Biosphere Reserve, Ontario, from 1974 onwards. Each year, May through June, volunteer research assistants check nest box contents daily, and band the adults and their young. Nest-box records are available from about 300 boxes from 3-4 sites during this period. Data collected includes nest box observations, clutch initiation dates, clutch size and egg weight, nest success, weather, insect abundance, and banding data. This data set includes all data entry related to eggs, nests, nestlings, nest check observations, and banding data from 1977 to 2014. This dataset is designed to be open access and used directly with R (version R 4.0.3) and can be complimented with large scale weather data from nearby weather station. Additionally, this dataset will be updated to include insect and weather data collected at the study site. The goal organizing this dataset is to be used for studies focusing on, but not limited to: egg weight shifts over time and with respect to weather and diet, effects of wintering grounds on egg mass and nest success, nestling provisioning rates, and nest success trends with respect to rainfall. ** All relevant metadata can be found within the dataset **
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".