Peer Review #1 of "Origin identification of migratory pests (European Starling) using geochemical fingerprinting (v0.1)"
Bibliographic record
Abstract
The European Starling (Sturnidae: Sturnus vulgaris L.) is an invasive bird in North America where it is an agricultural pest.In British Columbia (Canada), the starling population increases in orchards and vineyards in autumn, where they consume and damage ripening fruits.Starlings also cause damage in dairy farms and feedlots by consuming and contaminating food and spreading diseases.Damage can be partly mitigated by the use of scare devices, which can disperse flocks until they become habituated.Large-scale trapping and euthanizing before starlings move to fields and farms could be a practical means of preventing damage, but requires knowledge of natal origin.Within a small (20,831 km 2 ), agriculturally significant portion of south-central British Columbia, the Okanagan-Similkameen region, we used 21 trace elements in bone tissue to discriminate the spatial distribution of juvenile starlings and to reveal the geographic origin of the problem birds in fall.Stepwise discriminant analysis of trace elements classified juveniles to their natal origin (minimum discrimination distance of 12km) with 79% accuracy.In vineyards and orchards, the majority (55%) of problem birds derive from northern portions of the valley; and the remaining 45% of problem birds were a mixture of local and immigrant/unassigned birds.In contrast, problem birds in dairy farms and feedlots were largely immigrants/unassigned (89%) and 11% were local from northern region of the valley.Moreover, elemental signatures can separate starling populations in the Valley yielding a promising tool for identifying the geographic origin of these migratory birds.
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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 | 0.013 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.373 | 0.267 |
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".