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Record W3006138665 · doi:10.1002/ecs2.3036

Equitable transform applied to phenology and temperature in a changing climate: Scaling to maintain individuality

2020· article· en· W3006138665 on OpenAlexafffund
Cassandra Elphinstone, Greg H. R. Henry

Bibliographic record

VenueEcosphere · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaArcticNetPolar Knowledge Canada
KeywordsMissing dataScalingSet (abstract data type)Sequence (biology)Data setPrincipal component analysisTransformation (genetics)Function (biology)Computer scienceMultidimensional scalingData miningAlgorithmMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract We describe an equitable transform that can be used to estimate missing data points, determine systematic patterns in data, observe baseline changes, and detect different amplitudes in replicated sequences. It is applicable to short discrete two‐ or three‐dimensional data sets such as biological life cycles or water content in similar media. The technique is independent of the continuity or ordering of sequences but is distinct from methods such as principal component analysis. It is ideally used to preprocess noisy or incomplete data sets prior to analysis with other well‐established techniques. This transformation maintains systematic differences between individual sequences when the underlying pattern is a separable function in two variables added to another function in one of these variables. The equitable transform partitions the original noisy data into the underlying signal determined from the data and its residuals. Points from one sequence can be transformed to any other sequence without losing any information. Information about one or more sequences can be used to infer others with missing data. A link to a github R package is provided so the transform can easily be run on any two‐dimensional data set. Simulated two‐dimensional data sets are used to demonstrate its utility in recovering missing data and scaling/offsetting in one of the dimensions. We used the transform to determine that winter temperatures at a High Arctic site have warmed by 1.8° ± 0.4°C/decade and summer temperatures by 1.1° ± 0.2°C/decade from 1986 to 2007. Applied to 18 yr of phenology data for the tundra plant Dryas integrifolia at the same site, we determined that the annual cycles of phenology events could be modeled accurately. Phenology, in some circumstances, can be described as offsetting and scaling the rate at which life cycle events occur. We introduce the idea of scaling and shifting the seasonal cycle of a reference plant via the equitable transform in order to approximate the behavior of multiple phenological cycles. Relative phenology rates of Dryas integrifolia were found to have increased over time indicating duration of phenological stages have become shorter in recent years, likely in response to the warming climate.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2020
Admission routes2
Has abstractyes

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