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Record W2052507706 · doi:10.1175/2008bams2657.1

On The MSC Forecasters Forums and the Future Role of the Human Forecaster

2009· article· en· W2052507706 on OpenAlexaboutno aff
David Sills

Bibliographic record

VenueBulletin of the American Meteorological Society · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsnot available
Fundersnot available
KeywordsToolboxWeather forecastingComputer scienceRestructuringWeather predictionSituation awarenessOperations researchService (business)Plan (archaeology)Data scienceMeteorologyBusinessMarketingFinanceEngineeringGeography

Abstract

fetched live from OpenAlex

The Meteorological Service of Canada held a series of three Forecasters Forum meetings between 2003 and 2005 to seek input from the meteorological community on the best ways to implement a restructuring strategy and to develop a common vision related to the provision of weather forecasts. The meeting provided significant insight into a number of topics related to operational forecasting in Canada and have added to the larger discussion on these issues in the international meteorological community. During the course of the three forums, several themes emerged as overarching concerns. Foremost among them was the future role of the human forecaster. Most forum participants believed that human forecasters should be the "heart of weather prediction," with an increased emphasis on the analysis/diagnosis/prognosis paradigm, and recommended developing the sophisticated tools required to facilitate that role. Using results from the forums, it is suggested here that the primary role of the future forecaster should be to develop and maintain a sequence of plan-view composite depictions evolving through time to best represent the current and future states of the atmosphere. This would be accomplished using an area-based, object-oriented analysis/forecast system, with a toolbox of numerical weather prediction guidance and carefully designed artificial intelligence assistants. The forecaster's work would be focused on high-impact weather events, mainly in the short term but also in the longer term when necessary. Products would be automatically generated from the weather object database, allowing the forecast team to focus on "hands on" meteorology and maintaining shared situational awareness at all times.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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.

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

Citations27
Published2009
Admission routes1
Has abstractyes

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