Current-use pesticides in tree swallow (Tachycineta bicolor) prey
Bibliographic record
Abstract
Analysis of biological tissues or direct sources of food may better characterize exposure of non-target organisms to current-use pesticides. Food boluses were collected from tree swallow (Tachycineta bicolor) nestlings using a non-lethal, ligature method and were composited based on nest. The ligatures, which prevented the bolus from being swallowed, were placed on nestlings for 1 h until samples were collected. Samples in 2016 were collected from nestlings at 6 days and 12 days post-hatch whereas 2018 samples were collected from nestlings at 12 days post-hatch. Furthermore, in 2016 and 2018, insects commonly consumed by tree swallows were collected via sweep net and composited based on suborder (Brachycera, Nematocera, Zygoptera, or other) following identification via dichotomous keys. All samples were collected from an agricultural region of central Saskatchewan, Canada. Samples were freeze-dried and homogenized prior to extraction. A subsample was removed for analysis (the whole sample was used for those with a total mass less than 0.1 g). Following pressurized liquid extraction with 50/50 (v/v) acetone/dichloromethane, samples were cleaned-up via pass-through solid phase extraction using Oasis PRiME HLB cartridges (3 cc, 150 mg). All samples were analyzed for 170 current-use pesticides using gas chromatography and liquid chromatography tandem mass spectrometry (GC/MS/MS and LC/MS/MS). A total of 24 current-use pesticides and metabolites were detected in bolus samples (0.3-784.3 ng/g) and 19 current-use pesticides were detected in insects (0.2-1169.6 ng/g).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".