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Record W4392600444 · doi:10.5194/egusphere-egu24-6070

Reconciling climate change mitigation, biodiversity conservation and rice production through changes in water management strategies

2024· preprint· en· W4392600444 on OpenAlexaff
Sebastián Echeverría-Progulakis, Maite Martínez‐Eixarch, Néstor Pérez‐Méndez

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAgriculture, Water, and Health
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsClimate changeProduction (economics)BiodiversityBiodiversity conservationNatural resource economicsEnvironmental resource managementWater conservationEnvironmental scienceEnvironmental planningAgroforestryBusinessEconomicsEcologyWater resources

Abstract

fetched live from OpenAlex

Tackling climate change while enhancing biodiversity without compromising production is a main goal in agricultural policy. In rice farming, water-saving irrigation techniques alternative to permanent flooding are necessary to face water scarcity and have proven effective in reducing greenhouse gas (GHG) emissions, yet potential trade-offs with biodiversity conservation are often overlooked. Here we used a field-scale experiment to compare the effects of water management strategies representing a water use gradient on i) GHG emissions, ii) the diversity of aquatic macroinvertebrate and vertebrate (fish and amphibians) communities, and iii) crop productivity. Reduced methane emissions were observed in rice fields with lowest water use when compared to fields permanently flooded, yet the effect on aquatic biodiversity and crop yield was the opposite. Through this holistic assessment approach, we were able to identify an intermediate rice water-saving irrigation strategy that conciliates climate change mitigation, biodiversity conservation and crop production in rice agrosystems.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.592

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.251
Teacher spread0.209 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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