Corn field management for wintering waterfowl on eastern Long Island, New York
Bibliographic record
Abstract
The study took place in corn fields in Suffolk County, Long Island, New York, 7 February – 4 April 2018 and 7 February – 10 April 2019 (Fig.1). Fields were planted for typical production corn with 15.2 cm (6 inch) spacing among plants in rows 30.5 cm (12 inch) apart. Suffolk County contains coastal wetlands, freshwater ponds, and rural landscapes where corn fields are available to wintering waterfowl. Seasonal corn yield and wildlife abundance were determined at two corn fields in 2018 and 2019 (Cutchogue [41.023 ° N, -72.511° W] and Orient Point [41.141° N, -72.278° W]) and included another corn field in 2019 (Brookhaven [40.798° N, -72.891° W]). Corn fields were divided into three sections and marked them with flagging to identify them from a distance. The mean (± SE) corn field size was 4.08 ± 0.20 ha (Cutchogue = 3.99 ha, [0.87 ha, 1.33 ha, and 1.79 ha sections]; Orient = 4.47 ha, [1.46 ha, 1.46 ha, and 1.55 ha sections]; Brookhaven = 3.78 ha, [1.26 ha, 1.26 ha, and 1.26 ha sections]). Corn field samples were taken to obtain an index of corn availability and corn depletion rates following Barney [8]. One section in each field was chopped with a brush-hog every 2 weeks until all three sections in a field were chopped. Section of standing corn were sampled once the day before chopping and after chopping once every two weeks in 2018 and weekly in 2019. Sampling was adjusted to weekly in 2019 because some sections were depleted to zero or near zero kg/ha in < 2 weeks during 2018. A random sampling design was used to distribute samples throughout the field. Main transects (n = 3) were established perpendicular to the field edge in each section of a field (evenly spaced 20 − 26 m apart). Each sampling period, a random number generator was used to select sampling points along each main transect. The same number of samples were taken along each main transect (n = 4; n = 12 per section). A random number generator was used to determine the left or right direction of samples to be taken off of the main transect along a perpendicular transect. A random number generator was used to determine the distance of the sampling point along the perpendicular transect (between 1 – 10 m). Corn was sampled using a 1 m × 1 m quadrat at each sampling point and all corn within each quadrat was collected and placed in marked plastic bags. All individual kernels, cobs full of kernels, and cobs partial covered in kernels were included in the sample and frozen within 4 hrs of sampling. In the lab, corn was thawed, kernels were removed from cobs, and samples were dried at 60℃ until a constant mass at 48 hrs and weighed to ± 0.1 g, and reported as kg/ha. Wildlife surveys were conducted at each field 8 February – 3 April 2018 and 8 February – 9 April 2019. Morning and evening surveys were conducted, switching the time of survey at each field weekly. Morning surveys occurred 30 min before to 2 h after sunrise and evening surveys were 2 h before to 30 mins after sunset. To survey two fields on the same day, one field was surveyed in the morning and another field in the evening following weekly protocol for switching survey times. Each field was surveyed 3 times per week. Observation points were adjusted accordingly to maximize clear line of site when each section was chopped. Waterfowl flew into and landed in fields during sunrise and sunset surveys. Canada geese that were in fields at the start counts were included. This scenario reduced error in counting and identifying waterfowl to species, so 100% detection was assumed. For each field, total number of waterfowl, species composition, and other wildlife were recorded. Other wildlife included blackbirds (Icteridae), white-tailed deer (Odocoileus virginianus), and wild turkeys (Meleagris gallopavo).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".