Priporočila za zmanjševanje ogljičnega odtisa organizacije turističnih doživetij: Kulturna doživetja
Bibliographic record
Abstract
Kulturna doživetja zajemajo tako obiske mest, oglede stavb, arhitekture, gradov, sakralnih objektov, spomenikov, muzejev in galerij … kakor tudi različnih prireditev in tradicionalnih dogodkov. Tako stavbe kot zunaj izvedene prireditve so močno izpostavljene zunanjim vplivom ter s tem tudi grožnjam, ki jih povzročajo podnebne spremembe. Prav tako pa so lahko tudi same dejavniki, ki prispevajo k poslabšanju podnebnih razmer. Zato je priporočljivo, da ponudniki kulturnih vsebin najprej spremljajo lastne izpuste emisij CO2 in nato ukrepajo k zmanjšanju le-teh. Obstaja nekaj kalkulatorjev CO2, ki vključujejo tudi ocene za kulturni turizem. V poglavju so predstavljeni The Green Events Tool - GET, The Creative Green Tools Canada, Scope 3 Evaluator in The Climate Toolkit. Ko je vpliv kulturnih doživetij na podnebje poznan, pa se lahko tako posameznik, kot ponudniki, destinacijski upravljalci in država lotijo ukrepov, ki prispevajo k blaženju podnebnih sprememb. V tem poglavju so predstavljeni ukrepi, ki se navezujejo na različna področja, s katerimi se prepletajo kulturna doživetja: promet, prehrana, odpadki, prenova stavb, poraba virov.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".