MétaCan
Menu
Back to cohort
Record W2509115865 · doi:10.1614/ipsm-d-16-00035.1

Assessing Benthic Barriers vs. Aggressive Cutting as Effective Yellow Flag Iris (<i>Iris pseudacorus</i>) Control Mechanisms

2016· article· en· W2509115865 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

VenueInvasive Plant Science and Management · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsThompson Rivers University
FundersGovernment of CanadaThompson Rivers University
KeywordsBenthic zoneFlag (linear algebra)IRIS (biosensor)BiologyEnvironmental scienceFisheryMathematics

Abstract

fetched live from OpenAlex

An experiment was initiated to study the effects of rubber benthic barriers vs. aggressive cutting on the invasive aquatic emergent plant, yellow flag iris. Treatments were compared against a control at two locations within British Columbia, Canada (Vaseux Lake and Dutch Lake). Yellow flag iris response was significantly different between the two sites, but biologically the results were identical: the benthic barrier killed yellow flag iris rhizomes within 70 d of treatment. Over the extent of the research, at Vaseux Lake the effect of aggressive cutting was no different from the control, while aggressive cutting was statistically no different than the benthic barrier at Dutch Lake. Vegetation regrowth approximately 200 d after the benthic barriers were removed was not detected at either location. These results indicate that rubber benthic barriers may be an effective treatment for yellow flag iris and maybe suitable for other, similar species.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.317
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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