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Record W2170151767 · doi:10.21273/horttech.10.3.603

Chemical Characteristics of Water Used for Cranberry Production

2000· article· en· W2170151767 on OpenAlex
Eric Hanson, Carolyn J DeMoranville, Benjamin Little, D.A.J. McArthur, Jacques Painchaud, Kim Patten, Teryl R. Roper, Nicholi Vorsa, David E. Yarborough

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.

Bibliographic record

VenueHortTechnology · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsGovernment of CanadaUniversity of British Columbia
Fundersnot available
KeywordsAlkalinitySodium adsorption ratioEnvironmental scienceChemistryEnvironmental chemistryWater qualityAgronomyBiologyEcologyIrrigation

Abstract

fetched live from OpenAlex

Since up to 2.4 m (8 ft) of water may be applied annually to cranberry beds for various production purposes, water quality can alter soil chemical properties and potentially affect plant health. Many cranberry plantings have recently been developed in nontraditional production regions and on atypical sites, wherechemical properties of the available water may differ from those in cranberry sites in the traditional production regions. Water currently being used for cranberry production was sampled from farms in most major production regions to characterize its chemical characteristics. High alkalinity in many samples was a concern, since alkalinity can increase soil pH above the desired level for cranberries. Total soluble salt concentrations and sodium adsorption ratios were seldom high enough to be of concern. Water samples from long-established plantings were lower in alkalinity, pH, and soluble salt concentrations than samples from newer plantings without production histories.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
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 science0.0000.000
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 head0.021
GPT teacher head0.239
Teacher spread0.218 · 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