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Record W2587261310 · doi:10.1111/acv.12336

Grain spilled from moving trains create a substantial wildlife attractant in protected areas

2017· article· en· W2587261310 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAnimal Conservation · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsUniversity of Alberta
FundersParks Canada
KeywordsWildlifeForagingGrizzly BearsUrsusGeographyHabitatEnvironmental scienceDeposition (geology)Threatened speciesPopulationEcologyEnvironmental protectionBiologyDemography

Abstract

fetched live from OpenAlex

Abstract Transportation corridors can attract threatened wildlife via habitat enhancement and foraging opportunities, leading to collisions with vehicles. But wildlife may also be attracted to energy‐dense food products that are spilled or discarded from moving vehicles, which is rarely studied. Therefore, we quantified train‐spilled attractants in Banff and Yoho National Parks, Canada, where agricultural products (hereafter, grain) are transported along 134 km of railway and may contribute to wildlife mortality. We measured grain deposition from 2012 to 2015 at 19 sites and assessed the performance of three structures developed to measure spilled grain. We then modeled grain deposition with respect to four types of spatial and temporal variables: those related to grain shipment, physical habitat characteristic, train‐related characteristics and variables specific to the study site. Grain was spilled at a mean rate of 1.64 g m −2 day −1 ( sd = 3.60) from April to October ( n = 3 years) and 1.52 ( sd = 2.37) from November to March ( n = 1 year). Extrapolating annual deposition across the study area yielded enough grain (110 tons) to provide 4.77 × 10 8 kcal of gross energy, which is equivalent to the average annual caloric needs of 42–54 grizzly bears Ursus arctos horribilis ; the regional population is estimated at 50–73 animals. Much of this energy will not be accessible or available to bears; however, their attraction to it could contribute to rising and unsustainable rates of mortality. Models explained 9–31% of the variance in deposition for each grain type, primarily via coarse temporal variables of shipping rates and month. The absence of more specific predictive variables suggests that mitigation should target broader policies, such as prompt reporting and repair of leaky hopper cars, and limits to train stoppage in protected areas. We encourage more global assessment of the under‐studied issue of food attractants spilled by vehicles along transportation corridors.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.029
GPT teacher head0.260
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it