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Record W4394338048 · doi:10.6084/m9.figshare.14724732

Data and code from: Support for an area–heterogeneity tradeoff for biodiversity in croplands

2023· dataset· en· W4394338048 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldEnvironmental Science
TopicSustainable Agricultural Systems Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityCode (set theory)GeographyComputer scienceEcologyBiologyProgramming language

Abstract

fetched live from OpenAlex

Tab-delimited, plain-text data files and R scripts. Please see "Metadata.txt" for details. <br> Abstract from associated paper: Rapid expansion of the human population poses a challenge for wildlife conservation in agricultural landscapes. One proposition is that we could increase biodiversity in such landscapes by increasing crop diversity. However, studies report both positive and negative effects of crop diversity on biodiversity. One possible explanation, derived from the “area–heterogeneity tradeoff hypothesis,” is that the effect of crop diversity on biodiversity depends on a tradeoff between increasing the number of crop types in a landscape and decreasing the amount of each single crop type. This should cause positive effects of increasing crop diversity at low to intermediate crop diversity and negative effects at intermediate to high crop diversity. We also proposed two factors that could change the point at which the effect of increasing crop diversity shifts from positive to negative. First, we predicted that this shift occurs at a lower crop diversity when the surrounding landscape contains less semi-natural habitat, and at a higher crop diversity when the landscape contains more semi-natural habitat. This should increase the likelihood of detecting negative effects of crop diversity when semi-natural cover is low and positive effects when it is high. Second, we predicted that the shift from a positive to negative effect occurs at a lower crop diversity when it is measured locally than when it is measured at greater distances from the site, making detection of negative crop diversity effects more likely when measurements are at local extents. We tested these predictions using data on the biodiversity of herbaceous plants, butterflies, syrphid flies, woody plants, bees, carabid beetles, spiders, and birds at 221 crop field edges in Eastern Ontario, Canada. We found support for an area–crop diversity tradeoff. Semi-natural cover and measurement extent influenced the biodiversity–crop diversity relationship, with positive effects when semi-natural cover was high, and negative effects when semi-natural cover was low and when crop diversity was measured at local extents. The results suggest that policies/guidelines designed to increase crop diversity will not benefit biodiversity in the landscapes where conservation action is most urgently needed, i.e. in landscapes with high agricultural use and low semi-natural cover.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.067
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 communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.001

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 head0.114
GPT teacher head0.296
Teacher spread0.182 · 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